László Gulyás, Tamás Kozsik and John B. Corliss
Journal of Artificial Societies and Social Simulation 2 (3) 8

Kyeywords: Social Science Simulation, Agent-Based Modelling, Integrated Modelling Environment
Abstract: While computer models provide many advantages over traditional experimental methods, they also raise several problems. The process of software development is a complicated task with high potential for errors, especially when it is carried out by scientists holding their expertise in other fields than computer science. On the other hand, the process of creating computer simulations of social systems which reflect the reality of such systems requires insights considerably beyond expertise in computer science. The Multi-Agent Modelling Language (MAML) is one of the efforts to ease these difficulties. In its current version, MAML is a macro-language for Swarm (a freely distributed toolset under development at SFI), but it is also part of a larger Swarm-independent framework. Also, the design of MAML, while influenced by concepts from Swarm, is general enough to allow for later extension of the supported simulation kernels. This paper gives an overview of the mentioned larger framework, with special emphasis on MAML and its graphical CASE tool, the Model Design Interface.

Ian Lustick
Journal of Artificial Societies and Social Simulation 3 (1) 1

Kyeywords: Agent-Based Modeling, Identity, Constructivism
Abstract: Agent-based modeling is an alternative and complementary approach to the study of political identities, including ethnicity and nationalism. By generating many runs with different initial conditions large data sets of virtual histories can be accumulated. This paper presents the ABIR (Agent-Based Identity Repertoire) model which seeks to refine, elaborate, and test constructivist theories of identity and identity change. In this model agents with activated identities interact on a landscape. These agents have repertoires of latent identities. A simple set of micro rules, conforming to constructivist theory's standard propositions about the fluidity, multiplicity, and institutionalizability of identities, as well as their responsiveness to changing incentive structures, determines in any particular interaction what identities will be activated, deactivated, or maintained. Macro-patterns that emerge from these myriad micro-interactions can then be systematically studied. Experiments reported in this paper focus how variation in the size of agent repertoires can affect tension reduction and aggregation across the landscape. Results suggest that tipping and cascade effects are much more likely when a small number of exclusivist identities are present in a population.

Jim Doran
Journal of Artificial Societies and Social Simulation 4 (2) 4

Kyeywords: Software Agent, Agent-Based Modelling, Integrated Watershed Management, Sustainability, Fraser River, Intervention Strategy
Abstract: We propose an advanced agent-based modelling approach to ecosystem management, informed and motivated by consideration of the Fraser River watershed and its management problems. Agent-based modelling is introduced, and a three-stage computer-based research programme is formulated, the focus of which is on how best to intervene to cause stakeholders to co-operate effectively in ecosystem management, and on the objective discovery and comparison of intervention strategies by way of computer experimentation. The agent-based model outlined is technically relatively complex, and several potential difficulties in its detailed development are discussed. Types of ecosystem intervention strategy that might plausibly be discovered or recommended by the model are projected and compared with those currently advocated in the literature.

Armano Srbljinovic, Drazen Penzar, Petra Rodik and Kruno Kardov
Journal of Artificial Societies and Social Simulation 6 (1) 1

Kyeywords: Agent-Based Modelling; Ethnic Identity; Ethnic Mobilisation
Abstract: In this paper we used the methodology of agent-based modelling to help explaining why populations with very similar socio-demographic characteristics sometimes exhibit great differences in ethnic mobilisation levels during mobilisation processes. This agent-based model of ethnic mobilisation was inspired and developed by combining and extending several theories, ideas and modelling constructs that were already used in agent-based modelling of social processes. The model has been specifically adapted to account for some of the most important characteristics of ethnic mobilisation processes that took place in the former Yugoslavia. Results obtained by experimenting with the model indicate that the observed differences in mobilisation levels across populations may sometimes not be related to the variations within any particular socio-demographic factor, but merely to random differences in the initial states of the individuals. In this model these random differences primarily relate to the degrees of importance that individuals attach to their ethnic identity, as well as to the layout of social networks.

Michel Etienne, Christophe Le Page and Mathilde Cohen
Journal of Artificial Societies and Social Simulation 6 (2) 2

Kyeywords: Natural resource management; Management scenarios; Agent-Based Model, Viewpoints
Abstract: A multi-agent system was developed to simulate strategies of natural resource management in the Causse Méjan, a limestone plateau dominated by a rare grassland-dominated ecosystem endangered by pine invasion. To stimulate the emergence of alternative long-term management strategies for the sheep farms and the woodlands, contrasting dynamic viewpoints on land resources were designed at different space scales. To begin with, they were individually used to validate the model with each type of main stakeholders (foresters, farmers and the National Park of Cévennes rangers), to improve it and to propose individual scenarios of natural resource management. Once the model improved, the set of viewpoints made it possible to assess the impact of the individual scenarios on the main productive (sheep stocking rate, timber growth) and environmental (endangered species, landscape value) stakes on any spatial entity considered as relevant by any stakeholder. As the different opinions were collectively viewed and confronted, the need to agree to a compromise was highlighted and led to new scenarios based on more collective management of the pine woodlands. The results of these alternative scenarios were collectively evaluated anew and it was then possible to select a set of feasible scenarios stemming from current actors? perceptions and practices and to suggest alternative sylvopastoral management based on innovative practices. The paper underlines the usefulness of the representation of viewpoints in that it allowed for scenario description and impact assessment of the compared management strategies. It also shows how the step-by-step approach contributed to improve decision-making by National Park managers.

Ravi Bhavnani
Journal of Artificial Societies and Social Simulation 6 (4) 1

Kyeywords: Social Capital, Italy, Agent-Based Model
Abstract: Long duration historical studies have been formative in shaping comparative analysis. Yet historical processes are notoriously difficult to study, and their findings equally difficult to validate empirically. In this paper, I take Robert Putnam’s work on Civic Traditions in Modern Italy and attempt to bridge the gap between the study’s historical starting point and contemporary observations, using an agent-based model of social interaction. My use of a computational model to study historical processes—in this case the inculcation and spread of social capital—supports Putnam's claim of path dependence. Moving beyond Putnam’s study, my results indicate that the formation of civic (or uncivic) communities is not deterministic, that their emergence is sensitive to historical shocks, and that the absence of political boundaries lowers aggregate levels of civicness in regions characterized by effective institutions. In addition, the simulation suggests that minor improvement to ineffective institutions—making them moderately effective—constitute a mid-level equilibrium trap with the least desirable social consequences.

Benjamin M. Eidelson and Ian Lustick
Journal of Artificial Societies and Social Simulation 7 (3) 6

Kyeywords: Smallpox, Bioterrorism, Agent-Based Modeling, Stochastic Simulation, Vaccination Policy
Abstract: Because conjectural 'thought experiments' can be formalized, refined, and conducted systematically using computers, computational modeling is called for in situations that demand robust quantitative study of phenomena which occur only rarely, or may never occur at all. In light of mounting concerns regarding the threats of bioterrorism in general and smallpox in specific, we developed a stochastic agent-based model, VIR-POX, in order to explore the viability of available containment measures as defenses against the spread of this infectious disease. We found the various vaccination and containment programs to be highly interdependent, and ascertained that these policy options vary not only in their mean effects, but also in their subordination to factors of chance or otherwise uncontrollable interference, relationships which themselves fluctuate across ranges of the counterfactual distribution. Broadly speaking, ring vaccination rivaled mass vaccination if a very substantial proportion of smallpox cases could be detected and isolated almost immediately after infection, or if residual herd immunity in the population was relatively high. Pre-attack mass vaccination and post-attack mass vaccination were equivalent in their capacities to eliminate the virus from the population within five months, but the pre-attack strategy did so with significantly fewer deaths in the process. Our results suggest that the debate between ring and mass vaccination approaches may hinge on better understanding residual herd immunity and the feasibility of early detection measures.

Deborah Duong and John Grefenstette
Journal of Artificial Societies and Social Simulation 8 (1) 1

Kyeywords: Agent-Based Model, Computational Social Theory, Economics Simulation, Symbolic Interactionism, Emergent Language, Sociological Roles
Abstract: SISTER, a Symbolic Interactionist Simulation of Trade and Emergent Roles, captures a fundamental social process by which macro level roles emerge from micro level symbolic interaction. The knowledge in a SISTER society is held culturally, suspended in the mutual expectations agents have of each other based on signs (tags) that they read and display. In this study, this knowledge includes how to create composite goods. The knowledge of coordinating their creation arises endogenously. A symbol system emerges to denote these tasks. In terms of information theory, the degree of mutual information between the agent\'s signs (tags) and their behavior increases over time. The SISTER society of this study is an economic simulation, in which agents have the choice of growing all the goods they need by themselves, or concentrating their efforts in making more of fewer goods and trading them for other goods. They induce the sign of an agent to trade with, while at the same time, they induce a sign to display. The signs come to mean sets of behaviors, or roles, through this double induction. A system of roles emerges, holding the knowledge of social coordination needed to distribute tasks among the agents.

Michael Agar
Journal of Artificial Societies and Social Simulation 8 (1) 4

Kyeywords: Agent-Based Models, Ethnography, Substance Use, Emic/etic, Validity, Netlogo
Abstract: The link between agent-based models and social research is a foundational concern of this journal. In this article, the anthropological concept of 'emic' or 'insider's view' is used to foreground the value of learning what differences make a difference to actual human agents before building a model of those agents and their world. The author's Netlogo model of the epidemiology of illicit drug use provides the example case. In the end, the emic does powerfully inform and constrain the model, but etic or 'outsider' views are required as well. At the same time, the way the model motivates these etic frameworks offers a strong test of theoretical relevance and a potential avenue towards theory integration.

Anselm Fleischmann
Journal of Artificial Societies and Social Simulation 8 (2) 4

Kyeywords: Agent-Based Modelling, Luhmann Economy, Fuzzy Clustering
Abstract: The core of this work is the definition of an agent-based model for a simple Luhmann economy based on publications of Niklas Luhmann. • Using an implementation on a default personal computer the behaviour of the model is studied when assumptions regarding initial conditions are made. Fuzzy-c-means clustering is used as visualisation aid. The impact of the observation horizon (a model parameter determining how far agents can see) is studied interactively. • Solution paths of the Luhmann economy originating from an initial endowment to equilibrium (when the economy settles down) are studied. • The impact of model parameters determining the unevenness regarding the initial distribution of wealth is studied by Monte Carlo simulation. Niklas Luhmann\'s hypothesis, that the economy starts from and produces further inequality in order to continue (see Luhmann 1988, p. 112) could be reproduced by computer simulation. The main characteristic of the approach is the consideration of the cohesive structure of communication (i.e. one communicative act - many understanding observers) also prominent in (Dunbar 1996, pp. 192-207). The model gives directions how to model further aspects of Niklas Luhmann\'s theory.

José Manuel Galán and Luis R. Izquierdo
Journal of Artificial Societies and Social Simulation 8 (3) 2

Kyeywords: Replication, Agent-Based Modelling, Evolutionary Game Theory, Social Dilemmas, Norms, Metanorms
Abstract: In this paper we try to replicate the simulation results reported by Axelrod (1986) in an influential paper on the evolution of social norms. Our study shows that Axelrod's results are not as reliable as one would desire. We can obtain the opposite results by running the model for longer, by slightly modifying some of the parameters, or by changing some arbitrary assumptions in the model. This re-implementation exercise illustrates the importance of running stochastic simulations several times for many periods, exploring the parameter space adequately, complementing simulation with analytical work, and being aware of the scope of our simulation models.

Josep M. Pujol, Andreas Flache, Jordi Delgado and Ramon Sangüesa
Journal of Artificial Societies and Social Simulation 8 (4) 12

Kyeywords: Complex Networks, Power-Law, Scale-Free, Small-World, Agent-Based Modeling, Social Exchange Theory, Structural Emergence
Abstract: Small-world and power-law network structures have been prominently proposed as models of large networks. However, the assumptions of these models usually lack sociological grounding. We present a computational model grounded in social exchange theory. Agents search attractive exchange partners in a diverse population. Agent use simple decision heuristics, based on imperfect, local information. Computer simulations show that the topological structure of the emergent social network depends heavily upon two sets of conditions, harshness of the exchange game and learning capacities of the agents. Further analysis show that a combination of these conditions affects whether star-like, small-world or power-law structures emerge.

Riccardo Boero and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 8 (4) 6

Kyeywords: Agent-Based Models, Empirical Calibration and Validation, Taxanomy of Models
Abstract: The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.

Peter Andras, John Lazarus, Gilbert Roberts and Steven J Lynden
Journal of Artificial Societies and Social Simulation 9 (1) 7

Kyeywords: Agent-Based Modelling, Cooperation, Social Interaction Simulation, Uncertainty
Abstract: Uncertainty is an important factor that influences social evolution in natural and artificial environments. Here we distinguish between three aspects of uncertainty. Environmental uncertainty is the variance of resources in the environment, perceived uncertainty is the variance of the resource distribution as perceived by the organism and effective uncertainty is the variance of resources effectively enjoyed by individuals. We show analytically that perceived uncertainty is larger than environmental uncertainty and that effective uncertainty is smaller than perceived uncertainty, when cooperation is present. We use an agent society simulation in a two dimensional world for the generation of simulation data as one realisation of the analytical results. Together with our earlier theoretical work, results here show that cooperation can buffer the detrimental effects of uncertainty on the organism. The proposed conceptualisation of uncertainty can help in understanding its effects on social evolution and in designing artificial social environments.

Michael Makowsky
Journal of Artificial Societies and Social Simulation 9 (2) 7

Kyeywords: Agent-Based Model, Crime, Bounded Rationality, Life Expectancy, Rational Choice
Abstract: Rational criminals choose crime over lawfulness because it pays better; hence poverty correlates to criminal behavior. This correlation is an insufficient historical explanation. An agent-based model of urban crime, mortality, and exogenous population shocks supplements the standard economic story, closing the gap with an empirical reality that often breaks from trend. Agent decision making within the model is built around a career maximization function, with life expectancy as the key independent variable. Rational choice takes the form of a local information heuristic, resulting in subjectively rational suboptimal decision making. The effects of population shocks are explored using the Crime and Mortality Simulation (CAMSIM), with effects demonstrated to persist across generations. Past social trauma are found to lead to higher crime rates which subsequently decline as the effect degrades, though \'aftershocks\' are often experienced.

Francesc S. Beltran, Laura Salas and Vicenç Quera
Journal of Artificial Societies and Social Simulation 9 (3) 5

Kyeywords: Spatial Behavior, Proxemics, Agent-Based Modeling, Minimum Dissatisfaction Model, Small Groups, Social Interaction
Abstract: We present an agent-based model with the aim of studying how macro-level dynamics of spatial distances among interacting individuals in a closed space emerge from micro-level dyadic and local interactions. Our agents moved on a lattice (referred to as a room) using a model implemented in a computer program called P-Space in order to minimize their dissatisfaction, defined as a function of the discrepancy between the real distance and the ideal, or desired, distance between agents. Ideal distances evolved in accordance with the agent\'s personal and social space, which changed throughout the dynamics of the interactions among the agents. In the first set of simulations we studied the effects of the parameters of the function that generated ideal distances, and in a second set we explored how group macro-level behavior depended on model parameters and other variables. We learned that certain parameter values yielded consistent patterns in the agents\' personal and social spaces, which in turn led to avoidance and approaching behaviors in the agents. We also found that the spatial behavior of the group of agents as a whole was influenced by the values of the model parameters, as well as by other variables such as the number of agents. Our work demonstrates that the bottom-up approach is a useful way of explaining macro-level spatial behavior. The proposed model is also shown to be a powerful tool for simulating the spatial behavior of groups of interacting individuals.

Dan Miodownik
Journal of Artificial Societies and Social Simulation 9 (4) 2

Kyeywords: Autonomy Movements, Ethno-Regional Mobilization, Constructivism, Agent-Based Modeling, Collective Identity
Abstract: Explanations of the emergence of regional autonomy movements - political organizations seeking to express sub-state affinities and interests - often highlight cultural differences and economic incentives as important reasons driving regional elites and local politicians to form such organization and explain the support regional autonomy movements receive. In this paper I employ a specialized agent-based computer simulation as a laboratory for 'thought experiments' to evaluate alternative theoretical expectations of the independent and combined consequences of regional economic and cultural circumstances on the likelihood of regional mobilization. The simulations suggest that pronounced cultural differences and strong economic incentives contribute to the emergence of three independent yet related aspects of autonomy mobilization: the emergence of political boundaries, minority support, and minority clustering. Furthermore, these experiment indicate that the impact of cultural differences on the emergence of political boundaries may be contingent on the strength of the economic incentives, and visa versa.

Sujai Kumar and Sugata Mitra
Journal of Artificial Societies and Social Simulation 9 (4) 3

Kyeywords: Self-Organizing Systems, Complex Systems, Traffic, Emergent Behaviour, Agent-Based Modelling, Rule-Breaking
Abstract: Traffic signals and traffic flow models have been studied extensively in the past and have provided valuable insights on the design of signalling systems, congestion control, and punitive policies. This paper takes a slightly different tack and describes what happens at an intersection where the traffic signals are malfunctioning and stuck in some configuration. By modelling individual vehicles as agents, we were able to replicate the surprisingly organized traffic flow that we observed at a real malfunctioning intersection in urban India. Counter-intuitively, the very lawlessness that normally causes jams was causing traffic to flow smoothly at this intersection. We situate this research in the context of other research on emergent complex phenomena in traffic, and suggest further lines of research that could benefit from the analysis and modelling of rule-breaking behaviour.

Paul Ormerod and Rich Colbaugh
Journal of Artificial Societies and Social Simulation 9 (4) 9

Kyeywords: Agent-Based Model; Connectivity; Complex Systems; Networks
Abstract: There is empirical evidence from a range of disciplines that as the connectivity of a network increases, we observe an increase in the average fitness of the system. But at the same time, there is an increase in the proportion of failure/extinction events which are extremely large. The probability of observing an extreme event remains very low, but it is markedly higher than in the system with lower degrees of connectivity. We are therefore concerned with systems whose properties are not static but which evolve dynamically over time. The focus in this paper, motivated by the empirical examples, is on networks in which the robustness or fragility of the vertices is not given, but which themselves evolve over time We give examples from complex systems such as outages in the US power grid, the robustness properties of cell biology networks, and trade links and the propagation of both currency crises and disease. We consider systems which are populated by agents which are heterogeneous in terms of their fitness for survival. The agents are connected on a network, which evolves over time. In each period agents take self-interested decisions to increase their fitness for survival to form alliances which increase the connectivity of the network. The network is subjected to external negative shocks both with respect to the size of the shock and the spatial impact of the shock. We examine the size/frequency distribution of extinctions and how this distribution evolves as the connectivity of the network grows. The results are robust with respect to the choice of statistical distribution of the shocks. The model is deliberately kept as parsimonious and simple as possible, and refrains from incorporating features such as increasing returns and externalities arising from preferential attachment which might bias the model in the direction of having the empirically observed features of many real world networks. The model still generates results consistent with the empirical evidence: increasing the number of connections causes an increase in the average fitness of agents, yet at the same time makes the system as whole more vulnerable to catastrophic failure/extinction events on an near-global scale.

Paul Windrum, Giorgio Fagiolo and Alessio Moneta
Journal of Artificial Societies and Social Simulation 10 (2) 8

Kyeywords: Methodology, Empirical Validation, Agent-Based Models, Simulation, Calibration, History-Friendly Models
Abstract: This paper addresses a set of methodological problems arising in the empirical validation of agent-based (AB) economics models and discusses how these are currently being tackled. These problems are generic for all those engaged in AB modelling, not just economists. The discussion is therefore of direct relevance to JASSS readers. The paper has two objectives. The first objective is the identification of a set of issues that are common to all modellers engaged in empirical validation. This gives rise to a novel taxonomy that captures the relevant dimensions along which AB modellers differ. The second objective is a focused discussion of three alternative methodological approaches being developed in AB economics - indirect calibration, the Werker-Brenner approach, and the history-friendly approach – and a set of (as yet) unresolved issues for empirical validation that require future research.

Stephen Wendel and Catherine Dibble
Journal of Artificial Societies and Social Simulation 10 (2) 9

Kyeywords: Agent-Based Modeling, Scaling, Homogeneity, Compression
Abstract: We introduce a new method for processing agents in agent-based models that significantly improves the efficiency of certain models. Dynamic Agent Compression allows agents to shift in and out of a compressed state based on their changing levels of heterogeneity. Sets of homogeneous agents are stored in compact bins, making the model more efficient in its use of memory and computational cycles. Modelers can use this increased efficiency to speed up the execution times, to conserve memory, or to scale up the complexity or number of agents in their simulations. We describe in detail an implementation of Dynamic Agent Compression that is lossless, i.e., no model detail is discarded during the compression process. We also contrast lossless compression to lossy compression, which promises greater efficiency gains yet may introduce artifacts in model behavior. The advantages outweigh the overhead of Dynamic Agent Compression in models where agents are unevenly heterogeneous — where a set of highly heterogeneous agents are intermixed with numerous other agents that fall into broad internally homogeneous categories. Dynamic Agent Compression is not appropriate in models with few, exclusively complex, agents.

Uri Wilensky and William Rand
Journal of Artificial Societies and Social Simulation 10 (4) 2

Kyeywords: Replication, Agent-Based Modeling, Verification, Validation, Scientific Method, Ethnocentrism
Abstract: Scientists have increasingly employed computer models in their work. Recent years have seen a proliferation of agent-based models in the natural and social sciences. But with the exception of a few "classic" models, most of these models have never been replicated by anyone but the original developer. As replication is a critical component of the scientific method and a core practice of scientists, we argue herein for an increased practice of replication in the agent-based modeling community, and for widespread discussion of the issues surrounding replication. We begin by clarifying the concept of replication as it applies to ABM. Furthermore we argue that replication may have even greater benefits when applied to computational models than when applied to physical experiments. Replication of computational models affects model verification and validation and fosters shared understanding about modeling decisions. To facilitate replication, we must create standards for both how to replicate models and how to evaluate the replication. In this paper, we present a case study of our own attempt to replicate a classic agent-based model. We begin by describing an agent-based model from political science that was developed by Axelrod and Hammond. We then detail our effort to replicate that model and the challenges that arose in recreating the model and in determining if the replication was successful. We conclude this paper by discussing issues for (1) researchers attempting to replicate models and (2) researchers developing models in order to facilitate the replication of their results.

Roy Wilson
Journal of Artificial Societies and Social Simulation 10 (4) 4

Kyeywords: Social Influence; Decision Processes; Social Networks; Group Dynamics; Simulation; Agent-Based Modeling
Abstract: This paper describes a simulation study of decision-making. It is based on a model of social influence in small, task-oriented, groups. A process model of dyadic social influence is built on top of a dynamic model of status and task participation that describes the emergence of a stable power and prestige order. Two models of group decision-making are examined: a static model for which the beliefs of actors do not change, and a process model for which they do as a function of the standing of each member of each interacting pair in the evolving power and prestige order. The models are compared on a set of N=111 cases, each requiring an affirmative or negative group response to a proposition A(c) that pertains to a case c. Initial beliefs are assigned to each of five members of distinct professions based on an analysis of independently collected behavioral data pertinent to the proposition to be affirmed or denied in each case. Although the two influence models yield identical decisions in 70% of the cases examined, the differences between them are statistically significant and in several instances show a medium effect size. Most importantly, the differences can be explained in terms of social influence and the status and task participation model on which it depends.

Guido Fioretti and Alessandro Lomi
Journal of Artificial Societies and Social Simulation 11 (1) 1

Kyeywords: Organization Theory, Garbage Can Model, Agent-Based Modelling
Abstract: Cohen, March and Olsen\'s Garbage Can Model (GCM) of organizational choice represent perhaps the first – and remains by far the most influential –agent-based representation of organizational decision processes. According to the GCM organizations are conceptualized as crossroads of time-dependent flows of four distinct classes of objects: \'participants,\' \'opportunities,\' \'solutions\' and \'problems.\' Collisions among the different objects generate events called \'decisions.\' In this paper we use NetLogo to build an explicit agent-based representation of the original GCM. We conduct a series of simulation experiments to validate and extend some of the most interesting conclusions of the GCM. We show that our representation is able to reproduce a number of properties of the original model. Yet, unlike the original model, in our representation these properties are not encoded explicitly, but emerge from general principles of the Garbage Can decision processes.

Sylvie Huet and Guillaume Deffuant
Journal of Artificial Societies and Social Simulation 11 (2) 10

Kyeywords: Primacy Effect, Information Filtering, Agent-Based Model, Aggregated Model, Collective Effects of Interactions, Double-Modelling
Abstract: We study a model of primacy effect on individual's attitude. Typically, when receiving a strong negative feature first, the individual keeps a negative attitude whatever the number of moderate positive features it receives afterwards. We consider a population of individuals, which receive the features from a media, and communicate with each other. We observe that interactions favour the primacy effect, compared with a population of isolated individuals. We derive a differential equation system ruling the evolution of probabilities that individuals retain different sets of features. The study of this aggregated model of the IBM shows that interaction can increase or decrease the number of individuals exhibiting a primacy effect. We verify on the IBM that the interactions can decrease the primacy effect in the conditions suggested by the study of the aggregated model. We finally discuss the interest of such a double-modelling approach (using a model of the individual based model) for this application.

Ravi Bhavnani, Dan Miodownik and Jonas Nart
Journal of Artificial Societies and Social Simulation 11 (2) 7

Kyeywords: Agent-Based Model, Ethnicity, Salience, Polarization, Domination, Civil War, Greed, Natural Resources
Abstract: This research note provides a general introduction to REsCape: an agent-based computational framework for studying the relationship between natural resources, ethnicity, and civil war. By permitting the user to specify: (i) different resource profiles ranging from a purely agrarian economy to one based on the artisanal or industrial extraction of alluvial or kimberlite diamonds; (ii) different patterns of ethnic domination, ethnic polarization, and varying degrees of ethnic salience; as well as (iii) specific modes of play for key agents, the framework can be used to assess the effects of key variables — whether taken in isolation or in various combinations — on the onset and duration of civil war. Our objective is to make REsCape available as an open source toolkit in the future, one that can be used, modified, and refined by students and scholars of civil war.

Juliette Rouchier, Claudio Cioffi-Revilla, J. Gareth Polhill and Keiki Takadama
Journal of Artificial Societies and Social Simulation 11 (2) 8

Kyeywords: Social Simulation, Agent-Based Modelling, Comparative Computational Methodology, Validation, Replication
Abstract: [No abstract]

Oliver Will and Rainer Hegselmann
Journal of Artificial Societies and Social Simulation 11 (3) 3

Kyeywords: Replication, Social Dilemmas, Simulation Methodology, Cooperation, Trust, Agent-Based Modelling
Abstract: The article describes how and why we failed to replicate main effects of a computational model that Michael Macy and Yoshimichi Sato published in the Proceedings of the National Academy of Sciences (May 2002). The model is meant to answer a fundamental question about social life: Why, when and how is it possible to build trust with distant people? Based on their model, Macy and Sato warn the US society about an imminent danger: the possible break down of trust caused by too much social mobility. But the computational evidence for exactly that result turned out not to be replicable.

Noam Bergman, Alex Haxeltine, Lorraine Whitmarsh, Jonathan Köhler, Michel Schilperoord and Jan Rotmans
Journal of Artificial Societies and Social Simulation 11 (3) 7

Kyeywords: Complex Systems, Agent-Based Modelling, Social Simulation, Transitions, Transition Theory
Abstract: We report on research that is developing a simulation model for assessing systemic innovations, or 'transitions', of societal systems towards a more sustainable development. Our overall aim is to outline design principles for models that can offer new insights into tackling persistent problems in large-scale systems, such as the European road transport system or the regional management of water resources. The systemic nature of these problems is associated with them being complex, uncertain and cutting across a number of sectors, and indicates a need for radical technological and behavioural solutions that address changes at the systems level rather than offering incremental changes within sub-systems. Model design is inspired by recent research into transitions, an emerging paradigm which provides a framework for tackling persistent problems. We use concepts from the literature on transitions to develop a prototype of a generic 'transition model'. Our prototype aims to capture different types of transition pathways, using historical examples such as the transition from horse-drawn carriages to cars or that from sailing ships to steam ships. The model combines agent-based modelling techniques and system dynamics, and includes interactions of individual agents and sub-systems, as well as cumulative effects on system structures. We show success in simulating different historical transition pathways by adapting the model's parameters and rules for each example. Finally, we discuss the improvements necessary for systematically exploring and detailing transition pathways in empirical case-study applications to current and future transitions such as the transition to a sustainable transport system in Europe.

Michael Macy and Yoshimichi Sato
Journal of Artificial Societies and Social Simulation 11 (4) 11

Kyeywords: Replication, Social Dilemmas, Simulation Methodology, Cooperation, Trust, Agent-Based Modelling
Abstract: [No abstract]

Shuguang Suo and Yu Chen
Journal of Artificial Societies and Social Simulation 11 (4) 2

Kyeywords: Public Opinion, Complex Network, Consensus, Agent-Based Model
Abstract: This paper studies the problem of public opinion formation and concentrates on the interplays among three factors: individual attributes, environmental influences and information flow. We present a simple model to analyze the dynamics of four types of networks. Our simulations suggest that regular communities establish not only local consensus, but also global diversity in public opinions. However, when small world networks, random networks, or scale-free networks model social relationships, the results are sensitive to the elasticity coefficient of environmental influences and the average connectivity of the type of network. For example, a community with a higher average connectivity has a higher probability of consensus. Yet, it is misleading to predict results merely based on the characteristic path length of networks. In the process of changing environmental influences and average connectivity, sensitive areas are discovered in the system. By sensitive areas we mean that interior randomness emerges and we cannot predict unequivocally how many opinions will remain upon reaching equilibrium. We also investigate the role of authoritative individuals in information control. While enhancing average connectivity facilitates the diffusion of the authoritative opinion, it makes individuals subject to disturbance from non-authorities as well. Thus, a moderate average connectivity may be preferable because then the public will most likely form an opinion that is parallel with the authoritative one. In a community with a scale-free structure, the influence of authoritative individuals keeps constant with the change of the average connectivity. Provided that the influence of individuals is proportional to the number of their acquaintances, the smallest percentage of authorities is required for a controlled consensus in a scale free network. This study shows that the dynamics of public opinion varies from community to community due to the different degree of impressionability of people and the distinct social network structure of the community.

James Millington, Raúl Romero-Calcerrada, John Wainwright and George Perry
Journal of Artificial Societies and Social Simulation 11 (4) 4

Kyeywords: Land Use/Cover Change, Land Tenure, Wildfire, Mediterranean-Type Ecosystem, Agriculture, Spatial Heterogeneity
Abstract: Humans have a long history of activity in Mediterranean Basin landscapes. Spatial heterogeneity in these landscapes hinders our understanding about the impacts of changes in human activity on ecological processes, such as wildfire. The use of spatially-explicit models that simulate processes at fine scales should aid the investigation of spatial patterns at the broader, landscape scale. Here, we present an agent-based model of agricultural land-use decision-making to examine the importance of land tenure and land use on future land cover. The model considers two 'types' of land-use decision-making agent with differing perspectives; 'commercial' agents that are perfectly economically rational, and 'traditional' agents that represent part-time or 'traditional' farmers that manage their land because of its cultural, rather than economic, value. The structure of the model is described and results are presented for various scenarios of initial landscape configuration. Land-use/cover maps produced by the model are used to examine how wildfire risk changes for each scenario. Results indicate that land tenure configuration influences trajectories of land use change. However, simulations for various initial land-use configurations and compositions converge to similar states when land-tenure structure is held constant. For the scenarios considered, mean wildfire risk increases relative to the observed landscape. Increases in wildfire risk are not spatially uniform however, varying according to the composition and configuration of land use types. These unexpected spatial variations in wildfire risk highlight the advantages of using a spatially-explicit agent-based model of land use/cover change.

José Manuel Galán, Luis R. Izquierdo, Segismundo S. Izquierdo, José Ignacio Santos, Ricardo del Olmo, Adolfo López-Paredes and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 12 (1) 1

Kyeywords: Verification, Replication, Artefact, Error, Agent-Based Modelling, Modelling Roles
Abstract: The objectives of this paper are to define and classify different types of errors and artefacts that can appear in the process of developing an agent-based model, and to propose activities aimed at avoiding them during the model construction and testing phases. To do this in a structured way, we review the main concepts of the process of developing such a model – establishing a general framework that summarises the process of designing, implementing, and using agent-based models. Within this framework we identify the various stages where different types of errors and artefacts may appear. Finally we propose activities that could be used to detect (and hence eliminate) each type of error or artefact.

Luca Arciero, Claudia Biancotti, Leandro D'Aurizio and Claudio Impenna
Journal of Artificial Societies and Social Simulation 12 (1) 2

Kyeywords: Agent-Based Modeling, Payment Systems, RTGS, Liquidity, Crisis Simulation
Abstract: This paper presents an exploratory agent-based model of a real time gross settlement (RTGS) payment system. Banks are represented as agents who exchange payment requests, which are then settled according to a set of simple rules. The model features the main elements of a real-life system, including a central bank acting as liquidity provider, and a simplified money market. A simulation exercise using synthetic data of BI-REL (the Italian RTGS) predicts the macroscopic impact of a disruptive event on the flow of interbank payments. In our reduced-scale system, three hypothetical distinct phases emerge after the disruptive event: 1) a liquidity sink effect is generated and the participants\' liquidity expectations turn out to be excessive; 2) an illusory thickening of the money market follows, along with increased payment delays; and, finally 3) defaulted obligations dramatically rise. The banks cannot staunch the losses accruing on defaults, even after they become fully aware of the critical event, and a scenario emerges in which it might be necessary for the central bank to step in as liquidity provider.

Pedro Ribeiro de Andrade, Antonio Miguel Vieira Monteiro, Gilberto Câmara and Sandra Sandri
Journal of Artificial Societies and Social Simulation 12 (1) 5

Kyeywords: Spatial Games, Agent-Based Modelling, Mobility, Satisfaction, Chicken Game, Nash Equilibrium
Abstract: In this work we propose a new model for spatial games. We present a definition of mobility in terms of the satisfaction an agent has with its spatial location. Agents compete for space through a non-cooperative game by using mixed strategies. We are particularly interested in studyig the relation between Nash equilibrium and the winner strategy of a given model with mobility, and how the mobility can affect the results. The experiments show that mobility is an important variable concerning spatial games. When we change parameters that affect mobility, it may lead to the success of strategies away from Nash equilibrium.

Daniel Kornhauser, Uri Wilensky and William Rand
Journal of Artificial Societies and Social Simulation 12 (2) 1

Kyeywords: Visualization, Design, Graphics, Guidelines, Communication, Agent-Based Modeling
Abstract: In the field of agent-based modeling (ABM), visualizations play an important role in identifying, communicating and understanding important behavior of the modeled phenomenon. However, many modelers tend to create ineffective visualizations of Agent Based Models (ABM) due to lack of experience with visual design. This paper provides ABM visualization design guidelines in order to improve visual design with ABM toolkits. These guidelines will assist the modeler in creating clear and understandable ABM visualizations. We begin by introducing a non-hierarchical categorization of ABM visualizations. This categorization serves as a starting point in the creation of an ABM visualization. We go on to present well-known design techniques in the context of ABM visualization. These techniques are based on Gestalt psychology, semiology of graphics, and scientific visualization. They improve the visualization design by facilitating specific tasks, and providing a common language to critique visualizations through the use of visual variables. Subsequently, we discuss the application of these design techniques to simplify, emphasize and explain an ABM visualization. Finally, we illustrate these guidelines using a simple redesign of a NetLogo ABM visualization. These guidelines can be used to inform the development of design tools that assist users in the creation of ABM visualizations.

Lynne Hamill and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 12 (2) 3

Kyeywords: Social Networks, Personal Networks, Agent-Based Models
Abstract: None of the standard network models fit well with sociological observations of real social networks. This paper presents a simple structure for use in agent-based models of large social networks. Taking the idea of social circles, it incorporates key aspects of large social networks such as low density, high clustering and assortativity of degree of connectivity. The model is very flexible and can be used to create a wide variety of artificial social worlds.

Andrew Crooks, Andrew Hudson-Smith and Joel Dearden
Journal of Artificial Societies and Social Simulation 12 (4) 10

Kyeywords: Agent-Based Modelling, Pedestrian Evacuation, Segregation, Virtual Worlds, Second Life
Abstract: Urban models can be seen on a continuum between iconic and symbolic. Generally speaking, iconic models are physical versions of the real world at some scaled down representation, while symbolic models represent the system in terms of the way they function replacing the physical or material system by some logical and/or mathematical formulae. Traditionally iconic and symbolic models were distinct classes of model but due to the rise of digital computing the distinction between the two is becoming blurred, with symbolic models being embedded into iconic models. However, such models tend to be single user. This paper demonstrates how 3D symbolic models in the form of agent-based simulations can be embedded into iconic models using the multi-user virtual world of Second Life. Furthermore, the paper demonstrates Second Life\'s potential for social science simulation. To demonstrate this, we first introduce Second Life and provide two exemplar models; Conway\'s Game of Life, and Schelling\'s Segregation Model which highlight how symbolic models can be viewed in an iconic environment. We then present a simple pedestrian evacuation model which merges the iconic and symbolic together and extends the model to directly incorporate avatars and agents in the same environment illustrating how \'real\' participants can influence simulation outcomes. Such examples demonstrate the potential for creating highly visual, immersive, interactive agent-based models for social scientists in multi-user real time virtual worlds. The paper concludes with some final comments on problems with representing models in current virtual worlds and future avenues of research.

Stefania Bandini, Sara Manzoni and Giuseppe Vizzari
Journal of Artificial Societies and Social Simulation 12 (4) 4

Kyeywords: Multi-Agent Systems, Agent-Based Modeling and Simulation
Abstract: The term computer simulation is related to the usage of a computational model in order to improve the understanding of a system's behavior and/or to evaluate strategies for its operation, in explanatory or predictive schemes. There are cases in which practical or ethical reasons make it impossible to realize direct observations: in these cases, the possibility of realizing 'in-machina' experiments may represent the only way to study, analyze and evaluate models of those realities. Different situations and systems are characterized by the presence of autonomous entities whose local behaviors (actions and interactions) determine the evolution of the overall system; agent-based models are particularly suited to support the definition of models of such systems, but also to support the design and implementation of simulators. Agent-Based models and Multi-Agent Systems (MAS) have been adopted to simulate very different kinds of complex systems, from the simulation of socio-economic systems to the elaboration of scenarios for logistics optimization, from biological systems to urban planning. This paper discusses the specific aspects of this approach to modeling and simulation from the perspective of Informatics, describing the typical elements of an agent-based simulation model and the relevant research.

Herbert Dawid, Simon Gemkow, Philipp Harting and Michael Neugart
Journal of Artificial Societies and Social Simulation 12 (4) 5

Kyeywords: Agent-Based Model, Skills, Innovation, Regional Policy
Abstract: We report results of economic policy experiments carried out in the framework of the EURACE agent-based macroeconomic model featuring a distinct geographical dimension and heterogeneous workers with respect to skill types. Using a calibrated model able to replicate a range of stylized facts of goods and labor markets, it is examined in how far effects differ if policy measures aiming at an improvement of general skills are uniformly spread over all regions in the economy or focused in one particular region. We find that it depends on the level of spatial frictions on the labor market how the spatial distribution of policy measures affects the effects of the policy. Furthermore, we show that a reduction in spatial frictions does not necessarily improve the growth of output and household income.

Simon Deichsel and Andreas Pyka
Journal of Artificial Societies and Social Simulation 12 (4) 6

Kyeywords: Methodology, Agent-Based Modelling, Assumptions, Calibration
Abstract: The issues of empirical calibration of parameter values and functional relationships describing the interactions between the various actors plays an important role in agent based modelling. Agent-based models range from purely theoretical exercises focussing on the patterns in the dynamics of interactions processes to modelling frameworks which are oriented closely at the replication of empirical cases. ABMs are classified in terms of their generality and their use of empirical data. In the literature the recommendation can be found to aim at maximizing both criteria by building so-called 'abductive models'. This is almost the direct opposite of Milton Friedman's famous and provocative methodological credo 'the more significant a theory, the more unrealistic the assumptions'. Most methodologists and philosophers of science have harshly criticised Friedman's essay as inconsistent, wrong and misleading. By presenting arguments for a pragmatic reinterpretation of Friedman's essay, we will show why most of the philosophical critique misses the point. We claim that good simulations have to rely on assumptions, which are adequate for the purpose in hand and those are not necessarily the descriptively accurate ones.

Julian Padget, Richard Vidgen, James Mitchell, Amy Marshall and Rick Mellor
Journal of Artificial Societies and Social Simulation 12 (4) 8

Kyeywords: Coevolution, Agent-Based Modelling, NK, NKCS, Fitness Landscape
Abstract: The idea of agents exploring a fitness landscape in which they seek to move from 'fitness valleys' to higher 'fitness peaks' has been presented by Kauffman in the NK and NKCS models. The NK model addresses single species while the NKCS extension illustrates coevolving species on coupled fitness landscapes. We describe an agent-based simulation (Sendero), built in Repast, of the NK and NKCS models. The results from Sendero are validated against Kauffman's findings for the NK and NKCS models. We also describe extensions to the basic model, including population dynamics and communication networks for NK, and directed graphs and variable change rates for NKCS. The Sendero software is available as open source under the BSD licence and is thus available for download and extension by the research community.

Brian Heath, Raymond Hill and Frank Ciarallo
Journal of Artificial Societies and Social Simulation 12 (4) 9

Kyeywords: Agent-Based Modeling, Survey, Current Practices, Simulation Validation, Simulation Purpose
Abstract: In the 1990s, Agent-Based Modeling (ABM) began gaining popularity and represents a departure from the more classical simulation approaches. This departure, its recent development and its increasing application by non-traditional simulation disciplines indicates the need to continuously assess the current state of ABM and identify opportunities for improvement. To begin to satisfy this need, we surveyed and collected data from 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an agent-based model. From this large data set we establish the current practice of ABM in terms of year of publication, field of study, simulation software used, purpose of the simulation, acceptable validation criteria, validation techniques and complete description of the simulation. Based on the current practice we discuss six improvements needed to advance ABM as an analysis tool. These improvements include the development of ABM specific tools that are independent of software, the development of ABM as an independent discipline with a common language that extends across domains, the establishment of expectations for ABM that match their intended purposes, the requirement of complete descriptions of the simulation so others can independently replicate the results, the requirement that all models be completely validated and the development and application of statistical and non-statistical validation techniques specifically for ABM.

Mark Altaweel, Lilian N. Alessa and Andrew D. Kliskey
Journal of Artificial Societies and Social Simulation 13 (1) 15

Kyeywords: Agent-Based Modeling, Artificial Neural Network, Social Network, Social Influence, Resilience, Freshwater
Abstract: This paper presents a model, using concepts from artificial neural networks, that explains how small rural communities make decisions that affect access to potable freshwater. Field observations indicate that social relationships as well as individual goals and perceptions of decision makers have a strong influence on decisions that are made by community councils. Our work identifies three types of agents, which we designate as alpha, beta, and gamma agents. We address how gamma agents affect decisions made by community councils in passing resolutions that benefit a village\'s collective access to clean freshwater. The model, which we call the Agent Types Model (ATM), demonstrates the effects of social interactions, corporate influence, and agent-specific factors that determine choices for agents. Data from two different villages in rural Alaska and several parameter sensitivity tests are applied to the model. Results demonstrate that minimizing the social significance and agent-specific factors affecting gamma agents\' negative compliance increases the likelihood that communities adopt measures promoting potable freshwater access. The significance of this work demonstrates which types of communities are potentially more socially vulnerable or resilient to social-ecological change affecting water supplies.

Pierre Livet, Jean-Pierre Muller, Denis Phan and Lena Sanders
Journal of Artificial Societies and Social Simulation 13 (1) 3

Kyeywords: Ontology, Agent-Based Computational Economic, Agent-Based Model of Simulation, Model Design, Model Building, Knowledge Framework, Spatial Simulation, Social Simulation, Ontological Test
Abstract: Agent-Based Models are useful to describe and understand social, economic and spatial systems\' dynamics. But, beside the facilities which this methodology offers, evaluation and comparison of simulation models are sometimes problematic. A rigorous conceptual frame needs to be developed. This is in order to ensure the coherence in the chain linking at the one extreme the scientist\'s hypotheses about the modeled phenomenon and at the other the structure of rules in the computer program. This also systematizes the model design from the thematician conceptual framework as well. The aim is to reflect upon the role that a well defined ontology, based on the crossing of the philosophical and the computer science insights, can play to solve such questions and help the model building. We analyze different conceptions of ontology, introduce the \'ontological test\' and show its usefulness to compare models. Then we focus on the model building and show the place of a systematic ABM ontology. The latter process is situated within a larger framework called the \'knowledge framework\' in which not only the ontologies but also the notions of theory, model and empirical data take place. At last the relation between emergence and ontology is discussed.

Denis Phan and Franck Varenne
Journal of Artificial Societies and Social Simulation 13 (1) 5

Kyeywords: Agent-Based Models and Simulations, Epistemology, Economics, Social Sciences, Conceptual Exploration, Model World, Credible World, Experiment, Denotational Hierarchy
Abstract: Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through a simulation, section 2 gives the possibility to understand more precisely - and then to justify - the diversity of the epistemological positions presented in section 1. Our final claim is that careful attention to the multiplicity of the denotational powers of symbols at stake in complex models and computer simulations is necessary to determine, in each case, their proper epistemic status and credibility.

Federico Cecconi, Marco Campennì, Giulia Andrighetto and Rosaria Conte
Journal of Artificial Societies and Social Simulation 13 (1) 6

Kyeywords: Agent-Based Modelling, Equation-Based Modelling, Congestion Game, Model of Social Phenomena
Abstract: In this work simulation-based and analytical results on the emergence steady states in traffic-like interactions are presented and discussed. The objective of the paper is twofold: i) investigating the role of social conventions in coordination problem situations, and more specifically in congestion games; ii) comparing simulation-based and analytical results to figure out what these methodologies can tell us on the subject matter. Our main issue is that Agent-Based Modelling (ABM) and the Equation-Based Modelling (EBM) are not alternative, but in some circumstances complementary, and suggest some features distinguishing these two ways of modeling that go beyond the practical considerations provided by Parunak H.V.D., Robert Savit and Rick L. Riolo. Our model is based on the interaction of strategies of heterogeneous agents who have to cross a junction. In each junction there are only four inputs, each of which is passable only in the direction of the intersection and can be occupied only by an agent one at a time. The results generated by ABM simulations provide structured data for developing the analytical model through which generalizing the simulation results and make predictions. ABM simulations are artifacts that generate empirical data on the basis of the variables, properties, local rules and critical factors the modeler decides to implement into the model; in this way simulations allow generating controlled data, useful to test the theory and reduce the complexity, while EBM allows to close them, making thus possible to falsify them.

Claudio Cioffi-Revilla
Journal of Artificial Societies and Social Simulation 13 (1) 7

Kyeywords: Agent-Based Modeling Methodology, M2M, Social Simulation, Computational Social Science, Social Complexity, Inner Asia
Abstract: Social simulation - an emerging field of computational social science - has progressed from simple toy models to increasingly realistic models of complex social systems, such as agent-based models where heterogeneous agents interact with changing natural or artificial environments. These larger, multidisciplinary projects require a scientific research methodology distinct from, say, simpler social simulations with more limited scope, intentionally minimal complexity, and typically under a single investigator. This paper proposes a methodology for complex social simulations - particularly inter- and multi-disciplinary socio-natural systems with multi-level architecture - based on a succession of models akin to but distinct from the late Imre Lakatos' notion of a 'research programme'. The proposed methodology is illustrated through examples from the Mason-Smithsonian project on agent-based models of the rise and fall of polities in Inner Asia. While the proposed methodology requires further development, so far it has proven valuable for advancing the scientific objectives of the project and avoiding some pitfalls.

J. Gareth Polhill, Lee-Ann Sutherland and Nicholas M. Gotts
Journal of Artificial Societies and Social Simulation 13 (2) 10

Kyeywords: Agent-Based Modelling, Land Use/Cover Change, Qualitative Research, Interdisciplinary Research
Abstract: This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable insights that qualitative methods could provide is not always feasible. At the same time, many researchers advocate building completely new models for each scenario to be studied, violating one of the supposed advantages of the object-oriented programming languages in which many such systems are built: that of code reuse. The process described here uses coded interviews to identify themes suggesting changes to an existing model, the assumptions behind which are then checked with respondents. We find this increases the confidence with which the extended model can be applied to the case study, with a relatively small commitment required on the part of respondents.

Sungho Lee
Journal of Artificial Societies and Social Simulation 13 (2) 3

Kyeywords: Public IT Investment, Interoperability, Standardization, Social Network, Agent-Based Modeling, Exploratory Modeling
Abstract: Governments have come under increasing pressure to promote horizontal flows of information across agencies, but investment in cross-agency interoperable and standard systems have been minimally made since it seems to require government agencies to give up the autonomies in managing own systems and its outcomes may be subject to many external and interaction risks. By producing an agent-based model using 'Blanche' software, this study provides policy-makers with a simulation-based demonstration illustrating how government agencies can autonomously and interactively build, standardize, and operate interoperable IT systems in a decentralized environment. This simulation designs an illustrative body of 20 federal agencies and their missions. A multiplicative production function is adopted to model the interdependent effects of heterogeneous systems on joint mission capabilities, and six social network drivers (similarity, reciprocity, centrality, mission priority, interdependencies, and transitivity) are assumed to jointly determine inter-agency system utilization. This exercise simulates five policy alternatives derived from joint implementation of three policy levers (IT investment portfolio, standardization, and inter-agency operation). The simulation results show that modest investments in standard systems improve interoperability remarkably, but that a wide range of untargeted interoperability with lagging operational capabilities improves mission capability less remarkably. Nonetheless, exploratory modeling against the varying parameters for technology, interdependency, and social capital demonstrates that the wide range of untargeted interoperability responds better to uncertain future states and hence reduces the variances of joint mission capabilities. In sum, decentralized and adaptive investments in interoperable and standard systems can enhance joint mission capabilities substantially and robustly without requiring radical changes toward centralized IT management.

Jasper Muis
Journal of Artificial Societies and Social Simulation 13 (2) 4

Kyeywords: Agent-Based Model, Voting Behaviour, Mass Media, Empirical Validation
Abstract: Agent-based models of political party competition in a multidimensional policy space have been developed in order to reflect adaptive learning by party leaders with very limited information feedback. The key assumption is that two categories of actors continually make decisions: voters choose which party to support and party leaders offer citizens a certain policy package. After reviewing the arguments for using agent-based models, I elaborate two ways forward in the development of these models for political party competition. Firstly, theoretical progress is made in this article by taking the role of the mass media into account. In previous work it is implicitly assumed that all parties are equally visible for citizens, whereas I will start from the more realistic assumption that there is also competition for attention in the public sphere. With this addition, it is possible to address the question why new parties are seldom able to successfully compete with political actors already within the political system. Secondly, I argue that, if we really want to learn useful lessons from simulations, we should seek to empirically falsify models by confronting outcomes with real data. So far, most of the agent-based models of party competition have been an exclusively theoretical exercise. Therefore, I evaluate the empirical relevance of different simulations of Dutch party competition in the period from May 1998 until May 2002. Using independent data on party positions, I measure the extent to which simulations generate mean party sizes that resemble public opinion polls. The results demonstrate that it is feasible and realistic to simulate party competition in the Netherlands with agent-based models, even when a rather unstable period is investigated.

Tibor Bosse and Charlotte Gerritsen
Journal of Artificial Societies and Social Simulation 13 (2) 5

Kyeywords: Agent-Based Modelling, Criminal Hot Spots, Displacement, Reputation, Social Simulation, Analysis
Abstract: Within the field of Criminology, the spatio-temporal dynamics of crime are an important subject of study. In this area, typical questions are how the behaviour of offenders, targets, and guardians can be explained and predicted, as well as the emergence and displacement of criminal hot spots. In this article we present a combination of software tools that can be used as an experimental environment to address such questions. In particular, these tools comprise an agent-based simulation model, a verification tool, and a visualisation tool. The agent-based simulation model specifically focuses on the interplay between hot spots and reputation. Using this environment, a large number of simulation runs have been performed, of which results have been formally analysed. Based on these results, we argue that the presented environment offers a valuable approach to analyse the dynamics of criminal hot spots.

Dan Miodownik, Britt Cartrite and Ravi Bhavnani
Journal of Artificial Societies and Social Simulation 13 (3) 1

Kyeywords: Replication, Docking, Agent-Based Model, Italy, Social Capital
Abstract: This article has two primary objectives: (i) to replicate an agent-based model of social interaction by Bhavnani (2003), in which the author explicitly specifies mechanisms underpinning Robert Putnam\'s (1993) work on Civic Traditions in Modern Italy, bridging the gap between the study\'s historical starting point—political regimes that characterized 14th Century Italy—and contemporary levels of social capital—reflected in a \'civic\' North and an \'un-civic\' South; and (ii) to extend the original analysis, using a landscape of Italy that accounts for population density. The replication exercise is performed by different authors using an entirely distinct ABM toolkit (PS-I) with its own rule set governing agent-interaction and cultural change. The extension, which more closely approximates a docking exercise, utilizes equal area cartograms otherwise known as density-equalizing maps (Gastner and Newman 2004) to resize the territory according to 1993 population estimates. Our results indicate that: (i) using the criterion of distributional equivalence, we experience mixed success in replicating the original model given our inability to restrict the selection of partners to \'eligible\' neighbors and limit the number of agent interactions in a timestep; (ii) increasing the number of agents and introducing more realistic population distributions in our extension of the replication model increases distributional equivalence; (iii) using the weaker criteria of relational alignment, both the replication model and its extension capture the basic relationship between institutional effectiveness and civic change, the effect of open boundaries, historical shocks, and path dependence; and (iv) that replication and docking may be usefully combined in model-to-model analysis, with an eye towards verification, reimplementation, and alignment.

Riccardo Boero, Giangiacomo Bravo, Marco Castellani and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 13 (3) 6

Kyeywords: Reputation, Trustworthiness, Laboratory Experiment, Agent-Based Model, Exploration Vs. Exploitation
Abstract: This paper investigates the relevance of reputation to improve the explorative capabilities of agents in uncertain environments. We have presented a laboratory experiment where sixty-four subjects were asked to take iterated economic investment decisions. An agent-based model based on their behavioural patterns replicated the experiment exactly. Exploring this experimentally grounded model, we studied the effects of various reputational mechanisms on explorative capabilities at a systemic level. The results showed that reputation mechanisms increase the agents\' capability for coping with uncertain environments more than individualistic atomistic exploration strategies, although the former does entail a certain amount of false information inside the system.

Wolfgang Radax and Bernhard Rengs
Journal of Artificial Societies and Social Simulation 13 (4) 1

Kyeywords: Agent-Based Model, Verification, Comparative Computational Methodology, Prisoners Dilemma, Replication, Demographic Prisoners Dilemma
Abstract: This paper documents our efforts (and troubles) in replicating Epstein's (1998) demographic prisoner's dilemma model. Confronted with a number of ambiguous descriptions of model features we introduce a method for systematically generating a large number of model replications and testing for their equivalence to the original model. While, qualitatively speaking, a number of our replicated models resemble the results of the original model reasonably well, statistical testing reveals that in quantitative terms our endeavor was only partially successful. This fact hints towards some unstated assumptions regarding the original model. Finally we conduct a number of statistical tests with respect to the influence of certain design choices like the method of updating, the timing of events and the randomization of the activation order. The results of these tests highlight the importance of an explicit documentation of design choices and especially of the timing of events. A central lesson learned from this exercise is that the power of statistical replication analysis is to a large degree determined by the available data.

Lynne Hamill
Journal of Artificial Societies and Social Simulation 13 (4) 7

Kyeywords: Agent-Based Modelling,, NetLogo, Policy Advice
Abstract: This short note makes recommendations for the future direction of research in agent-based modelling (ABM). It is a personal view based on my experience as a policy adviser who has recently come to ABM. I suggest that to promote the use of ABM, the ABM community needs demonstrate the value of modelling to other social scientists by showing-by-doing and offering training projects; and to produce tools, guidance on good-practice and basic building blocks. Then the policy contexts most likely to benefit from ABM need to be identified along with any new data requirements, so that the usefulness of ABM can be demonstrated to policy analysts. This is, in my view, the challenge facing the ABM community for the next 15 years.

Adam G. Dunn and Blanca Gallego
Journal of Artificial Societies and Social Simulation 13 (4) 8

Kyeywords: Innovation Diffusion, Scale-Free Networks, Health Policy, Agent-Based Modelling
Abstract: Medical innovations, in the form of new medication or other clinical practices, evolve and spread through health care systems, impacting on the quality and standards of health care provision, which is demonstrably heterogeneous by geography. Our aim is to investigate the potential for the diffusion of innovation to influence health inequality and overall levels of recommended care. We extend existing diffusion of innovation models to produce agent-based simulations that mimic population-wide adoption of new practices by doctors within a network of influence. Using a computational model of network construction in lieu of empirical data about a network, we simulate the diffusion of competing innovations as they enter and proliferate through a state system comprising 24 geo-political regions, 216 facilities and over 77,000 individuals. Results show that stronger clustering within hospitals or geo-political regions is associated with slower adoption amongst smaller and rural facilities. Results of repeated simulation show how the nature of uptake and competition can contribute to low average levels of recommended care within a system that relies on diffusive adoption. We conclude that an increased disparity in adoption rates is associated with high levels of clustering in the network, and the social phenomena of competitive diffusion of innovation potentially contributes to low levels of recommended care.

Peer-Olaf Siebers and Uwe Aickelin
Journal of Artificial Societies and Social Simulation 14 (2) 2

Kyeywords: Retail Performance, Management Practices, Proactive Behaviour, Service Experience, Agent-Based Modelling, Simulation
Abstract: There has been a noticeable shift in the relative composition of the industry in the developed countries in recent years; manufacturing is decreasing while the service sector is becoming more important. However, currently most simulation models for investigating service systems are still built in the same way as manufacturing simulation models, using a process-oriented world view, i.e. they model the flow of passive entities through a system. These kinds of models allow studying aspects of operational management but are not well suited for studying the dynamics that appear in service systems due to human behaviour. For these kinds of studies we require tools that allow modelling the system and entities using an object-oriented world view, where intelligent objects serve as abstract \'actors\' that are goal directed and can behave proactively. In our work we combine process-oriented discrete event simulation modelling and object-oriented agent based simulation modelling to investigate the impact of people management practices on retail productivity. In this paper, we reveal in a series of experiments what impact considering proactivity can have on the output accuracy of simulation models of human centric systems. The model and data we use for this investigation are based on a case study in a UK department store. We show that considering proactivity positively influences the validity of these kinds of models and therefore allows analysts to make better recommendations regarding strategies to apply people management practices.

Christof Knoeri, Claudia R. Binder and Hans-Joerg Althaus
Journal of Artificial Societies and Social Simulation 14 (2) 4

Kyeywords: Agent Operationalization, Decision-Making, Analytical Hierarchy Process (AHP), Agent-Based Modeling, Conceptual Validation
Abstract: The potential of agent-based modeling (ABM) has been demonstrated in various research fields. However, three major concerns limit the full exploitation of ABM; (i) agents are too simple and behave unrealistically without any empirical basis, (ii) \'proof of concept\' applications are too theoretical and (iii) too much value placed on operational validity instead of conceptual validity. This paper presents an operationalization approach to determine the key system agents, their interaction, decision-making and behavior for context specific ABM, thus addressing the above-mentioned shortcomings. The approach is embedded in the framework of Giddens\' structuration theory and the structural agent analysis (SAA). The agents\' individual decision-making (i.e. reflected decisions) is operationalized by adapting the analytical hierarchy process (AHP). The approach is supported by empirical system knowledge, allowing us to test empirically the presumed decision-making and behavioral assumptions. The output is an array of sample agents with realistic (i.e. empirically quantified) decision-making and behavior. Results from a Swiss mineral construction material case study illustrate the information which can be derived by applying the proposed approach and demonstrate its practicability for context specific agent-based model development.

Christopher D. Hollander and Annie S. Wu
Journal of Artificial Societies and Social Simulation 14 (2) 6

Kyeywords: Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling
Abstract: Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.

Piergiuseppe Morone and Richard Taylor
Journal of Artificial Societies and Social Simulation 14 (2) 7

Kyeywords: Knowledge Diffusion, Innovation, Agent-Based Model, Validation
Abstract: In this brief note we reply to César García-Díaz and Diemo Urbig who reviewed our book on Knowledge Diffusion and Innovation (Edward Elgar Publishing: Cheltenham, 2010). We take this opportunity to reaffirm our personal view on several relevant issues, such as the need for a holistic view in economics, the adoption of a pragmatic heuristic approach when dealing with complex socio-economic systems, the relevance of a \'prototype model\' to setting a rigorous conceptual framework and the proposition of a novel way of looking at knowledge and innovation.

Ana Luisa Ballinas-Hernández, Angélica Muñoz-Meléndez and Alejandro Rangel-Huerta
Journal of Artificial Societies and Social Simulation 14 (3) 2

Kyeywords: Agent-Based Modeling, Pedestrian Crowd, Activity Measurement
Abstract: An agent-based model to simulate a pedestrian crowd in a corridor is presented. Pedestrian crowd models are valuable tools to gain insight into the behavior of human crowds in both, everyday and crisis situations. The main contribution of this work is the definition of a pedestrian crowd model by applying ideas from the field of the kinetic theory of living systems on the one hand, and ideas from the field of computational agents on the other hand. Such combination supported a quantitative characterization of the performance of our agents, a neglected issue in agent-based models, through well-known kinetic parameters. Fundamental diagrams of flow and activity are presented for both, groups of homogeneous pedestrians, and groups of heterogeneous pedestrians in terms of their willingness to reach their goals.

Wesley Wildman and Richard Sosis
Journal of Artificial Societies and Social Simulation 14 (3) 6

Kyeywords: Costly Signaling, Credibility Enhancing Displays, Cultural Transmission, Religion, Charismatic Leader, Agent-Based Model
Abstract: Costly signaling theory has been employed to explain the persistence of costly displays in a wide array of species, including humans. Henrich (2009) builds on earlier signaling models to develop a cultural evolutionary model of costly displays. Significantly, Henrich's model shows that there can be a stable equilibrium for an entire population committed to costly displays, persisting alongside a no-cost stable equilibrium for the entire population. Here we generalize Henrich's result to the more realistic situation of a population peppered with subgroups committed to high-cost beliefs and practices. The investigative tool is an agent-based model in which agents have cognitive capacities similar to those presupposed in Henrich's population-level cultural evolutionary model, and agents perform similar fitness calculations. Unlike in Henrich's model, which has no group differentiation within the population, our model agents use fitness calculations to determine whether to join or leave high-cost groups. According to our model, high-cost groups achieve long-term stability within a larger population under a wide range of circumstances, a finding that extends Henrich's result in a more realistic direction. The most important emergent pathway to costly group stability is the simultaneous presence of high charisma and consistency of the group leader and high cost of the group. These findings have strategic implications both for leading groups committed to costly beliefs and practices and for controlling their size and influence within wider cultural settings.

Levent Yilmaz
Journal of Artificial Societies and Social Simulation 14 (4) 2

Kyeywords: Agent-Based Model, Complexity, Innovation, Science Studies, Diversity
Abstract: Development of theoretically sound methods and strategies for informed science and innovation policy analysis is critically important to each nation's ability to benefit from R&D investments. Gaining deeper insight into complex social processes that influence the growth and formation of scientific fields and development over time of a diverse workforce requires a systemic and holistic view. A research agenda for the development of rigorous complex adaptive systems models is examined to facilitate the study of incentives, strategies, mobility, and stability of the science-based innovation ecosystem, while examining implications for the sustainability of a diverse science enterprise.

Marc Mölders, Robin D. Fink and Johannes Weyer
Journal of Artificial Societies and Social Simulation 14 (4) 6

Kyeywords: Systems Theory, Theory of Action and Decision Making, Academic Publication System, Science System, New Public Management, Agent-Based Modeling and Simulation
Abstract: The paper at hand applies agent-based modeling and simulations (ABMS) as a tool to reconstruct and to analyze how the science system works. A Luhmannian systems perspective is combined with a model of decision making of individual actors. Additionally, changes in the socio-political context of science, such as the introduction of „new public management\", are considered as factors affecting the functionality of the system as well as the decisions of individual scientists (e.g. where to publish their papers). Computer simulation helps to understand the complex interplay of developments at the macro (system) and the micro (actor) level.

Petra Ahrweiler
Journal of Artificial Societies and Social Simulation 14 (4) 8

Kyeywords: Simulating Science, Theory Interaction, Agent-Based Modelling, Theory Network
Abstract: This position paper presents a framework for modelling theory communities where theories interact as agents in a conceptual network. It starts with introducing the difficulties in integrating scientific theories by discussing some recent approaches, especially of structuralist theory of science. Theories might differ in reference, extension, scope, objectives, functions, architecture, language etc. To address these potential integration barriers, the paper employs a broad definition of "scientific theory", where a theory is a more or less complex description a describer puts forward in a context called science with the aim of making sense of the world. This definition opens up the agency dimension of theories: theories "do" something. They work on a - however ontologically interpreted - subject matter. They describe something, and most of them claim that their descriptions of this "something" are superior to those of others. For modelling purposes, the paper makes use of such description behaviour of scientific theories on two levels. The first is the level where theories describe the world in their terms. The second is a sub-case of the first: theories can of course describe the description behaviour of other theories concerning this world and compare with own description behaviour. From here, interaction and potential cooperation between theories could be potentially identified by each theory perspective individually. Generating inclusive theory communities and simulating their dynamics using an agent-based model means to implement theories as agents; to create an environment where the agents work as autonomous entities in a self-constituted universe of discourse; to observe what they do with this environment (they will try to apply their concepts, and instantiate their mechanisms of sense-making); and to let them mutually describe and analyse their behaviour and suggest areas for interaction. Some mechanisms for compatibility testing are discussed and the prototype of the model with preliminary applications is introduced.

Nicolas Payette
Journal of Artificial Societies and Social Simulation 14 (4) 9

Kyeywords: Agent-Based Models, Science Dynamics, Social Networks, Scientometrics, Evolutionary Computation
Abstract: The goal of this paper is to provide a sketch of what an agent-based model of the scientific process could be. It is argued that such a model should be constructed with normative claims in mind: i.e. that it should be useful for scientific policy making. In our tentative model, agents are researchers producing ideas that are points on an epistemic landscape. We are interested in our agents finding the best possible ideas. Our agents are interested in acquiring credit from their peers, which they can do by writing papers that are going to get cited by other scientists. They can also share their ideas with collaborators and students, which will help them eventually get cited. The model is designed to answer questions about the effect that different possible behaviors have on both the individual scientists and the scientific community as a whole.

Adam Arsenault, James Nolan, Richard Schoney and Donald Gilchrist
Journal of Artificial Societies and Social Simulation 15 (1) 11

Kyeywords: Multi-Agent Simulation, Auctions, Agriculture
Abstract: Land acquisition and ownership is an important part of modern agriculture in North America. Given the unique nature of farmland as a good, this paper develops a multi-agent simulation of farmland auction markets in a Canadian context. The model is used to generate data on land transactions between farm agents to determine if a particular auction design or type is better suited to farmland transactions. The simulation uses three different sealed-bid auctions, as well as an English auction. The auctions are compared on the basis of efficiency, stability, and perceived surplus. We find that the form of agent learning about land markets affects both sale price and the variance of sale prices in all of the studied auctions. The second-price-sealed-bid auction generates the most perceived surplus, most equitable share of surplus, and also decreases uncertainty in the common-value element of prices. But on a macroscopic level, it appears that auction choice does not influence market structure or evolution over time.

Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham
Journal of Artificial Societies and Social Simulation 15 (1) 2

Kyeywords: Agent-Based Modeling, Crime, Violence, Anthropology, Socio-Cultural Model, Police
Abstract: We perform analysis of data on crime and violence for 5,660 U.S. cities over the period of 2005-2009 and uncover the following trends: 1) The proportion of law enforcement officers required to maintain a steady low level of criminal activity increases with the size of the population of the city; 2) The number of criminal/violent events per 1,000 inhabitants of a city shows non-monotonic behavior with size of the population. We construct a dynamical model allowing for system-level, mechanistic understanding of these trends. In our model the level of rational behavior of individuals in the population is encoded into each citizen's perceived risk function. We find strong dependence on size of the population, which leads to partially irrational behavior on the part of citizens. The nature of violence changes from global outbursts of criminal/violent activity in small cities to spatio-temporally distributed, decentralized outbursts of activity in large cities, indicating that in order to maintain peace, bigger cities need larger ratio of law enforcement officers than smaller cities. We also observe existence of tipping points for communities of all sizes in the model: reducing the number of law enforcement officers below a critical level can rapidly increase the incidence of criminal/violent activity. Though surprising, these trends are in agreement with the data.

Julia Schindler
Journal of Artificial Societies and Social Simulation 15 (1) 4

Kyeywords: Agent-Based Model, Common-Pool Resources, Behavioral Game Theory, Nash Equilibria, Nash Extension NetLogo, Socio-Psychological Dispositions, Tragedy of the Commons
Abstract: In current research there is increasing evidence on why and how common-pool resources are successfully, i.e. sustainably, managed without the force of (often unsuccessful) top-level policy regulations. G. Hardin argued in 1968 in his Tragedy of the Commons (Hardin 1968) that commons must become depleted if users are free to choose extraction and resource use levels. In this study, we propose that socio-psychological factors can explain the success of resource use of a common without any top-level regulations. We exemplify this behavior by a spatio-temporally dynamic agent-based model of the Tragedy of the Commons using behavioral game theory and Nash equilibria calculation. By providing a spatio-temporal representation of Hardin's dilemma, the model could verify his argument in a temporal way if socio-psychological influence is disregarded, and indicated that under its influence the common can be sustained. We illustrated how dispositions such as cooperativeness, positive reciprocity, fairness towards others, and risk aversion broadly can support sustainable use, while negative reciprocity, fairness towards oneself, and conformity can inhibit it. Though, we also showed that it would be dangerous to generalize this kind of behavior, as changes in one of these dispositions can result in opposite system behavior, in dependence on the other dispositions. Due to this general capacity to account for such complex behavior that real common-pool system usually exhibit, and its ability to model intermediate equilibria, the proposed modelling approach, i.e. combining game-theory solution concepts with agent-based modelling, may be worth an assessment of its capacity to model empirical phenomena.

Hugues Bersini
Journal of Artificial Societies and Social Simulation 15 (1) 9

Kyeywords: Agent-Based Modeling, Object-Orientation Simulation, UML, Complex Systems
Abstract: Although the majority of researchers interested in ABM increasingly agree that the most natural way to program their models is to adopt OO practices, UML diagrams are still largely absent from their publications. In the last 15 years, the use of UML has risen constantly, to the point where UML has become the de facto standard for graphical visualization of software development. UML and its 13 diagrams has many universally accepted virtues. Most importantly, UML provides a level of abstraction higher than that offered by OO programming languages (Java, C++, Python, .Net ...). This abstraction layer encourages researchers to spend more time on modeling rather than on programming. This paper initially presents the four most common UML diagrams - class, sequence, state and activity diagrams (based on my personal experience, these are the most useful diagrams for ABM development). The most important features of these diagrams are discussed, and explanations based on conceptual pieces often found in ABM models are given of how best to use the diagrams. Subsequently, some very well known and classical ABM models such as the Schelling segregation model, the spatial evolutionary game, and a continuous double action free market are subjected to more detailed UML analysis.

Nina Schwarz, Daniel Kahlenberg, Dagmar Haase and Ralf Seppelt
Journal of Artificial Societies and Social Simulation 15 (2) 8

Kyeywords: Agent-Based Modelling, Urban, Land Use, Repast
Abstract: Modelling urban land use change can foster understanding of underlying processes and is increasingly realized using agent-based models (ABM) as they allow for explicitly coding land management decisions. However, urban land use change is the result of interactions of a variety of individuals as well as organisations. Thus, simulation models on urban land use need to include a diversity of agent types which in turn leads to complex interactions and coding processes. This paper presents the new ABMland tool which can help in this process: It is software for developing agent-based models for urban land use change within a spatially explicit and joint environment. ABMland allows for implementing agent-based models and parallel model development while simplifying the coding process. Six major agent types are already included as coupled models: residents, planners, infrastructure providers, businesses, developers and lobbyists. Their interactions are pre-defined and ensure valid communication during the simulation. The software is implemented in Java building upon Repast Simphony and other libraries.

Edgar Sioson
Journal of Artificial Societies and Social Simulation 15 (3) 6

Kyeywords: Simulation Testbed, Reputation Systems, Decentralized Currency, Modular Framework, Agent-Based Model
Abstract: We present Flora, a testbed that supports multidimensional fitness and resource modeling. Its main features are evaluation metrics related to population wellbeing, scalable representation of resource diversity, and composability of sociotechnical test scenarios through the TDI framework. We ran simulations to illustrate Flora's use in modeling the effects of using information infrastructures with different component systems. We analyzed the impact of hoarders in the absence of accounting systems, compared the performance of different decentralized currency systems in terms of accounting design features, and modeled the potential impact of reputation systems in deterring detrimental socioeconomic behavior. Among findings were the importance of having resource diversity as well as resources that each could target different fitness dimension needs; the inherent robustness of accounting systems that allow organizations to set budgets independently of centrally issued currency; and the greater effectiveness of buyer-screening compared to seller-screening as a means for influencing malevolent socioeconomic actors.

Jan C. Thiele, Winfried Kurth and Volker Grimm
Journal of Artificial Societies and Social Simulation 15 (3) 8

Kyeywords: Agent-Based Modelling, Design of Experiments, R, NetLogo, Model Analysis, Modelling Software
Abstract: A seamless integration of software platforms for implementing agent-based models and for analysing their output would facilitate comprehensive model analyses and thereby make agent-based modelling more useful. Here we report on recently developed tools for linking two widely used software platforms: NetLogo for implementing agent-based models, and R for the statistical analysis and design of experiments. Embedding R into NetLogo allows the use of advanced statistical analyses, specific statistical distributions, and advanced tools for visualization from within NetLogo programs. Embedding NetLogo into R makes it possible to design simulation experiments and all settings for analysing model output from the outset, using R, and then embed NetLogo programs in this virtual laboratory. Our linking tools have the potential to significantly advance research based on agent-based modelling.

Amit Patel, Andrew Crooks and Naoru Koizumi
Journal of Artificial Societies and Social Simulation 15 (4) 2

Kyeywords: Slums, Housing, Developing Countries, Urban Poor, Informal Settlements, Agent-Based Modeling
Abstract: Slums provide shelter for nearly one third of the world's urban population, most of them in the developing world. Slumulation represents an agent-based model which explores questions such as i) how slums come into existence, expand or disappear ii) where and when they emerge in a city and iii) which processes may improve housing conditions for urban poor. The model has three types of agents that influence emergence or sustenance of slums in a city: households, developers and politicians, each of them playing distinct roles. We model a multi-scale spatial environment in a stylized form that has housing units at the micro-scale and electoral wards consisting of multiple housing units at the macro-scale. Slums emerge as a result of human-environment interaction processes and inter-scale feedbacks within our model.

Timothy R. Gulden
Journal of Artificial Societies and Social Simulation 16 (2) 1

Kyeywords: Agent-Based Modeling, Agent-Based Computational Economics, International Economics, Comparative Advantage, Increasing Returns, NetLogo
Abstract: This paper makes use of an agent-based framework to extend traditional models of comparative advantage in international trade, illustrating several cases that make theoretical room for industrial policy and the regulation of trade. Using an agent based implementation of the Hecksher-Ohlin trade model; the paper confirms Samuelson's 2004 result demonstrating that the principle of comparative advantage does not ensure that technological progress in one country benefits its trading partners. It goes on to demonstrate that the presence of increasing returns leads to a situation with multiple equilibria, where free market trading policies can not be relied on to deliver an outcome which is efficient or equitable, with first movers in development enjoying permanent advantage over later developing nations. Finally, the paper examines the impact of relaxation of the Ricardian assumption of capital immobility on the principle of comparative advantage. It finds that the dynamics of factor trade are radically different from the dynamics of trade in goods and that factor mobility converts a regime of comparative advantage into a regime of absolute advantage, thus obviating the reassuring equity results that stem from comparative advantage.

Yushim Kim, Wei Zhong and Yongwan Chun
Journal of Artificial Societies and Social Simulation 16 (2) 8

Kyeywords: Fraud, Public Service Delivery, Deterrence, Agent-Based Modeling
Abstract: Public service delivery programs are not free from players' opportunistic behaviors, such as fraudulent benefit exchanges. The standard methods used to detect such misbehaviors are static, less effective in uncovering interactions between corrupt agents, and easy to evade because of corrupt agents' familiarity with detection procedures. Current fraud detection efforts do not match the dynamics and adaptive processes they are supposed to monitor and regulate. In this paper, an agent-based simulation model is built to gain insight on sanction choices to deter fraudulent activities in public service delivery programs. The simulation outputs demonstrate that sanctions with low certainty must be accompanied by prompt action in order to observe a reduction in fraudulent vendors. However, a similar level of reduction in fraudulent vendors may be achieved once a certain number of fraudulent vendors are sanctioned, even if the public agency's action is relatively delayed. These characteristics of sanctions provide strategic choices that public service delivery program managers can consider based on their priorities and resources.

Naoki Shiba
Journal of Artificial Societies and Social Simulation 16 (3) 11

Kyeywords: Social Simulation, Agent-Based Models (ABM), Theorem-Proof Approach, Mate-Search Problem, Two-Sided Matching, Job Matching
Abstract: This paper discusses an extended version of the matching problem which includes the mate search problem; this version is a generalization of a traditional optimization problem. The matching problem is extended to a form of the asymmetric two-sided matching problem. An agent-based simulation model is built and simulation results are presented. Todd and Miller (1999) simulated the two-sided matching problem in a symmetric setting. In his model, there are the same number of agents in both parties (groups), each of whom has his/her own mate value. Each agent in a party tries to find his/her mate in the other party, based on his/her candidate's mate value and his/her own aspiration level for his/her partner's mate value. Each agent learns his/her own mate value and adjusts his/her aspiration level through the trial period (adolescence). Todd and Miller (1999) tried several search rules and learning mechanisms that are symmetric for both parties. In the present paper, Todd and Miller's (1999) model is extended to an asymmetric setting where the two parties have different numbers of agents, and the search rule and the learning mechanism for the two parties differ. Through the simulation, the search rules and the learning mechanisms which were identified to be appropriate in a symmetric setting are revealed to be inappropriate in the asymmetric setting and the reason why this is so is discussed. Furthermore, some general facts are derived using a mathematical theorem-proof approach. Some of these facts are used to direct a revision of the model, and a revised simulation model is presented. An implication is obtained for practical situations in asymmetric matching setting. For example, in the job hunting case, if job applicants want to finish their job hunting successfully, they should be modest at the beginning of the hunt.

Samer Hassan, Javier Arroyo, José Manuel Galán, Luis Antunes and Juan Pavón
Journal of Artificial Societies and Social Simulation 16 (3) 13

Kyeywords: Forecasting, Guidelines, Prediction, Agent-Based Modelling, Modelling Process, Social Simulation
Abstract: This paper presents a set of guidelines, imported from the field of forecasting, that can help social simulation and, more specifically, agent-based modelling practitioners to improve the predictive performance and the robustness of their models. The presentation starts with a discussion on the current debate on prediction in social processes, followed by an overview of the recent experience and lessons learnt from the field of forecasting. This is the basis to define standard practices when developing agent-based models under the perspective of forecasting experimentation. In this context, the guidelines are structured in six categories that correspond to key issues that should be taken into account when building a predictor agent-based model: the modelling process, the data adequacy, the space of solutions, the expert involvement, the validation, and the dissemination and replication. The application of these guidelines is illustrated with an existing agent-based model. We conclude by tackling some intrinsic difficulties that agent-based modelling often faces when dealing with prediction models.

Fredrik Jansson
Journal of Artificial Societies and Social Simulation 16 (3) 2

Kyeywords: Agent-Based Modelling, Ethnocentrism, Prisoners'' Dilemma, Spatial Interactions, Validation
Abstract: Ethnocentrism refers to the tendency to behave differently towards strangers based only on whether they belong to the ingroup or the outgroup. It is a widespread phenomenon that can be triggered by arbitrary cues, but the origins of which is not clearly understood. In a recent simulation model by Hammond and Axelrod, an ingroup bias evolves in the prisoner's dilemma game. However, it will be argued here that the model does little to advance our understanding of ethnocentrism. The model assumes a spatial structure in which agents interact only with their immediate neighbourhood, populated mostly by clones, and the marker becomes an approximate cue of whether the partner is one. It will be shown that agents with an ingroup bias are successful compared to unconditional co-operators since they only exclude non-clones, but are outcompeted by less error-prone kin identifiers. Thus, the results of the simulations can be explained by a simple form of kin selection. These findings illustrate how spatial assumptions can alter a model to the extent that it no longer describes the phenomenon under study.

Scott Heckbert
Journal of Artificial Societies and Social Simulation 16 (4) 11

Kyeywords: Social-Ecological System, Archaeology, Cellular Automata, Network Model, Trade Network, Agent-Based Model
Abstract: This paper presents results from the MayaSim model, an integrated agent-based, cellular automata, and network model representing the ancient Maya social-ecological system. The model represents the relationship between population growth, agricultural production, soil degradation, climate variability, primary productivity, hydrology, ecosystem services, forest succession, and the stability of trade networks. Agents representing settlements develop and expand within a spatial landscape that changes under climate variation and responds to anthropogenic impacts. The model is able to reproduce spatial patterns and timelines somewhat analogous to that of the ancient Maya, although this proof-of-concept model requires refinement and further archaeological data for calibration. This paper aims to identify candidate features of a resilient versus vulnerable social-ecological system, and employs computer simulation to explore this topic, using the ancient Maya as an example. Complex systems modelling identifies how interconnected variables behave, considering fast-moving variables such as land cover change and trade connections, meso-speed variables such as demographics and climate variability, as well as slow-moving variables such as soil degradation.

Annie Waldherr and Nanda Wijermans
Journal of Artificial Societies and Social Simulation 16 (4) 13

Kyeywords: Social Simulation, Agent-Based Modelling, Rejective Criticism, Constructive Feedback, Communication, Peer Support
Abstract: When talking to fellow modellers about the feedback we get on our simulation models the conversation quickly shifts to anecdotes of rejective scepticism. Many of us experience that they get only few remarks, and especially only little helpful constructive feedback on their simulation models. In this forum paper, we give an overview and reflections on the most common criticisms experienced by ABM modellers. Our goal is to start a discussion on how to respond to criticism, and particularly rejective scepticism, in a way that makes it help to improve our models and consequently also increase acceptance and impact of our work. We proceed by identifying common criticism on agent-based modelling and social simulation methods and show where it shifts to rejection. In the second part, we reflect on the reasons for rejecting the agent-based approach, which we mainly locate in a lack of understanding on the one hand, and academic territorialism on the other hand. Finally, we also give our personal advice to socsim modellers of how to deal with both forms of rejective criticism.

Jeffrey Demarest, Sheree Pagsuyoin, Gerard Learmonth, Jonathan Mellor and Rebecca Dillingham
Journal of Artificial Societies and Social Simulation 16 (4) 3

Kyeywords: Agent-Based Model, Water Quality, Early Childhood Diarrhea, Stunting
Abstract: Diarrhea, the second leading cause of child morbidity and mortality, can have detrimental effects in the physical and cognitive development of children in developing countries. Health interventions (e.g., increased access to health services and safe water) designed to address this problem are difficult to implement in resource-limited settings. In this paper, we present a tool for understanding the complex relationship between water and public health in rural areas of a developing country. A spatial and temporal agent-based model (ABM) was developed to simulate the current water, sanitation, and health status in two villages in Limpopo Province, South Africa. The model was calibrated using empirical data and published sources. It was used to simulate the effects of poor water quality on the frequency of diarrheal episodes in children, and consequently on child development. Preliminary simulation results show that at the current total coliform levels in the water sources of the studied villages, children are expected to experience stunting by as much as -1.0 standard deviations from the World Health Organization height norms. With minor modifications, the calibrated ABM can be used to design and evaluate intervention strategies for improving child health in these villages. The model can also be applied to other regions worldwide that face the same environmental challenges and conditions as the studied villages.

Denton Cockburn, Stefani A. Crabtree, Ziad Kobti, Timothy A. Kohler and R. Kyle Bocinsky
Journal of Artificial Societies and Social Simulation 16 (4) 4

Kyeywords: Specialization, Agent-Based Modeling, Archaeology, Social Networks, Models of Social Influence, Barter
Abstract: We introduce a model for agent specialization in small-scale human societies that incorporates planning based on social influence and economic state. Agents allocate their time among available tasks based on exchange, demand, competition from other agents, family needs, and previous experiences. Agents exchange and request goods using barter, balanced reciprocal exchange, and generalized reciprocal exchange. We use a weight-based reinforcement model for the allocation of resources among tasks. The Village Ecodynamics Project (VEP) area acts as our case study, and the work reported here extends previous versions of the VEP agent-based model (“Village”). This model simulates settlement and subsistence practices in Pueblo societies of the central Mesa Verde region between A.D. 600 and 1300. In the base model on which we build here, agents represent households seeking to minimize their caloric costs for obtaining enough calories, protein, fuel, and water from a landscape which is always changing due to both exogenous factors (climate) and human resource use. Compared to the baseline condition of no specialization, specialization in conjunction with barter increases population wealth, global population size, and degree of aggregation. Differences between scenarios for specialization in which agents use only a weight-based model for time allocation among tasks, and one in which they also consider social influence, are more subtle. The networks generated by barter in the latter scenario exhibit higher clustering coefficients, suggesting that social influence allows a few agents to assume particularly influential roles in the global exchange network.

Julia Schindler
Journal of Artificial Societies and Social Simulation 16 (4) 6

Kyeywords: Agent-Based Modelling, Policy-Support-Tool, Critique, Justification, Land Use
Abstract: Although agent-based modeling is a strong modelling method in many aspects, its high degree of freedom in agent design can also be regarded as weakness. This freedom requires strong validation strategies during model design for empirical models, especially when models aim to be descriptive enough for policy support. Where theory or evidence does not support model design, assumptions are usually made. In these cases, arguments should be given for why the assumptions do not impair the validity of results. However, we believe that such justifications are sometimes weak in such kinds of models. In particular, we believe that the justification arguments are mostly plausible, but often not strong enough to overrule other plausible arguments leading to different designs. We believe that the reasons for this argumentative ambiguity are sometimes rooted in the type of underlying theory, framework, or validation strategy chosen. The point is that we suspect that simulation results can be sensitive to this ambiguity. To test this hypothesis, we selected a well-tried theory/framework/validation design strategy, and built alternative versions of a land-use change model in line with the underlying strategy. Results clearly show that levels and direction of simulated land-use change are significantly different among model versions.

Dongwon Lim, Hwansoo Lee, Hangjung Zo and Andrew Ciganek
Journal of Artificial Societies and Social Simulation 17 (1) 13

Kyeywords: Digital Divide, Opinion Dynamics, Agent-Based Model, Bounded Confidence Model
Abstract: The Internet is a public environment where people increasingly share information and exchange opinions. Not everyone can afford the costs of using the Internet, causing online opinions to be distorted in favor of certain social groups. This study examines the effect of the digital divide on opinion formation using the agent-based modeling (ABM) method. It extends the bounded confidence model to incorporate an online context and introduces accessibility and connectivity as new parameters. The simulation results indicate that connected agents are quicker to converge on a certain opinion than disconnected agents. Connected agents form an opinion cluster while disconnected agents are scattered over a broad range of opinions. The results also show that social harmony is harder to achieve as an individual’s ability to communicate their own opinion improves. Both connected and disconnected agents are more likely to become a minority with higher accessibility. Disconnected agents are 11 to 14 times more likely to become a minority than connected agents, which suggests that the digital divide may be associated with discrimination. This study provides additional insights for academia as well as practitioners on opinion formation in the digital divide. Research limitations are addressed along with suggested future research directions.

Ruth Dolado, Francesc S. Beltran and Vicenç Quera
Journal of Artificial Societies and Social Simulation 17 (1) 8

Kyeywords: Social Structure, Agent-Based Models (ABM), Biological Models
Abstract: Based on previous models (Hemelrijk 1998; Puga-González, Hildenbrant & Hemelrijk 2009), we have developed an agent-based model and software, called A-KinGDom, which allows us to simulate the emergence of the social structure in a group of non-human primates. The model includes dominance and affiliative interactions and incorporates two main innovations (preliminary dominance interactions and a kinship factor), which allow us to define four different attack and affiliative strategies. In accordance with these strategies, we compared the data obtained under four simulation conditions with the results obtained in a previous study (Dolado & Beltran 2012) involving empirical observations of a captive group of mangabeys (Cercocebus torquatus). The results show that the combination of the effect of kinship on affiliative interactions and the use of ambiguity-reducing attack provide results that are the most similar to the results of the biological model (i.e., a captive group of mangabeys) used in this study.

Emmanuel Labarbe and Daniel Thiel
Journal of Artificial Societies and Social Simulation 17 (1) 9

Kyeywords: Misperception, Interactions, Complementary Activities, Information Sharing, Agent-Based Model
Abstract: Agents who invest periodically in two complementary projects i and j try to minimize shortfall due to misperceptions concerning the interaction a between i and j. Previous studies have analytically solved such problems but they have been limited to two agents making one decision. We set out with the hypothesis of a large number of deciders sharing information with their nearest neighbors in order to improve the understanding of a. After each period of time, they exchange information on their real payoff values which enables them to choose the best neighbor expected perception of  in order to minimize their shortfall. To model this situation, we used an agent-based approach and we considered that the payoff information transmission was more or less efficient depending on the difficulty to assess the real values or when agents voluntarily transfer wrong data to their neighbors. Our simulation results showed that the total shortfall of the network: i.) declines in case of overestimation of a, ii.) depends on the initial agent opinions about a, iii.) evolves in two different curve morphologies, iv.) is influenced by information quality and can express a high heterogeneity of final opinions and v.) declines if the size of the neighborhood increases, which is a counterintuitive result.

Bo Xu, Renjing Liu and Weijiao Liu
Journal of Artificial Societies and Social Simulation 17 (2) 2

Kyeywords: Individual Bias, Agent-Based Modeling, Diversity, Exploration, Exploitation
Abstract: We introduce individual bias to the simulation model of exploration and exploitation and examine the joint effects of individual bias and other parameters, aiming to answer two questions: First, whether reducing individual bias can increase organizational objectivity? Second, whether measures, such as increasing organization size, can increase organizational objectivity in the presence of individual bias? Our results show that individual bias has both positive and negative effects, and reducing individual bias may be not beneficial when organization size is large or environment is turbulent. Diverse knowledge resulting from large organization size can help avoid the negative effects of individual bias when the bias is strong enough so that the individuals who are less limited by bias can be distinguished as the source of learning. Our results also suggest that increasing interpersonal learning, decreasing learning from the organization, task complexity, and environmental turbulence, and maintaining personnel turnover can improve organizational objectivity in the presence of individual bias.

Matthew Oremland and Reinhard Laubenbacher
Journal of Artificial Societies and Social Simulation 17 (2) 6

Kyeywords: Agent-Based Modeling, Optimization, Statistical Test, Genetic Algorithms, Reduction
Abstract: Questions concerning how one can influence an agent-based model in order to best achieve some specific goal are optimization problems. In many models, the number of possible control inputs is too large to be enumerated by computers; hence methods must be developed in order to find solutions that do not require a search of the entire solution space. Model reduction techniques are introduced and a statistical measure for model similarity is proposed. Heuristic methods can be effective in solving multi-objective optimization problems. A framework for model reduction and heuristic optimization is applied to two representative models, indicating its applicability to a wide range of agent-based models. Results from data analysis, model reduction, and algorithm performance are assessed.

Hai-hua Hu, Wen-tian Cui, Jun Lin and Yan-jun Qian
Journal of Artificial Societies and Social Simulation 17 (2) 7

Kyeywords: ICTs, Social Connectivity, Collective Action, Cultural Difference, Political Preference Distribution, Agent-Based Modeling
Abstract: In recent years, information and communication technologies (ICTs) have significantly affected the outcomes of large-scale collective actions. In addition, there is a well-known theoretical proposition that ICTs can fuel collective action by increasing individuals’ social connectivity that is closely related to recruitment capacity. This study aims to test this proposition by examining two moderating factors: the cultural context (i.e., online communication patterns) and the political context (i.e., the distribution of political preferences). By utilizing agent-based modeling, we find that ICT-improved connectivity not only scales down collective action if the distribution of political preference is insufficiently dispersed, but it also slows the diffusion speed if the overall propensity to participate is not strong. Moreover, the effects of ICT-improved connectivity on the scale and speed of collective action are similar under different cultural contexts. However, the theoretical implications suggest that ICTs are more effective in the collectivistic culture than in the individualistic culture.

Elio Marchione, Shane D Johnson and Alan Wilson
Journal of Artificial Societies and Social Simulation 17 (2) 9

Kyeywords: Maritime Piracy, Crime, Map Generation, Simulation, Agent-Based Modelling
Abstract: This paper presents a model to generate dynamic patterns of maritime piracy. Model details, outputs and calibration are illustrated. The model presented here is a tool to estimate the number of pirates and their area of action. The Gulf of Aden is considered as a case study, and data on pirate attacks, vessels routes and flows through the Gulf of Aden in the year 2010 are used to build the model. Agent-based modelling is employed to simulate pirate, vessel and naval forces behaviours.

Jan C. Thiele, Winfried Kurth and Volker Grimm
Journal of Artificial Societies and Social Simulation 17 (3) 11

Kyeywords: Parameter Fitting, Sensitivity Analysis, Model Calibration, Agent-Based Model, Inverse Modeling, NetLogo
Abstract: Agent-based models are increasingly used to address questions regarding real-world phenomena and mechanisms; therefore, the calibration of model parameters to certain data sets and patterns is often needed. Furthermore, sensitivity analysis is an important part of the development and analysis of any simulation model. By exploring the sensitivity of model output to changes in parameters, we learn about the relative importance of the various mechanisms represented in the model and how robust the model output is to parameter uncertainty. These insights foster the understanding of models and their use for theory development and applications. Both steps of the model development cycle require massive repetitions of simulation runs with varying parameter values. To facilitate parameter estimation and sensitivity analysis for agent-based modellers, we show how to use a suite of important established methods. Because NetLogo and R are widely used in agent-based modelling and for statistical analyses, we use a simple model implemented in NetLogo as an example, packages in R that implement the respective methods, and the RNetLogo package, which links R and NetLogo. We briefly introduce each method and provide references for further reading. We then list the packages in R that may be used for implementing the methods, provide short code examples demonstrating how the methods can be applied in R, and present and discuss the corresponding outputs. The Supplementary Material includes full, adaptable code samples for using the presented methods with R and NetLogo. Our overall aim is to make agent-based modellers aware of existing methods and tools for parameter estimation and sensitivity analysis and to provide accessible tools for using these methods. In this way, we hope to contribute to establishing an advanced culture of relating agent-based models to data and patterns observed in real systems and to foster rigorous and structured analyses of agent-based models.

Márcia Baptista, Carlos Roque Martinho, Francisco Lima, Pedro A. Santos and Helmut Prendinger
Journal of Artificial Societies and Social Simulation 17 (3) 7

Kyeywords: Agent-Based Modeling, Business Simulation, Consumer Behavior, Learning Processes
Abstract: Artificial society simulations may provide unprecedented insight into the intricate dynamics of economic markets. Such an insight may help solve the well-known black-box dilemma of business simulations, where designers prefer model concealment over model transparency. The core contribution of this work is an agent-based business simulation that models the marketplace as an artificial society of consumers. In the simulation, users assume the role of a store owner playing against an artificial intelligence competitor. The simulation can be accessed via a graphical user interface that animates the decision behavior of consumers. Consumers are modeled as agents with concrete beliefs, intentions and desires that act to maximize their utility and accomplish their purchase plans. We claim that unlike the classical equation-based approach, the visualization of market dynamics facilitated by our agent-based approach can provide important information to the user. We hypothesize that such information is key to understanding several economic concepts. To validate our hypothesis, we conducted an experiment with 30 users, where we compared the effects of the graphical animation of the market. Our results indicate that the agent-based approach has better learning outcomes both at the level of users' subjective self-assessment and at the level of objective performance metrics and knowledge acquisition tests. As a secondary contribution, we demonstrate by example how simple codification rules at the level of the utility functions of agents allow the emergence of diverse macroeconomic behavior of a two-product duopoly.

Mauricio Salgado, Elio Marchione and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 17 (4) 3

Kyeywords: Agent-Based Modelling, Differential School Effectiveness, Multilevel Modelling, Peer Effects, Teacher Expectation Bias
Abstract: During the last thirty years education researchers have developed models for judging the comparative performance of schools, in studies of what has become known as “differential school effectiveness”. A great deal of empirical research has been carried out to understand why differences between schools might emerge, with variable-based models being the preferred research tool. The use of more explanatory models such as agent-based models (ABM) has been limited. This paper describes an ABM that addresses this topic, using data from the London Educational Authority's Junior Project. To compare the results and performance with more traditional modelling techniques, the same data are also fitted to a multilevel model (MLM), one of the preferred variable-based models used in the field. The paper reports the results of both models and compares their performances in terms of predictive and explanatory power. Although the fitted MLM outperforms the proposed ABM, the latter still offers a reasonable fit and provides a causal mechanism to explain differences in the identified school performances that is absent in the MLM. Since MLM and ABM stress different aspects, rather than conflicting they are compatible methods.

Bill Kaye-Blake, Chris Schilling and Elizabeth Post
Journal of Artificial Societies and Social Simulation 17 (4) 5

Kyeywords: Agriculture, Interdisciplinary Research, Multi-Agent Simulation, Validation, Agent-Based Model
Abstract: This paper describes the process and results of validating a simulation model of agriculture for a region in New Zealand. Validation is treated as a process, in which simulation models are made useful for specific purposes by making them conform to observed historical trends and relationships. In this case, the model was calibrated to reproduce the year-by-year conversion to dairying from 1993 to 2012 in Southland, New Zealand. This was achieved by holding constant some elements of the simulation model, based on economic theory or data, and by running simulations on a range of values for two key parameters. The paper describes the model and process, and demonstrates that empirical validation is possible if approached pragmatically with a view to the intended use of the model. Important elements are: using stylised facts to limit the parameter space ex ante, establishing the range of model outcomes and focusing on the most likely parameter space, focusing the search for parameter values where there is the greatest uncertainty, and using historical data to calibrate models.

Jesús Rosales-Carreón and César García-Díaz
Journal of Artificial Societies and Social Simulation 18 (1) 10

Kyeywords: Agent-Based Model, Near-Zero Energy Buildings, Innovation Systems, Knowledge Elicitation, Systemigrams
Abstract: This paper examines the use of qualitative information in the construction of an agent- based model in order to study the growth of near-Zero Energy Buildings (nZEB’s) in the Netherlands through the innovation systems perspective. Drawing on desktop research and semi-structured interviews, this paper offers two major findings. First, we observed that the difficulties to the development of nZEB’s have been shaped by interaction and institutional barriers: the inner complexity of the building sector has decisively impacted on the growth of nZEB’s. Second, exploring interviewees’ understanding of the system via an agent-based model has brought fresh insights about the problem. Overall, this is a call for an interdisciplinary approach to understand the changes required for nZEB’s in their path for a successful adoption. Agent-based computational modelling, complemented with knowledge that was elicited from several stakeholders within the building sector, has helped to inspect the implication of common beliefs in the course of shaping possible futures toward a transition to nZEB’s.

Xin Gu, Karen Blackmore, David Cornforth and Keith Nesbitt
Journal of Artificial Societies and Social Simulation 18 (2) 10

Kyeywords: Academic Science, Lotka’s Law, Strategic Publication Model, Agent-Based Model
Abstract: The rapid changes occurring in the higher education domain are placing increasing pressure on the actors in this space to focus efforts on identifying and adopting strategies for success. One particular group of interest are academics or scientists, and the ways that these individuals, or collectives as institutional or discipline-based science systems, make decisions about how best to achieve success in their chosen field. The agent-based model and simulation that we present draws on the hypothetical “strategic publication model” proposed by Mölders, Fink and Weyer (2011), and extends this work by defining experimental settings to implement a prototype ABMS in NetLogo. While considerable work remains to fully resolve theoretical issues relating to the scope, calibration and validation of the model, this work goes some way toward resolving some of the details associated with defining appropriate experimental settings. Also presented are the results of four experiments that focus on exploring the emergent effects of the system that result from varying the strategic mix of actors in the system.

Ammar Malik, Andrew Crooks, Hilton Root and Melanie Swartz
Journal of Artificial Societies and Social Simulation 18 (2) 12

Kyeywords: Developing Countries, Urban, Segregation, Land Use, Transport, Agent-Based Modeling
Abstract: Scholars and urban planners have suggested that the key characteristic of leading world cities is that they attract the highest quality human talent through educational and professional opportunities. They offer enabling environments for productive human interactions and the growth of knowledge-based industries which drives economic growth through innovation. Both through hard and soft infrastructure, they offer physical connectivity which fosters human creativity and results in higher income levels. When combined with population density, socio-economic diversity and societal tolerance; the elevated interaction intensity diffuses creativity and improves productivity. In many developing country cities however, rapid urbanization is increasing sprawl and causing deteriorating in public services. We operationalize these insights by creating a stylized agent-based model where heterogeneous and independent decision-making agents interact under the following three scenarios: (1) improved urban transportation investments; (2) mixed land-use regulations; and (3) reduced residential segregation. We find that any combination of these scenarios results in greater population density and enables the diffusion of creativity, thus resulting in economic growth. However, the results demonstrate a clear trade-off between rapid economic progress and socioeconomic equity mainly due to the crowding out of low- and middle-income households from clusters of creativity.

Klaus G. Troitzsch
Journal of Artificial Societies and Social Simulation 18 (2) 14

Kyeywords: NetLogo, OWL, OWL-API, Ontology, Agent-Based Model
Abstract: J. Gary Polhills forum paper in this issue was an invitation to try the OWL extension on a model that was written more than a year ago. Download and installation was a matter of a few minutes, extending the old model with a few lines as shown in the paper was not a problem either, visualising the OWL output with different versions of Protégé was a little more difficult, but in the end showed interesting suggestions how to improve the original version of the NetLogo model.

Handi Chandra Putra, Haiyan Zhang and Clinton Andrews
Journal of Artificial Societies and Social Simulation 18 (2) 18

Kyeywords: Flooding Risk, Real Estate Market, Agent-Based Model
Abstract: Changing flood risks threaten the value of billions of dollars worth of coastal real estate as well as the viability of coastal communities. This paper presents an agent-based model to capture some of the main features of the housing market that emerges from interactions between autonomous buyers and sellers. We use this model to investigate the adaptive responses of real estate markets to changing patterns of flooding and alternative flood insurance policies. The model includes interactions among households and government through land use regulations, property tax collection and dissemination of flooding risk information. We use detailed data from a flood-prone coastal community in New Jersey, USA to calibrate our model.

Caroline Krejci and Benita Beamon
Journal of Artificial Societies and Social Simulation 18 (2) 19

Kyeywords: Food Supply Chains, Sustainable Agriculture, Coordination, Agent-Based Modeling, Farmer Decision Making, Multi-Agent Simulation
Abstract: To increase profitability, farmers often decide to form strategic partnerships with other farmers, pooling their resources and outputs for greater efficiency and scale. These coordination decisions can have far-reaching and complex implications for overall food supply chain structural emergence, which in turn impacts system outcomes and long-term sustainability. In this paper, we describe an agent-based model that explores the impacts of farmer coordination decisions on the development of food supply chain structure over time. This model focuses on one type of coordination mechanism implementation method, in which coordinated farmer groups produce a single crop type and combine their yields to achieve economies of scale. The farmer agents’ decisions to coordinate with one another depend on their evaluation of the tradeoff between their autonomy and the expected economic benefits of coordination. Each coordination decision is a bilateral process in which the terms of group reward sharing are negotiated. We capture the effects of farmers’ size, income, and autonomy premia, as well as volume-price relationships and group profit-sharing rules, on the rate of farmer coordination and the number and size of groups that form. Results indicate that under many conditions, coordination groups tend to consolidate over time, which suggests implications for overall supply chain structural resilience.

Hai-hua Hu, Jun Lin and Wen-tian Cui
Journal of Artificial Societies and Social Simulation 18 (2) 6

Kyeywords: Local Opinion Heterogeneity, Participation Likelihood, Participation Timing, Collective Behavior, Agent-Based Modeling, Threshold Model
Abstract: Local opinion heterogeneity (LOH) critically influences an individual’s choice of collective behaviors, such as voting and protesting. However, several empirical studies have presented different conclusions on how LOH affects such preference. In the current research, the effect of LOH is considered based on agent-based modeling and the threshold model introduced by Granovetter (1978). A series of simulation experiments and statistical analyses are conducted. Results show that LOH has an inverse U-shape effect on the likelihood of participation (whether an individual decides to participate). By contrast, the findings reveal that LOH has a monotonous effect on participation timing (when a participant makes the decision). Specifically, when LOH is high, an individual opts to participate early. These observations can be explained by the influence of LOH on the structure of social networks and by the moderating effect of the global distribution of opinions within the population.

Kazuya Yamamoto
Journal of Artificial Societies and Social Simulation 18 (2) 8

Kyeywords: Mobilization, Identity, Nation, Ethnicity, Culture, Agent-Based Modeling
Abstract: In modern states, mobilization policy has been used to awaken people to new ideas such as national identity, industrial capitalism, and civic society. However, it has long been debated whether mobilization in new countries or in countries under reconstruction creates an integrated identity or results in fragmentation of various ethnic groups. Although the idea that identity is not immutable but malleable is now widely accepted in political science, sociology, and other social sciences, the degree to which identity can be reconstructed once it has been mobilized remains unclear. This study employs an agent-based model to address questions regarding the relationship between governments’ mobilization and the integration of identity in countries. The analysis suggests that more rapid mobilization by governments stabilizes a greater ethnic cleavage. This result is found to be robust by changing parameters and by modifying the specifications of the model. In addition, the analysis presents two other implications. The first is that a spiraling fragmentation of identity might occur if governments fail to accommodate people. The second is that in an age of advanced communication, governments need more assimilative power than before in order to secure integration. The analysis suggests that future research about identity formation in countries should consider the rigidity as well as the flexibility of identity.

Sara McPhee-Knowles
Journal of Artificial Societies and Social Simulation 18 (2) 9

Kyeywords: Agent-Based Modeling, Search, Food Safety, Inspection, Policy
Abstract: The overall burden of foodborne illness is unknown, in part because of under-reporting and limited surveillance. Although the morbidity associated with foodborne illness is lower than ever, public risk perception and an increasingly complex food supply chain contribute to uncertainty in the food system. This paper presents an agent-based model of a simple food safety system involving consumers, inspectors and stores, and investigates the effect of three different inspection scenarios incorporating access to information. The increasing complexity of the food supply chain and agent-based modeling as an appropriate method for this line of investigation from a policy perspective are discussed.

Jonathan Ozik, Nicholson Collier, Todd Combs, Charles M. Macal and Michael North
Journal of Artificial Societies and Social Simulation 18 (3) 11

Kyeywords: Agent-Based Modeling, Statecharts, Agent-Based Social Simulation, Repast Simphony, Software Engineering Processes
Abstract: Agent states and transitions between states are important abstractions in agent-based social simulation (ABSS). Although it is common to develop ad hoc implementations of state-based and transition-based agent behaviors, “best practice” software engineering processes provide transparent and formally grounded design notations that translate directly into working implementations. Statecharts are a software engineering design methodology and an explicit visual and logical representation of the states of system components and the transitions between those states. Used in ABSS, they can clarify a model’s logic and allow for efficient software engineering of complex state-based models. In addition to agent state and behavioral logic representation, visual statecharts can also be useful for monitoring agent status during a simulation, quickly conveying the underlying dynamics of complex models as a simulation evolves over time. Visual approaches include drag-and-drop editing capabilities for constructing state-based models of agent behaviors and conditions for agent state transitions. Repast Simphony is a widely used, open source, and freely accessible agent-based modeling toolkit. While it is possible for Repast Simphony users to create their own implementations of state-based agent behaviors and even create dynamic agent state visualizations, the effort involved in doing so is usually prohibitive. The new statecharts framework in Repast Simphony, a subset of Harel’s statecharts, introduces software engineering practices through the use of statecharts that directly translate visual representations of agent states and behaviors into software implementations. By integrating an agent statecharts framework into Repast Simphony, we have made it easier for users at all levels to take advantage of this important modeling paradigm. Through the visual programming that statecharts afford, users can effectively create the software underlying agents and agent-based models. This paper describes the development and use of the free and open source Repast Simphony statecharts capability for developing ABSS models.

Andrew Collins, Mikel Petty, Daniele Vernon-Bido and Solomon Sherfey
Journal of Artificial Societies and Social Simulation 18 (3) 12

Kyeywords: Agent-Based Modeling and Simulation, Standards, Standardization, Standards Development Organization, ODD, Simulation Methods
Abstract: Standards are as old as civilization itself and they are vital to human development. Standards touch almost every part of our lives, from the water we drink to the language used to write this article. A sign of a good standard is one that we do not notice. Good standards exist and so do processes and organizations to create and maintain them. As agent-based modeling and simulation matures as a methodology, a discussion of standards applicable to it becomes increasingly important. Descriptive standards for agent-based models, such as the Overview, Design concepts, and Details protocol and agent-based extensions to the Unified Modeling Language, have already begun to emerge. Software tools for implementing such models, such as Netlogo and Repast Simphony, are increasingly well-known and have the potential to become de facto standards among the wider scientific community for agent-based simulation. Based on the findings of a series of workshops that brought together experts throughout the modeling and simulation community, we argue that agent-based modeling and simulation is no different from the other emerging technical subjects in the sense that standards, both existing and new, may be applicable to it, and that the community should both adopt existing standards that are relevant and exploit the already existing standards processes and organizations to develop new ones.

Hai-hua Hu, Jun Lin and Wen-tian Cui
Journal of Artificial Societies and Social Simulation 18 (3) 16

Kyeywords: Intervention Strategy, Diffusion of Collective Behavior, Social Network, Agent-Based Modeling
Abstract: This paper examines the intervention strategies for the diffusion of collective behavior, such as promoting innovation adoption and repressing a strike. An intervention strategy refers to controlling the behaviors of a small number of individuals in terms of their social or personal attributes, including connectivity (i.e., the number of social ties one holds), motivation (i.e., an individual’s intrinsic cost–benefit judgment on behavior change), and sensitivity (i.e., the degree to which one follows others). Extensive agent-based simulations demonstrate that the optimal strategy fundamentally depends on the goal and time of intervention. Moreover, the nature of the social network (determined by homophily type and level) moderates the effectiveness of a strategy. These results have substantial implications for the design and evaluation of intervention programs.

Luis R. Izquierdo, Doina Olaru, Segismundo S. Izquierdo, Sharon Purchase and Geoffrey N. Soutar
Journal of Artificial Societies and Social Simulation 18 (4) 1

Kyeywords: Fuzzy Logic, NetLogo, Social Simulation, Agent-Based Modelling, Mamdani Inference, IF-THEN Rule
Abstract: Fuzzy Logic is a framework particularly useful to formalise and deal with imprecise concepts and statements expressed in natural language. This paper has three related aims. First, it aims to provide a short introduction to the basics of Fuzzy Logic within the context of social simulation. Secondly, it presents a well-documented NetLogo extension that facilitates the use of Fuzzy Logic within NetLogo. Finally, by providing a concrete example, it shows how researchers can use the Fuzzy Logic extension to build agent-based models in which individual agents hold their own fuzzy concepts and use their own fuzzy rules, which may also change over time. We argue that Fuzzy Logic and the tools provided here can be useful in Social Simulation in different ways. For example, they can assist in the process of analysing the robustness of a certain social theory expressed in natural language to different specifications of the imprecise concepts that the theory may contain (such as e.g. “wealthy”, “poor” or “disadvantaged”). They can also facilitate the exploration of the effect that heterogeneity in concept interpretations may have in a society (i.e. the significance of the fact that different people may have different interpretations of the same concept). Thus, this paper and the tools included in it can make the endeavour of translating social theories into computer programs easier and more rigorous at the same time.

Albert Zimmermann, Anke Möhring, Gabriele Mack, Ali Ferjani and Stefan Mann
Journal of Artificial Societies and Social Simulation 18 (4) 11

Kyeywords: Agent-Based Model, Agriculture, FADN, Extrapolation
Abstract: Simulation results can be highly sensitive to the way agents are upscaled to a larger organizational and spatial level. This paper tests an ex-post validation method for forecasting models by using old base years and forecasting into recent years for which observed data is already available. Our case in point is a comparison between different upscaling methods in the agent-based agricultural sector model SWISSland. It is shown that individual-farm extrapolation factors strongly enhance alignment with the total population in the base year. However, they may cause inconsistencies in those agent-based models in which relations between the farms are an important part. Therefore, an adjustment of the sample by making almost no use of some farms whilst making highly disproportionate use of others turned out to be the most suitable method for the SWISSland model.

Fernando Fonseca, Rui António Rodrigues Ramos and Antônio Nélson Rodrigues da Silva
Journal of Artificial Societies and Social Simulation 18 (4) 13

Kyeywords: Industrial Estates, Agent-Based Models, Firms
Abstract: This article describes the main approaches adopted in a study focused on planning industrial estates on a subregional scale. The study was supported by an agent-based model, using firms as agents to assess the attractiveness of industrial estates. The simulation was made by the NetLogo toolkit and the environment represents a geographical space. Three scenarios and four hypotheses were used in the simulation to test the impact of different policies on the attractiveness of industrial estates. Policies were distinguished by the level of municipal coordination at which they were implemented and by the type of intervention. In the model, the attractiveness of industrial estates was based on the level of facilities, amenities, accessibility and on the price of land in each industrial estate. Firms are able to move and relocate whenever they find an attractive estate. The relocating firms were selected by their size, location and distance to an industrial estate. Results show that a coordinated policy among municipalities is the most efficient policy to promote advanced-qualified estates. In these scenarios, it was observed that more industrial estates became attractive, more firms were relocated and more vacant lots were occupied. Furthermore, the results also indicate that the promotion of widespread industrial estates with poor-quality infrastructures and amenities is an inefficient policy to attract firms.

Peter Revay
Journal of Artificial Societies and Social Simulation 18 (4) 14

Kyeywords: Social Norms, Agent-Based Modeling, Social Networks, Neighborhood Structure, Cooperation
Abstract: The different ways individuals socialize with others affect the conditions under which social norms are able to emerge. In this work an agent-based model of cooperation in a population of adaptive agents is presented. The model has the ability to implement a multitude of network topologies. The agents possess strategies represented by boldness and vengefulness values in the spirit of Axelrod's (1986) norms game. However, unlike in the norms game, the simulations abandon the evolutionary approach and only follow a single-generation of agents who are nevertheless able to adapt their strategies based on changes in their environment. The model is analyzed for potential emergence or collapse of norms under different network and neighborhood configurations as well as different vigilance levels in the agent population. In doing so the model is found able to exhibit interesting emergent behavior suggesting potential for norm establishment even without the use of so-called metanorms. Although the model shows that the success of the norm is dependent on the neighborhood size and the vigilance of the agent population, the likelihood of norm collapse is not monotonically related to decreases in vigilance.

Ju-Sung Lee, Tatiana Filatova, Arika Ligmann-Zielinska, Behrooz Hassani-Mahmooei, Forrest Stonedahl, Iris Lorscheid, Alexey Voinov, J. Gareth Polhill, Zhanli Sun and Dawn C. Parker
Journal of Artificial Societies and Social Simulation 18 (4) 4

Kyeywords: Agent-Based Modeling, Methodologies, Statistical Test, Sensitivity Analysis, Spatio-Temporal Heterogeneity, Visualization
Abstract: The proliferation of agent-based models (ABMs) in recent decades has motivated model practitioners to improve the transparency, replicability, and trust in results derived from ABMs. The complexity of ABMs has risen in stride with advances in computing power and resources, resulting in larger models with complex interactions and learning and whose outputs are often high-dimensional and require sophisticated analytical approaches. Similarly, the increasing use of data and dynamics in ABMs has further enhanced the complexity of their outputs. In this article, we offer an overview of the state-of-the-art approaches in analyzing and reporting ABM outputs highlighting challenges and outstanding issues. In particular, we examine issues surrounding variance stability (in connection with determination of appropriate number of runs and hypothesis testing), sensitivity analysis, spatio-temporal analysis, visualization, and effective communication of all these to non-technical audiences, such as various stakeholders.

Liang Mao
Journal of Artificial Societies and Social Simulation 18 (4) 6

Kyeywords: Self-Initiated Behavior, Infectious Diseases, Agent-Based Modeling, Relative Agreement Rules, Social Network
Abstract: Human self-initiated behavior against epidemics is recognized to have significant impacts on disease spread. A few epidemic models have incorporated self-initiated behavior, and most of them are based on a classic population-based approach, which assumes a homogeneous population and a perfect mixing pattern, thus failing to capture heterogeneity among individuals, such as their responsive behavior to diseases. This article proposes an agent-based model that combines epidemic simulation with a relative agreement model for individual self-initiated behavior. This model explicitly represents discrete individuals, their contact structure, and most importantly, their progressive decision making processes, thus characterizing individualized responses to disease risks. The model simulation and sensitivity analysis show the existence of critical points (threshold values) in the model parameter space to control influenza epidemic including minimum values for the initially positive population size, the communication rate, and the attitude uncertainty. These threshold effects shed insights on preventive strategy design to deal with the current circumstances that new vaccines are often insufficient to combat emerging communicable diseases.

Adam Rorabaugh
Journal of Artificial Societies and Social Simulation 18 (4) 8

Kyeywords: Cultural Transmission, Seasonal Mobility, Complex Foragers, Agent-Based Modeling, Social Networks, Cultural Drift
Abstract: Understanding the relationships between seasonal social networks and diversity in artifact styles, is crucial for examining the production and reproduction of knowledge among complex foraging societies such as those of the Pacific Northwest Coast. This agent-based model examines the impact of seasonal aggregation, dispersion, and learning opportunities on the richness and evenness of artifact styles under random social learning (unbiased transmission). The results of these simulations suggest that the relationship between learning opportunities and innovation rate has more impact on artifact style richness and evenness than seasonal social networks. Seasonal aggregation does appear to result in a higher amount of one-off rare variants, but this effect is not statistically significant. Overall, the restriction of learning opportunities appears more crucial in patterning cultural diversity among complex foragers than the potential impacts from individuals drawing on different seasonal social networks.

Nick Scott, Michael Livingston, Aaron Hart, James Wilson, David Moore and Paul Dietze
Journal of Artificial Societies and Social Simulation 19 (1) 10

Kyeywords: Agent-Based Model, NetLogo, Alcohol, Night-Time Economy, Heavy Drinking, SimDrink
Abstract: Aggression and other acute harms experienced in the night-time economy are topics of significant public health concern. Although policies to minimise these harms are frequently proposed, there is often little evidence available to support their effectiveness. In particular, indirect and displacement effects are rarely measured. This paper describes a proof-of-concept agent-based model ‘SimDrink’, built in NetLogo, which simulates a population of 18-25 year old heavy alcohol drinkers on a night out in Melbourne to provide a means for conducting policy experiments to inform policy decisions. The model includes demographic, setting and situational-behavioural heterogeneity and is able to capture any unintended consequences of policy changes. It consists of individuals and their friendship groups moving between private, public-commercial (e.g. nightclub) and public-niche (e.g. bar, pub) venues while tracking their alcohol consumption, spending and whether or not they experience consumption-related harms (i.e. drink too much), are involved in verbal violence, or have difficulty getting home. When compared to available literature, the model can reproduce current estimates for the prevalence of verbal violence experienced by this population on a single night out, and produce realistic values for the prevalence of consumption-related and transport-related harms. Outputs are robust to variations in underlying parameters. Further work with policy makers is required to identify several specific proposed harm reduction interventions that can be virtually implemented and compared. This will allow evidence based decisions to be made and will help to ensure any interventions have their intended effects.

Jonas Friege, Georg Holtz and Emile Chappin
Journal of Artificial Societies and Social Simulation 19 (1) 4

Kyeywords: Spatial Agent-Based Model, Decision-Making Process, Homeowners, Thermal Insulation, Situational Factors, Social Interaction
Abstract: Insulating existing buildings offers great potential for reducing greenhouse gas emissions and meeting Germany’s climate protection targets. Previous research suggests that, since homeowners’ decision-making processes are inadequately understood as yet, today’s incentives aiming at increasing insulation activity lead to unsatisfactory results. We developed an agent-based model to foster the understanding of homeowners’ decision-making processes regarding insulation and to explore how situational factors, such as the structural condition of houses and social interaction, influence their insulation activity. Simulation experiments allow us furthermore to study the influence of socio-spatial structures such as residential segregation and population density on the diffusion of renovation behavior among homeowners. Based on the insights gained, we derive recommendations for designing innovative policy instruments. We conclude that the success of particular policy instruments aiming at increasing homeowners’ insulation activity in a specific region depends on the socio-spatial structure at hand, and that reducing financial constraints only has a relatively low potential for increasing Germany’s insulation rate. Policy instruments should also target the fact that specific renovation occasions are used to undertake additional insulation activities, e.g. by incentivizing lenders and craftsmen to advise homeowners to have insulation installed.

Mazhar Sajjad, Karandeep Singh, Euihyun Paik and Chang-Won Ahn
Journal of Artificial Societies and Social Simulation 19 (1) 9

Kyeywords: Data-Driven, Agent-Based Model, Family Formation, Socioeeconomic Status
Abstract: In this paper, we propose a data-driven agent-based modeling approach that boosts the strength of agent-based models (ABM) in the dynamics of family formation. The proposed model analyzes the impact of socioeconomic factors on individual decisions about family formations. The key features of our model are the heterogeneous nature regarding agent’s age and socioeconomic factors: income and education. Based on these attributes, agents take decisions about acceptable partners and transition to family formation. One of our objectives is to fill the gap that exists between the methodologies of demography and agent-based social simulation. Making such a connection between these two approaches, this model attempts to incorporate empirical data into agent-based social simulation which enables us to analyze the transition of family formation effectively. Further, our simulated results depict the patterns of the hazards of family formation that are observed at micro-level dynamics and explains how marriage patterns change overtime. The proposed work gives a strong insight to strengthen the extent of demographic analysis through data-driven agent-based approach.

Francois Lamy, Brendan Quinn, Robyn Dwyer, Nicola Thomson, David Moore and Paul Dietze
Journal of Artificial Societies and Social Simulation 19 (2) 3

Kyeywords: Agent-Based Modelling, Methamphetamine Use, Drug-Related Harms, Treatment Access, Drug Career
Abstract: Methamphetamine use in Australia has recently attracted considerable attention due to increased human and social costs. Despite evidences indicating increasing methamphetamine-related harm and significant numbers of frequent and dependent users, methamphetamine treatment coverage remains low in Australia. This paper aims to investigate the complex interplay between methamphetamine use and treatment-related access by designing an agent-based model, using epidemiological data and expert-derived assumptions. This paper presents the architecture and core mechanisms of an agent-based model, TreatMethHarm, and details the results of model calibration performed by testing the key model parameters. At this stage of development, TreatMethHarm is able to produce proportions of methamphetamine users that replicate those produced by our epidemiological survey. However, this agent-based model still requires additional information and further tests before validation. TreatMethHarm provides a useful tool to elicit dialogue between researchers from different disciplines, integrate a variety of data and identify missing information.

Jiaqi Ge and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 19 (3) 11

Kyeywords: Agent-Based Model, Commuting, CO2 Emissions, Flexitime, Urban Concentration
Abstract: This paper develops an agent-based model of the daily commute in Aberdeen City and the surrounding area in Scotland, UK. We study the impact of flexitime work arrangements, urban concentration, a new bypass, and cycle lanes on commute time length, reliability and CO2 emissions, and analyse the diverse conflation of these factors and the different connections of them in order to detect their cumulative effects. Our results suggest that flexitime will reduce CO2 emissions from traffic. It also reduces mean commute time and makes commute time more reliable. We find that although higher urban concentration will make travel time less reliable, it will reduce CO2 emissions from commuting and cut commute time length. There might also be a trade-off between travel time length and reliability regarding urban concentration. We show that the new bypass will only reduce mean commute time by a small amount, while slightly increasing total CO2 emissions. Finally, we find that cyclists sharing roads with cars do not necessarily slow down the traffic on the whole. We conclude that infrastructural, social and urban issues should never be studied in isolation with each other, and that urban policies will have ramifications for both urban and surrounding ex-urban areas.

Stefan Holm, Renato Lemm, Oliver Thees and Lorenz M. Hilty
Journal of Artificial Societies and Social Simulation 19 (3) 3

Kyeywords: Agent-Based Modeling, Discrete Choice Experiments, Preference Elicitation, Decision Model, Market Simulation, Wood Market
Abstract: Agent-based modeling is a promising method to investigate market dynamics, as it allows modeling the behavior of all market participants individually. Integrating empirical data in the agents’ decision model can improve the validity of agent-based models (ABMs). We present an approach of using discrete choice experiments (DCEs) to enhance the empirical foundation of ABMs. The DCE method is based on random utility theory and therefore has the potential to enhance the ABM approach with a well-established economic theory. Our combined approach is applied to a case study of a roundwood market in Switzerland. We conducted DCEs with roundwood suppliers to quantitatively characterize the agents’ decision model. We evaluate our approach using a fitness measure and compare two DCE evaluation methods, latent class analysis and hierarchical Bayes. Additionally, we analyze the influence of the error term of the utility function on the simulation results and present a way to estimate its probability distribution.

Jan Drchal, Michal Čertický and Michal Jakob
Journal of Artificial Societies and Social Simulation 19 (3) 5

Kyeywords: Agent-Based Modelling, Activity Based Model, Transport, Validation, Methodology, Simulation
Abstract: Activity-based models are a specific type of agent-based models widely used in transport and urban planning to generate and study travel demand. They deal with agents that structure their behaviour in terms of daily activity schedules: sequences of activity instances (such as work, sleep or shopping) with assigned start times, durations and locations, and interconnected by trips with assigned transport modes and routes. Despite growing importance of activity-based models in transport modelling, there has been no work focusing specifically on statistical validation of such models so far. In this paper, we propose a six-step Validation Framework for Activity-based Models (VALFRAM) that exploits historical real-world data to quantify the model's validity in terms of a set of numeric metrics. The framework compares the temporal and spatial properties and the structure of modelled activity schedules against real-world origin-destination matrices and travel diaries. We demonstrate the usefulness of the framework on a set of six different activity-based transport models.

Sebastiaan Greeven, Oscar Kraan, Emile Chappin and Jan H. Kwakkel
Journal of Artificial Societies and Social Simulation 19 (3) 9

Kyeywords: Agent-Based Modeling, Scenario Discovery, Uncertainty, Climate Change Mitigation, Exploratory Modeling
Abstract: Developing model-based narratives of society’s response to climate change is challenged by two factors. First, society’s response to possible future climate change is subject to many uncertainties. Second, we argue that society’s mitigation action emerge out of the actions and interactions of the many actors in society. Together, these two factors imply that the overarching dynamics of society’s response to climate change are unpredictable. In contrast to conventional processes of developing scenarios, in this study the emergence of climate change mitigation action by society has been represented in an agent-based model with which we developed two narratives of the emergence of climate change mitigation action by applying exploratory modelling and analysis. The agent-based model represents a two-level game involving governments and citizens changing their emission behaviour in the face of climate change through mitigation action. Insights gained from the exploration on uncertainties pertaining to the system have been used to construct two internally consistent and plausible narratives on the pathways of the emergence of mitigation action, which, as we argue, are a reasonable summary of the uncertainty space. The first narrative highlights how and when strong mitigation action emerges while the second narrative highlights how and when weak mitigation action emerges. In contrast to a conventional scenario development process, these two scenarios have been discovered bottom up rather than being defined top down. They succinctly capture the possible outcomes of the emergence of climate change mitigation by society across a large range of uncertain factors. The narratives therefore help in conveying the consequences of the various uncertainties influencing the emergence of climate change mitigation action by society.

Agnieszka Kowalska-Styczeń and Katarzyna Sznajd-Weron
Journal of Artificial Societies and Social Simulation 19 (4) 10

Kyeywords: Agent-Based Model, Word of Mouth Marketing, Cellular Automata, Consumer Behavior
Abstract: We use a general cellular automata model to study the consumer decision-making process. Within this general model we use three different rules governing word-of-mouth communication (w-o-m), one majority rule and two unanimity rules, and ask the question if differences between these three w-o-m rules, introduced on the microscopic level, will manifest on the macroscopic level. We show that in the model with the majority rule the neighborhood plays a significant role in terms of the market shares whereas movement (interpreted as seeking for information in other sources) is almost negligible. Exactly the opposite phenomena are observed for models in which unanimity, instead of majority, is needed to convince agents. We also introduce a modification of the unanimity rule, based on the Latane theory of the social influence, and show that on the macroscopic level this modification is indistinguishable from the simple unanimity rule. We conclude the paper with a recommendation which rules are more appropriate to model particular marketing phenomena.

Bernardo Alves Furtado and Isaque Daniel Rocha Eberhardt
Journal of Artificial Societies and Social Simulation 19 (4) 12

Kyeywords: Modeling, Agent-Based Models, Public Finance, Taxes, Municipalities, Quality of Life
Abstract: This study simulates the evolution of artificial economies in order to understand the tax relevance of administrative boundaries in the quality of life of its citizens. The modeling involves the construction of a computational algorithm, which includes citizens, bounded into families; firms and governments; all of them interacting in markets for goods, labor and real estate. The real estate market allows families to move to dwellings with higher quality or lower price when the families capitalize property values. The goods market allows consumers to search on a flexible number of firms choosing by price and proximity. The labor market entails a matching process between firms (given its location) and candidates, according to their qualification. The government may be configured into one, four or seven distinct sub-national governments, which are all economically conurbated. The role of government is to collect taxes on the value added of firms in its territory and invest the taxes into higher levels of quality of life for residents. The results suggest that the configuration of administrative boundaries is relevant to the levels of quality of life arising from the reversal of taxes. The model with seven regions is more dynamic, but more unequal and heterogeneous across regions. The simulation with only one region is more homogeneously poor. The study seeks to contribute to a theoretical and methodological framework as well as to describe, operationalize and test computer models of public finance analysis, with explicitly spatial and dynamic emphasis. Several alternatives of expansion of the model for future research are described. Moreover, this study adds to the existing literature in the realm of simple microeconomic computational models, specifying structural relationships between local governments and firms, consumers and dwellings mediated by distance.

Mingxin Zhang, Alexander Verbraeck, Rongqing Meng, Bin Chen and Xiaogang Qiu
Journal of Artificial Societies and Social Simulation 19 (4) 3

Kyeywords: Spatial Contacts, Agent-Based Modeling, Artificial City
Abstract: Spatial contacts among human beings are considered as one of the influential factors during the transmission of contagious diseases, such as influenza and tuberculosis. Therefore, representing and understanding spatial contacts plays an important role in epidemic modeling research. However, most current research only considers regular spatial contacts such as contacts at home/school/office, or they assume static social networks for modeling social contacts and omit travel contacts in their epidemic models. This paper describes a way to model relatively complete spatial contacts in the context of a large-scale artificial city, which combines different data sources to construct an agent-based model of the city Beijing. In this model, agents have regular contacts when executing their daily activity patterns which is similar to other large-scale agent-based epidemic models. Besides, a microscopic public transportation component is included in the artificial city to model public travel contacts. Moreover, social contacts also emerge in this model due to the dynamic generation of social networks. To systematically examine the effect of the relatively complete spatial contacts have for epidemic prediction in the artificial city, a pandemic influenza disease progression model was implemented in this artificial city. The simulation results validated the model. In addition, the way to model spatial contacts in this paper shows potential not only for improving comprehension of disease spread dynamics, but also for use in other social systems, such as public transportation systems and city level evacuation planning.

Patryk Siedlecki, Janusz Szwabiński and Tomasz Weron
Journal of Artificial Societies and Social Simulation 19 (4) 9

Kyeywords: Opinion Dynamics, Social Influence, Conformity, Anticonformity, Bi-Polarization, Agent-Based Modelling
Abstract: Simmering debates leading to polarization are observed in many domains. Although empirical findings show a strong correlation between this phenomenon and modularity of a social network, still little is known about the actual mechanisms driving communities to conflicting opinions. In this paper, we used an agent-based model to check if the polarization may be induced by a competition between two types of social response: conformity and anticonformity. The proposed model builds on the q-voter model (Castellano et al, 2009b) and uses a double-clique topology in order to capture segmentation of a community. Our results indicate that the interplay between intra-clique conformity and inter-clique anticonformity may indeed lead to a bi-polarized state of the entire system. We have found a dynamic phase transition controlled by the fraction L of negative cross-links between cliques. In the regime of small values of L the system is able to reach the total positive consensus. If the values of L are large enough, anticonformity takes over and the system always ends up in a polarized stated. Putting it the other way around, the segmentation of the network is not a sufficient condition for the polarization to appear. A suitable level of antagonistic interactions between segments is required to arrive at a polarized steady state within our model.

Giorgio Fagiolo and Andrea Roventini
Journal of Artificial Societies and Social Simulation 20 (1) 1

Kyeywords: Economic Policy, Agent-Based Models, DSGE Models, Great Recession
Abstract: The Great Recession seems to be a natural experiment for economic analysis, in that it has shown the inadequacy of the predominant theoretical framework - the New Neoclassical Synthesis (NNS) - grounded on the DSGE model. In this paper, we present a critical discussion of the theoretical, empirical and political-economy pitfalls of the DSGE-based approach to policy analysis. We suggest that a more fruitful research avenue should escape the strong theoretical requirements of NNS models (e.g., equilibrium, rationality, representative agent, etc.) and consider the economy as a complex evolving system, i.e. as an ecology populated by heterogenous agents, whose far-from-equilibrium interactions continuously change the structure of the system. This is indeed the methodological core of agent-based computational economics (ACE), which is presented in this paper. We also discuss how ACE has been applied to policy analysis issues, and we provide a survey of macroeconomic policy applications (fiscal and monetary policy, bank regulation, labor market structural reforms and climate change interventions). Finally, we conclude by discussing the methodological status of ACE, as well as the problems it raises.

Julija Vasiljevska, Jochem Douw, Anna Mengolini and Igor Nikolic
Journal of Artificial Societies and Social Simulation 20 (1) 12

Kyeywords: Electricity Consumer, Agent-Based Modelling, Smart Metering, Consumer Values
Abstract: EU Regulation 2009/72/EC concerning common rules for internal market in electricity calls upon 80% of EU electricity consumers to be equipped with smart metering systems by 2020, provided that a positive economic assessment of all long-term costs and benefits to the market and the individual consumer is guaranteed. Understanding the impact that smart metering systems may have on the electricity stakeholders (consumers, distribution system operators, energy suppliers and the society at large) is important for faster and effective deployment of such systems and of the innovative services they offer. For this purpose, in this paper an agent-based model is developed, where the electricity consumer behaviour due to different smart metering policies is simulated. Consumers are modelled as household agents having dynamic preferences on types of electricity contracts offered by the supplier. Development of preferences depends on personal values, memory and attitudes, as well as the degree of interaction in a social network structure. We are interested in exploring possible diffusion rates of smart metering enabled services under different policy interventions and the impact of this technological diffusion on individual and societal performance indicators. In four simulation experiments and three intervention policies we observe the diffusion of energy services and individual and societal performance indicators (electricity savings, CO2 emissions savings, social welfare, consumers' comfort change), as well as consumers' satisfaction. From these results and based on expert validation, we conclude that providing the consumer with more options does not necessarily lead to higher consumer's satisfaction, or better societal performance. A good policy should be centred on effective ways to tackle consumers concerns.

Peter Duggins
Journal of Artificial Societies and Social Simulation 20 (1) 13

Kyeywords: Agent-Based Model, Opinion Dynamics, Social Networks, Conformity, Polarization, Extremism
Abstract: Agent-based models are versatile tools for studying how societal opinion change, including political polarization and cultural diffusion, emerges from individual behavior. This study expands agents' psychological realism using empirically-motivated rules governing interpersonal influence, commitment to previous beliefs, and conformity in social contexts. Computational experiments establish that these extensions produce three novel results: (a) sustained strong diversity of opinions within the population, (b) opinion subcultures, and (c) pluralistic ignorance. These phenomena arise from a combination of agents' intolerance, susceptibility and conformity, with extremist agents and social networks playing important roles. The distribution and dynamics of simulated opinions reproduce two empirical datasets on Americans' political opinions.

Sven Banisch and Eckehard Olbrich
Journal of Artificial Societies and Social Simulation 20 (1) 14

Kyeywords: Search Equilibrium Model, Agent-Based Models, Model Alignment, Heterogeneous Agents, Adaptive Agents, Temporal Difference Learning
Abstract: In this paper, we develop an agent-based version of the Diamond search equilibrium model - also called Coconut Model. In this model, agents are faced with production decisions that have to be evaluated based on their expectations about the future utility of the produced entity which in turn depends on the global production level via a trading mechanism. While the original dynamical systems formulation assumes an infinite number of homogeneously adapting agents obeying strong rationality conditions, the agent-based setting allows to discuss the effects of heterogeneous and adaptive expectations and enables the analysis of non-equilibrium trajectories. Starting from a baseline implementation that matches the asymptotic behavior of the original model, we show how agent heterogeneity can be accounted for in the aggregate dynamical equations. We then show that when agents adapt their strategies by a simple temporal difference learning scheme, the system converges to one of the fixed points of the original system. Systematic simulations reveal that this is the only stable equilibrium solution.

Steven F. Railsback, Daniel Ayllón, Uta Berger, Volker Grimm, Steven Lytinen, Colin Sheppard and Jan Thiele
Journal of Artificial Societies and Social Simulation 20 (1) 3

Kyeywords: Agent-Based Modeling, Computational Efficiency, Execution Speed, Individual-Based Modeling, NetLogo, Modeling Platforms
Abstract: NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. Our experience does not support that belief. NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models in which most agents do nothing on most time steps, discrete event simulation—facilitated by the time extension to NetLogo—can dramatically increase speed. NetLogo’s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.

Jan Dubbelboer, Igor Nikolic, Katie Jenkins and Jim Hall
Journal of Artificial Societies and Social Simulation 20 (1) 6

Kyeywords: Flooding, London, Flood Insurance, Flood Re, Agent-Based Modelling
Abstract: Flood risk emerges from the dynamic interaction between natural hazards and human vulnerability. Methods for the quantification of flood risk are well established, but tend to deal with human and economic vulnerability as being static or changing with an exogenously defined trend. In this paper we present an Agent-Based Model (ABM) developed to simulate the dynamical evolution of flood risk and vulnerability, and facilitate an investigation of insurance mechanism in London. The ABM has been developed to firstly allow an analysis of the vulnerability of homeowners to surface water flooding, which is one of the greatest short-term climate risks in the UK with estimated annual costs of £1.3bn to £2.2bn. These costs have been estimated to increase by 60-220% over the next 50 years due to climate change and urbanisation. Vulnerability is influenced by homeowner’s decisions to move house and/or install measures to protect their properties from flooding. In particular, the ABM focuses on the role of flood insurance, simulating the current public-private partnership between the government and insurers in the UK, and the forthcoming re-insurance scheme Flood Re, designed as a roadmap to support the future affordability and availability of flood insurance. The ABM includes interaction between homeowners, sellers and buyers, an insurer, a local government and a developer. Detailed GIS and qualitative data of the London borough of Camden are used to represent an area at high risk of surface water flooding. The ABM highlights how future development can exacerbate current levels of surface water flood risk in Camden. Investment in flood protection measures are shown to be beneficial for reducing surface water flood risk. The Flood Re scheme is shown to achieve its aim of securing affordable flood insurance premiums, however, is placed under increasing pressure in the future as the risk of surface water flooding continues to increase.

Paola D'Orazio and Gianfranco Giulioni
Journal of Artificial Societies and Social Simulation 20 (1) 9

Kyeywords: Agent-Based Model, Credit Supply, Consumer Debt, Precautionary Saving, Wealth Distribution, Labor Market Matching
Abstract: The paper develops an agent-based model populated by heterogeneous consumers, a productive sector and a banking sector. Taking a bottom up approach, the paper aims at providing a first tool to analyze households' borrowing dynamics in the different phases of the business cycle by relaxing some assumptions of mainstream consumption models and considering more realistic household borrowing behaviors. Although very simple, the model allows us to grasp the main implications of the interaction between consumers' wants (desired consumption), consumers' beliefs (their expectations about their future income), the behavior of the banking sector (rationing) and the behavior of the production sector (forecasting future demand). After presenting and discussing sensitivity analysis over a parameters' set, the paper reports results and the ex-post validation by comparing artificial and empirical distributions computed using the European Household Finance and Consumption Survey data set.

Friedrich Krebs
Journal of Artificial Societies and Social Simulation 20 (2) 10

Kyeywords: Green Electricity, Innovation Diffusion, Spatially Explicit Agent-Based Model, Empirical Calibration and Validation
Abstract: Spatially explicit agent-based models (ABM) of innovation diffusion have experienced growing attention over the last few years. The ABM presented in this paper investigates the adoption of green electricity tariffs by German households. The model represents empirically characterised household types as agent types which differ in their decision preferences regarding green electricity and other psychological properties. Agent populations are initialised based on spatially explicit socio demographic data describing the sociological lifestyles found in Germany. For model calibration and validation we use historical data on the German green electricity market including a rich dataset of spatially explicit customer data of one of the major providers of green electricity. In order to assess the similarity of the simulation results to historical observations we introduce two validation measures which capture different aspects of the green electricity diffusion. One measure is based on the residuals of spatially-aggregated time series of model indicators and the other measure considers a temporally aggregated but spatially disaggregated indicator of spatial spread. Finally, we demonstrate the descriptive richness of the model by investigating simulation outputs of the calibrated model in more detail. In particular, the results provide insights into the dynamics of the spatial and lifestyle heterogeneity “underneath” the diffusion curve of green electricity in Germany.

Jin Li and Renbin Xiao
Journal of Artificial Societies and Social Simulation 20 (2) 4

Kyeywords: Social Computing, Collective Behaviour, Agent-Based Model, Multidimensional Opinion Polarization, Social Judgement Theory, Multi-Agent System
Abstract: Opinion polarization in a group is an important phenomenon in collective behaviour that has become increasingly frequent during periods of social transition. In general, an opinion includes several dimensions in reality. By combining social judgement theory with the multi-agent model, we propose a multidimensional opinion evolution model for studying the dynamics of opinion polarization. Compared with previous models, a major contribution is that the opinion of the agent is extended to multiple dimensions, and the BA network is used as a model of real social networks. The results demonstrate that polarization is influenced by the average degree of the network, and the polarization process is affected by the parameters of the assimilation effect and contrast effect. Moreover, the evolution processes in different dimensions of opinion show correlation under certain specific conditions, and the discontinuous equilibrium phenomenon is observed in multidimensional opinion evolution in subsequent experiments.

Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 20 (2) 5

Kyeywords: Agent-Based Model, Epistemic Landscape, Research Funding, Fashions, Maps of Science
Abstract: The paper presents an agent-based model of an evolution of research interests in a scientific community. The research epistemic/funding landscape is divided into separate domains, which differ in impact on society and the perceived utility, which may determine the public willingness to fund. Scientific domains also differ in their potential for attention grabbing, crucial discoveries, which make them fashionable and also attract funding. The scientists may `follow' the availability of funds via a stylized grant based scheme. The model includes possible effects of the additional public relation and lobbying efforts, promoting certain disciplines at the cost of others. Results are based on two multi-parameter NetLogo models. The first uses an abstract, square lattice topology, and serves as a tool to understand the effects of the parameters describing the individual preferences. The second model, sharing the internal dynamics with the first one, is based on an actual research topics map and projects statistics, derived from the UK Research Council data for 2007--2016. Despite simplifications, results reproduce characteristics of the British research community surprisingly well.

Jule Thober, Birgit Müller, Jürgen Groeneveld and Volker Grimm
Journal of Artificial Societies and Social Simulation 20 (2) 8

Kyeywords: Agent-Based Modelling, Social-Ecological Modelling, Model Development, Model Testing, Model Analysis, Human Decision-Making
Abstract: Understanding social-ecological systems (SES) is crucial to supporting the sustainable management of resources. Agent-based modelling is a valuable tool to achieve this because it can represent the behaviour and interactions of organisms, human actors and institutions. Agent-based models (ABMs) have therefore already been widely used to study SES. However, ABMs of SES are by their very nature complex. They are therefore difficult to parameterize and analyse, which can limit their usefulness. It is time to critically reflect upon the current state-of-the-art to evaluate to what degree the potential of agent-based modelling for gaining general insights and supporting specific decision-making has already been utilized. We reviewed achievements and challenges by building upon developments in good modelling practice in the field of ecological modelling with its longer history. As a reference, we used the TRACE framework, which encompasses elements of model development, testing and analysis. We firstly reviewed achievements and challenges with regard to the elements of the TRACE framework addressed in reviews and method papers of social-ecological ABMs. Secondly, in a mini-review, we evaluated whether and to what degree the elements of the TRACE framework were addressed in publications on specific ABMs. We identified substantial gaps with regard to (1) communicating whether the models represented real systems well enough for their intended purpose and (2) analysing the models in a systematic and transparent way so that model output is not only observed but also understood. To fill these gaps, a joint effort of the modelling community is needed to foster the advancement and use of strategies such as participatory approaches, standard protocols for communication, sharing of source code, and tools and strategies for model design and analysis. Throughout our analyses, we provide specific recommendations and references for improving the state-of-the-art. We thereby hope to contribute to the establishment of a new advanced culture of agent-based modelling of SES that will allow us to better develop general theory and practical solutions.

Ulf Lotzmann and Martin Neumann
Journal of Artificial Societies and Social Simulation 20 (3) 13

Kyeywords: Interpretative Research Process, Agent-Based Modelling, Generative Social Science, Qualitative Data, Thick Description, Cultural Studies
Abstract: Agent-based social simulation is well-known for generative explanations. Following the theory of thick description we extend the generative paradigm to interpretative research in cultural studies. Using the example of qualitative data about criminal culture, the paper describes a research process that facilitates interpretative research by growing virtual cultures. Relying on qualitative data for the development of agent rules, the research process combines several steps: Qualitative data analysis following the Grounded Theory paradigm enables concept identification, resulting in the development of a conceptual model of the concept relations. The software tool CCD is used in conceptual modelling which assists semi-automatic transformation in a simulation model developed in the simulation platform DRAMS. Both tools preserve traceability to the empirical evidence throughout the research process. Traceability enables interpretation of simulations by generating a narrative storyline of the simulation. Thereby simulation enables a qualitative exploration of textual data. The whole process generates a thick description of the subject of study, in our example criminal culture. The simulation is characterized by a socio-cognitive coupling of agents’ reasoning on the state of the mind of other agents. This reveals a thick description of how participants make sense of the phenomenology of a situation from the perspective of their worldview.

Rainer Hegselmann
Journal of Artificial Societies and Social Simulation 20 (3) 15

Kyeywords: Schelling, Sakoda, Checkerboard Models, Tipping Models, Threshold Models, Agent-Based Modeling
Abstract: The Journal of Mathematical Sociology (JMS) started in 1971. The second issue contained its most cited article: Thomas C. Schelling, “Dynamic Models of Segregation”. In that article, Schelling presented a family of models, one of which became a canonical model. To date it is called the Schelling model—an eponym that affixes the inventor’s name to the invention, one of the highest forms of scientific recognition. In the very first issue of JMS, James Minoru Sakoda published an article entitled “The Checkerboard Model of Social Interaction”. Sakoda’s article more or less went unrecognized. Yet, a careful comparison demonstrates that in a certain sense the Schelling model is just an instance of Sakoda’s model. A precursor of that model was already part of Sakoda’s 1949 dissertation submitted to the University of California at Berkeley. A substantial amount of evidence indicates that in the 1970s Sakoda was well known and recognized as a computational social scientist, whereas Schelling was an unknown in the field. A generation later, the pattern of recognition almost completely reversed: Sakoda had become the unknown, while Schelling was the well-known inventor of the pioneering Schelling model. This article explains this puzzling pattern of recognition. Technical and social factors play a decisive role. Some contrafactual historical reflection suggests that the final result was not inevitable.

Norma L. Abrica-Jacinto, Evguenii Kurmyshev and Héctor A. Juárez
Journal of Artificial Societies and Social Simulation 20 (3) 3

Kyeywords: Opinion Dynamics, Ideological Affinity, Artificial Society, Relative Agreement, Agent-Based Model
Abstract: Ideology is one of the defining elements of opinion dynamics. In this paper, we report the effects of the nonlinear interaction of ideological affinity with the psychological reaction of agents in the frame of a multiparametric mathematical model of opinion dynamics. Computer simulations of artificial networked societies composed of agents of two psychological types were used for studying opinion formation; the simulations showed a phenomenon of preferential self-organization into groups of ideological affinity at the first stages of opinion evolution. The separation into ideologically akin opinion groups (ideological affinity) was more notable in societies composed mostly of concord agents; a larger opinion polarization was associated with the increase of agents’ initial average opinion uncertainty. We also observed a sensibility of opinion dynamics to the initial conditions of opinion and uncertainty, indicating potential instabilities. A measure of convergence was introduced to facilitate the analysis of transitions between the opinion states of networked societies and to detect social instability events. We found that the average of opinion uncertainty distribution reaches a steady state with values lower than the initial average value, sometimes nearing zero, which points at socially apathetic agents. Our analyses showed that the model can be utilized for further investigation on opinion dynamics and can be extended to other social phenomena.

Mark Kofi Boateng and Kwame Awuah-Offei
Journal of Artificial Societies and Social Simulation 20 (3) 4

Kyeywords: Mining Community, Agent-Based Modeling, Diffusion, Sensitivity Analysis, Mining
Abstract: The mining industry has difficulties predicting changes in the level of community acceptance of its projects over time. These changes are due to changes in the society and individual perceptions around these mines as a result of the mines’ environmental and social impacts. Agent-based modeling can be used to facilitate better understanding of how community acceptance changes with changing mine environmental impacts. This work investigates the sensitivity of an agent-based model (ABM) for predicting changes in community acceptance of a mining project due to information diffusion to key input parameters. Specifically, this study investigates the responsiveness of the ABM to average degree (total number of friends) of the social network, close neighbor ratio (a measure of homophily in the social network) and number of early adopters (“innovators”). A two-level full factorial experiment was used to investigate the sensitivity of the model to these parameters. The primary (main), secondary and tertiary effects of each parameter were estimated to assess the model’s sensitivity. The results show that the model is more responsive to close neighbor ratio and number of early adopters than average degree. Consequently, uncertainty surrounding the inferences drawn from simulation experiments using the agent-based model will be minimized by obtaining more reliable estimates of close neighbor ratio and number of early adopters. While it is possible to reliably estimate the level of early adopters from the literature, the degree of homophily (close neighbor ratio) has to be estimated from surveys that can be expensive and unreliable. Further, work is required to find economic ways to document relevant degrees of homophily in social networks in mining communities.

Sherif El-Tawil, Jieshi Fang, Benigno Aguirre and Eric Best
Journal of Artificial Societies and Social Simulation 20 (4) 10

Kyeywords: Egress, Agent-Based Model, Scalar Field Method, Social Relationships, the Station Building Fire
Abstract: Using agent-based modeling, this study presents the results of a computational study of social relationships among more than four hundreds evacuees in The Station Nightclub building in Rhode Island. The fire occurred on the night of February 20, 2003 and resulted in 100 fatalities. After summarizing and calibrating the computational method used, parametric studies are conducted to quantitatively investigate the influences of the presence of social relationships and familiarity of the building floor plan on the death and injury tolls. It is demonstrated that the proposed model has the ability to reasonably handle the complex social relationships and group behaviors present during egress. The simulations quantify how intimate social affiliations delay the overall egress process and show the extent by which lack of knowledge of a building floor plan limits exit choices and adversely affects the number of safe evacuations.

Matthew Oldham
Journal of Artificial Societies and Social Simulation 20 (4) 13

Kyeywords: Agent-Based Model, Artificial Stock Market, Networks, Portfolio Anlaysis
Abstract: The behavior of financial markets has frustrated, and continues to frustrate, investors and academics. By utilizing a complex systems framework, researchers have discovered new fields of investigations that have provided meaningful insight into the behavior of financial markets. The use of agent-based models (ABMs) and the inclusion of network science have played an important role in increasing the relevance of the complex systems to financial markets. The challenge of how best to combine these new techniques to produce meaningful results that can be accepted by the broader community remains an issue. By implementing an artificial stock market that utilizes an Ising model based agent-based model (ABM), this paper provides insights into the mechanisms that drive the returns in financial markets, including periods of elevated prices and excess volatility. A key finding is that the network topology investors form significantly affects the behavior of the market, with the exception being if investors have a bias to following their neighbors, at which point the topology becomes redundant. The model also investigates the impact of introducing multiple risky assets, something that has been absent in previous attempts. By successfully addressing these issues this paper helps to refine and shape a variety of further research tasks for the use of ABMs in uncovering the dynamics of financial markets.

Annalisa Stefanelli and Roman Seidl
Journal of Artificial Societies and Social Simulation 20 (4) 3

Kyeywords: Agent-Based Model, Arguments, Opinion Dynamics, Social Judgment
Abstract: The effect of social interactions on how opinions are developed and changed over time is crucial to public processes that involve citizens and their points of view. In this opinion dynamics exercise, we address the topic of nuclear waste repositories in Switzerland and suggest a more realistic investigation of public opinion using agent-based modeling in combination with empirical data and sociopsychological theory. Empirical data obtained from an online questionnaire (N = 841) is used for the initialization of the model, whose agents directly represent the participants. We use social judgment theory (SJT) to describe how opinions can be adapted during social interactions, including through mechanisms of contrast and assimilation. Furthermore, we focus on the definition of “opinion” itself, claiming that working with disaggregated opinions (i.e., arguments) can play a determining role if one aims to capture real-world mechanisms of opinion dynamics. Simulation results show different patterns for the three different argument categories used for this specific topic (i.e., risk, benefit, and process), suggesting a mutual influence between an individual’s initial knowledge and evaluations and an individual’s social dynamics and opinion changes. The importance of content-related and empirical information, as well as the theory and mechanisms used in the social simulation, are discussed.

Alena Schmidt, Magdalena Necpalova, Albert Zimmermann, Stefan Mann, Johan Six and Gabriele Mack
Journal of Artificial Societies and Social Simulation 20 (4) 7

Kyeywords: Sensitivity Analysis, Full Factorial Design, Nitrogen Input Tax, Nitrogen Surplus, Swiss Agriculture
Abstract: The reduction of nitrogen (N) surplus is an ongoing topic in the agri-environmental policies of many countries in the developed world. The introduction of N balance estimation in agricultural sector models is therefore pertinent and requires an interdisciplinary approach. We extended the agent based agricultural sector model SWISSland with an N farm gate balance estimation to pre-evaluate the introduction of a levy on N inputs, particularly a levy on fertilizer and imported concentrates, on N surplus reduction in the Swiss agriculture. The model was based on the Swiss farm accountancy data network (FADN) for 3,000 farms. The model’s ability to represent the N balance was assessed by conducting a structured full factorial sensitivity analysis. The sensitivity analysis revealed the possibility to switch to organic farming and the hectare based payments for ensuring food security as key parameters with the largest influence on the modelled N surplus. The evaluation of N input levy scenarios suggested that an introduction of a tax of 800% of N price will reduce the N surplus by 10% indicating a price elasticity of -0.03. The sensitivity analysis and the results from the levy scenarios suggest that indirect instruments, such as optimizing the direct payments scheme, should be considered rather than direct instruments for an effective N surpluses mitigation in Swiss agriculture.

Nathan Geffen and Stefan Scholz
Journal of Artificial Societies and Social Simulation 20 (4) 8

Kyeywords: HIV, Agent-Based Models, Sexually Transmitted Infections, Pair-Matching
Abstract: Microsimulations and agent-based models across various disciplines need to match agents into relationships. Some of these models need to repeatedly match different pairs of agents, for example microsimulations of sexually transmitted infection epidemics. We describe the requirements for pair-matching in these types of microsimulations, and present several pair-matching algorithms: Brute force (BFPM), Random (RPM), Random k (RKPM), Weighted shuffle (WSPM), Cluster shuffle (CSPM), and Distribution counting (DCPM). Using two microsimulations, we empirically compare the speeds, and pairing quality of these six algorithms. For models which execute pair-matching many thousands or millions of times, BFPM is not usually a practical option because it is slow. On the other hand, RPM is fast but chooses poor quality pairs. Nevertheless both algorithms are used, sometimes implicitly, in many models. Here we use them as yardsticks for upper and lower bounds for speed and quality. In these tests CSPM offers the best trade-off of speed and effectiveness. In general, CSPM is fast and produces stochastic, high quality pair-matches, which are often desirable characteristics for pair-matching in discrete time step microsimulations. Moreover it is a simple algorithm that can be easily adapted for the specific needs of a particular domain. However, for some models, RKPM or DCPM would be as fast as CSPM with matches of similar quality. We discuss the circumstances under which this would happen.

Sara Cuenda, Maximiliano Fernández, Javier Galeano and José A. Capitán
Journal of Artificial Societies and Social Simulation 21 (1) 2

Kyeywords: Interbank Markets, Agent-Based Modeling, Complex Networks
Abstract: The description of the empirical structure of interbank networks constitutes an important field of study since network theory can be used as a powerful tool to assess the resilience of financial systems and their robustness against failures. On the other hand, the development of reliable models of interbank market structure is relevant as they can be used to analyze systemic risk in the absence of transaction data or to test statistical hypotheses regarding network properties. Based on a detailed data-driven analysis of bank positions (assets and liabilities) taken from the Bankscope database, we here develop a minimal, stochastic, agent-based network model that accounts for the basic topology of interbank networks reported in the literature. The main assumption of our model is that loans between banks attempt to compensate assets and liabilities at each time step, and the model renders networks comparable with those observed in empirical studies. In particular, our model is able to qualitatively reproduce degree distributions, the distribution of the number of transactions, the distribution of exposures, the correlations with nearest-neighbor out-degree, and the clustering coefficient. As our simple model captures the overall structure of empirical networks, it can thus be used as a null model for testing hypotheses relative to other specific properties of interbank networks.

Ross Gore, Carlos Lemos, F. LeRon Shults and Wesley Wildman
Journal of Artificial Societies and Social Simulation 21 (1) 4

Kyeywords: Religion, Agent-Based Model, Data Based Modeling, Social Influence
Abstract: We employ existing data sets and agent-based modeling to forecast changes in religiosity and existential security among a collective of individuals over time. Existential security reflects the extent of economic, socioeconomic and human development provided by society. Our model includes agents in social networks interacting with one another based on the education level of the agents, the religious practices of the agents, and each agent's existential security within their natural and social environments. The data used to inform the values and relationships among these variables is based on rigorous statistical analysis of the International Social Survey Programme Religion Module (ISSP) and the Human Development Report (HDR). We conduct an evaluation that demonstrates, for the countries and time periods studied, that our model provides a more accurate forecast of changes in existential security and religiosity than two alternative approaches. The improved accuracy is largely due to the inclusion of social networks with educational homophily which alters the way in which religiosity and existential security change in the model. These dynamics grow societies where two individuals with the same initial religious practices (or belief In God, or supernatural beliefs) evolve differently based on the educational backgrounds of the individuals with which they surround themselves. Finally, we discuss the limitations of our model and provide direction for future work.

Abigail Sullivan, Li An and Abigail York
Journal of Artificial Societies and Social Simulation 21 (1) 5

Kyeywords: Agent-Based Model, Institutions, Invasive Pest, Collective Action
Abstract: There are multiple theories regarding how institutions change over time, but institutional change is often difficult to study and understand in practice. Agent-based modeling is known as a technique to explore emergent phenomena resulting from the micro level activities and interactions between heterogeneous agents and between agents and the environment. Such models allow researchers to investigate theories which may otherwise be difficult to examine. We present a theoretically driven agent-based model to explore two perspectives on institutional change, rational choice and cultural diffusion, in the context of invasive plant management in Chitwan, Nepal. The Chitwan region is grappling with the spread of the invasive mile-a-minute weed, Mikania micrantha (Mikania). We focus on understanding which perspective of institutional change better fits empirical survey data on Mikania management. We find that rational choice is an unlikely candidate for institutional change in Chitwan and that the social learning and imitation mechanism modeled in the cultural diffusion perspective better replicates empirical patterns. Additionally, the model reveals that the percentage of agents adopting the best practice removal method is not as influential in reducing Mikania as the initial amount of Mikania removed. This result indicates that it may be useful to conduct an empirical assessment varying the initial amount of Mikania removed to understand the management implications for successful removal of Mikania in Chitwan and elsewhere.

Carlo Proietti and Antonio Franco
Journal of Artificial Societies and Social Simulation 21 (1) 6

Kyeywords: Agent-Based Model, Social Norms, Game Theory
Abstract: Social norms play a fundamental role in holding groups together. The rationale behind most of them is to coordinate individual actions into a beneficial societal outcome. However, there are cases where pro-social behavior within a community seems, to the contrary, to cause inefficiencies and suboptimal collective outcomes. An explanation for this is that individuals in a society are of different types and their type determines the norm of fairness they adopt. Not all such norms are bound to be beneficial at the societal level. When individuals of different types meet a clash of norms can arise. This, in turn, can determine an advantage for the “wrong” type. We show this by a game-theoretic analysis in a very simple setting. To test this result - as well as its possible remedies - we also devise a specific simulation model. Our model is written in NETLOGO and is a first attempt to study our problem within an artificial environment that simulates the evolution of a society over time.

Ismo T. Koponen and Maija Nousiainen
Journal of Artificial Societies and Social Simulation 21 (2) 1

Kyeywords: Discourse Patterns, Task Focused Groups, Agent-Based Model, Competition, Cooperation
Abstract: Discourse patterns in a small group are assumed to form largely through the group's internal social dynamics when group members compete for floor in discourse. Here we approach such discourse pattern formation through the agent-based model (ABM). In the ABM introduced here the agents' interactions and participation in discussions are dependent on the agents' inherent potential activity to participate in discussion and on realised, externalised activity, discursivity. The discourse patterns are assumed to be outcomes of peer-to-peer comparison events, where agents competitively compare their activities and discursivities, and where activities also affect agents' cooperation in increasing the discursivity, i.e. floor for discourse. These two effects and their influence on discourse pattern formation are parameterised as comptetivity and cooperativity. The discourse patterns are here based on the agents' discursivity. The patterns in groups of four agents up to seven agents are characterised through triadic census (i.e. though counting triadic sub-patterns). The cases of low competitivity is shown to give rise to fully connected egalitarian, triadic patterns, which with increasing competitivity are transformed to strong dyadic patterns. An increase in cooperativity enhances the emergence of egalitarian triads and helps to maintain the formation of fully and partially connected triadic pattern also in cases of high competitivity. In larger groups of six and seven agents, isolation becomes common, in contrast to groups of four agents where isolation is relatively rare. These results are in concordance with known empirical findings of discourse and participation patterns in small groups.

Tamsin E. Lee
Journal of Artificial Societies and Social Simulation 21 (2) 10

Kyeywords: Agent-Based Modelling, Individual-Based Model, Protest Behaviour, Social Simulation, Netlogo
Abstract: More frequently protests are accompanied by an opposing group performing a counter protest. This phenomenon can increase tension such that police must try to keep the two groups separated. However, what is the best strategy for police? This paper uses a simple agent-based model to determine the best strategy for keeping the two groups separated. The 'thin blue line' varies in density (number of police), width and the keenness of police to approach protesters. Three different groups of protesters are modelled to mimic peaceful, average and volatile protests. In most cases, a few police forming a single-file 'thin blue line' separating the groups is very effective. However, when the protests are more volatile, it is more effective to have many police occupying a wide 'thin blue line', and police being keen to approach protesters. To the authors knowledge, this is the first paper to model protests and counter-protests.

Fabian Adelt, Johannes Weyer, Sebastian Hoffmann and Andreas Ihrig
Journal of Artificial Societies and Social Simulation 21 (2) 2

Kyeywords: Governance, Agent-Based Modelling, Complexity, Infrastructure Systems, Transport Network, Transport Mode Choice
Abstract: The current paper is positioned at the intersection of computer simulation, governance research, and research on infrastructure systems, such as transportation or energy. It proposes a simulation framework, “Simulation of the governance of complex systems” (SimCo), to study the governability of complex socio-technical systems experimentally by means of agent-based modelling (ABM). SimCo is rooted in a sociological macro-micro-macro model of a socio-technical system, taking into account the interplay of agents' choices (micro) and situational constraints (macro). The paper presents the conceptualization of SimCo, its elements and subsystems as well as their interactions. SimCo depicts the daily routines of users performing their tasks (e.g. going to work) by choosing among different technologies (e.g. modes of transportation), occasionally deciding to replace a worn-out technology. All components entail different dimensions that can be adjusted, thus allowing operators to purposefully intervene, for instance in the case of risk management (e.g. preventing congestion) or system transformation (e.g. towards sustainable mobility). Experiments with a basic scenario of an urban road transport system demonstrate the effects of different modes of governance (soft control, strong control and a combination of both), revealing that soft control may be the best strategy to govern a complex socio-technical system.

Marc Jaxa-Rozen and Jan H. Kwakkel
Journal of Artificial Societies and Social Simulation 21 (2) 4

Kyeywords: Agent-Based Modelling, NetLogo, Python
Abstract: Methods for testing and analyzing agent-based models have drawn increasing attention in the literature, in the context of efforts to establish standard frameworks for the development and documentation of models. This process can benefit from the use of established software environments for data analysis and visualization. For instance, the popular NetLogo agent-based modelling software can be interfaced with Mathematica and R, letting modellers use the advanced analysis capabilities available in these programming languages. To extend these capabilities to an additional user base, this paper presents the pyNetLogo connector, which allows NetLogo to be controlled from the Python general-purpose programming language. Given Python’s increasing popularity for scientific computing, this provides additional flexibility for modellers and analysts. PyNetLogo’s features are demonstrated by controlling one of NetLogo’s example models from an interactive Python environment, then performing a global sensitivity analysis with parallel processing.

Ahmed Laatabi, Nicolas Marilleau, Tri Nguyen-Huu, Hassan Hbid and Mohamed Ait Babram
Journal of Artificial Societies and Social Simulation 21 (2) 9

Kyeywords: Empirical Agent-Based Models, ODD Protocol, ODD+2D, Mapping, Data Analysis, Social Simulation
Abstract: The quantity of data and processes used in modeling projects has been dramatically increasing in recent years due to the progress in computation capability and to the popularity of new approaches such as open data. Modelers face an increasing difficulty in analyzing and modeling complex systems that consist of many heterogeneous entities. Adapting existing models is relevant to avoid dealing with the complexity of writing and studying a new model from scratch. ODD (Overview, Design concepts, Details) protocol has emerged as a solution to document Agent-Based Models (ABMs). It appears to be a convenient solution to address significant problems such as comprehension, replication, and dissemination. However, it lacks a standard that formalizes the use of data in empirical models. This paper tackles this issue by proposing a set of rules that outline the use of empirical data inside an ABM. We call this new protocol ODD+2D (ODD+Decision + Data). ODD+2D integrates a mapping diagram called DAMap (Data to Agent Mapping). This mapping model formalizes how data are processed and mapped to agent-based models. In this paper, we focus on the architecture of ODD+2D, and we illustrate it with a residential mobility model in Marrakesh.

Kamwoo Lee, Sinan Ulkuatam, Peter Beling and William Scherer
Journal of Artificial Societies and Social Simulation 21 (3) 5

Kyeywords: Cryptocurrency, Bitcoin, Inverse Reinforcement Learning, Agent-Based Modeling
Abstract: In this paper, we present a novel method to predict Bitcoin price movement utilizing inverse reinforcement learning (IRL) and agent-based modeling (ABM). Our approach consists of predicting the price through reproducing synthetic yet realistic behaviors of rational agents in a simulated market, instead of estimating relationships between the price and price-related factors. IRL provides a systematic way to find the behavioral rules of each agent from Blockchain data by framing the trading behavior estimation as a problem of recovering motivations from observed behavior and generating rules consistent with these motivations. Once the rules are recovered, an agent-based model creates hypothetical interactions between the recovered behavioral rules, discovering equilibrium prices as emergent features through matching the supply and demand of Bitcoin. One distinct aspect of our approach with ABM is that while conventional approaches manually design individual rules, our agents’ rules are channeled from IRL. Our experimental results show that the proposed method can predict short-term market price while outlining overall market trend.

Marcin Czupryna, Paweł Oleksy, Piotr Przybek and Bogumił Kamiński
Journal of Artificial Societies and Social Simulation 21 (3) 6

Kyeywords: Agent-Based Modelling, Market Development, Behavioural Factors, Viticulture, Wine
Abstract: In this paper, we apply an agent-based approach to explain both the final state and the dynamics of the development process of the wine sector in the Małopolska region in Poland. This sector has been affected by various environmental, institutional, behavioural and social factors and has undergone evolutionary changes in recent years. The econometric analysis of empirical data of vineyards in this region provides insights into the degree of influence of various factors under consideration on the aggregate number of vineyards in sub-regions. However, this does no explain the dynamics of the local formation of new vineyards or the underlying latent attitudes of vineyard owners. To overcome this limitation, we developed an agent-based model with heterogeneous agents (regular farms as well as large and small vineyards), which allowed us to identify a two-stage development scenario: i) community building and ii) vineyard creation. Our findings are of two types. Firstly, we showed a case where the agent-based model has good predictive power, in situations where the econometric model fails. Secondly, estimation of the agent-based model parameters and sensitivity analysis revealed crucial factors that have driven development of viticulture in the Małopolska region. In particular, we find that the crucial element underlying the good predictive power of the model is that it enables us to capture the fact that wine enthusiasts initially concentrate in sub-regions with more benign environmental conditions. Next, when one of them eventually established a vineyard, agents in the community had a lowered barrier to entry via the possibility of practical knowledge exchange, joint marketing efforts or vineyard maintenance resource sharing. This is in line with current evidence, which shows strong clustering effects, namely, a relatively large number of vineyards originate at relatively similar times and locations.

John Bullinaria
Journal of Artificial Societies and Social Simulation 21 (3) 7

Kyeywords: Agent-Based Models, Gender Inequalities, Career Preferences, Social Learning, Evolution
Abstract: An agent-based simulation framework is presented that provides a principled approach for investigating gender inequalities in professional hierarchies such as universities or businesses. Populations of artificial agents compete for promotion in their chosen professions, leading to emergent distributions that can be matched to real-life scenarios, and allowing the influence of socially or genetically acquired career preferences to be explored. The aim is that such models will enable better understanding of how imbalances emerge and evolve, facilitate the identification of specific signals that can indicate the presence or absence of discrimination, and provide a tool for determining how and when particular intervention strategies may be appropriate for rectifying any inequalities. Results generated from a representative series of abstract case studies involving innate or culturally-acquired gender-based ability differences, gender-based discrimination, and various forms of gender-specific career preferences, demonstrate the power of the approach. These simulations will hopefully inspire and facilitate better approaches for dealing with these issues in real life.

Claudius Graebner
Journal of Artificial Societies and Social Simulation 21 (3) 8

Kyeywords: Agent-Based Modelling, Epistemology, Models, Validation, Verification
Abstract: Agent-based simulations have become increasingly prominent in various disciplines. This trend is positive, but it comes with challenges: while there are more and more standards for design, verification, validation, and presentation of the models, the various meta-theoretical strategies of how the models should be related to reality often remain implicit. Differences in the epistemological foundations of models make it however, difficult to relate distinct models to each other and to ensure a cumulative expansion of knowledge. Concepts and the analytic language developed by philosophers of science can help to overcome these obstacles. This paper introduces some of these concepts to the modelling community. It also presents an epistemological framework that helps to clarify how one wishes to generate knowledge about reality by the means of one's model and that helps to relate models to each other. Since the interpretation of a model is strongly connected to the activities of model verification and validation, these two activities will be embedded into the framework and their respective epistemological roles will be clarified. The resulting meta-theoretical framework aligns well with recently proposed frameworks for model presentation and evaluation.

Oliver Reinhardt, Jason Hilton, Tom Warnke, Jakub Bijak and Adelinde M. Uhrmacher
Journal of Artificial Societies and Social Simulation 21 (3) 9

Kyeywords: Agent-Based Modeling, Demography, Simulation Experimentation, Meta-Modeling
Abstract: In the last decade, the uptake of agent-based modeling in demography and other population sciences has been slowly increasing. Still, in such areas, where traditional data-driven, statistical approaches prevail, the hypothesis-driven design of agent-based models leads to questioning the validity of these models. Consequently, suitable means to increase the confidence into models and simulation results are required. To that end, explicit, replicable simulation experiments play a central role in model design and validation. However, the analysis of more complex models implies executing various experiments, each of which combines various methods. To streamline these experimentation processes a flexible computational simulation environment is necessary. With a new binding between SESSL -- an internal domain-specific language for simulation experiments -- and ML3 -- a simulator for linked lives designed specifically for agent-based demographic models -- we cater for these objectives and provide a powerful simulation tool. The proposed approach can serve as a foundation for current efforts of employing advanced and statistical model analysis of agent-based demographic models, as part of a wider process of iterative model building. We demonstrate its potential in specifying and executing different experiments with a simple model of return migration and a more complex model of social care.

Loïs Vanhée and Frank Dignum
Journal of Artificial Societies and Social Simulation 21 (4) 11

Kyeywords: Cultures, Social Simulations, Agent-Based Modelling
Abstract: This paper presents a simulation model and derived from it a theory to explain how known cultural influences on individual decisions lead to collective phenomena. This simulation models the evolution of a business organization, replicating key micro-level cultural influences on individual decisions (such as allocating and accepting tasks) and subsequent macro-level collective cultural phenomena (such as robustness and sensitivity to environmental complexity). As a result, we derived a theory on how to relate the influence of culture from individual decisions to collective outcomes, based on this simulation. We also point out that cultures appear to be related to specific sets of abstract, coherent and recurrent interaction patterns between individuals.

Morgane Dumont, Johan Barthelemy, Nam Huynh and Timoteo Carletti
Journal of Artificial Societies and Social Simulation 21 (4) 3

Kyeywords: Microsimulation, Agent-Based Modelling, Ordering of Models, Population Evolution, Robustness
Abstract: Agent based modelling is nowadays widely used in transport and the social science. Forecasting population evolution and analysing the impact of hypothetical policies are often the main goal of these developments. Such models are based on sub-models defining the interactions of agents either with other agents or with their environment. Sometimes, several models represent phenomena arising at the same time in the real life. Hence, the question of the order in which these sub-models need to be applied is very relevant for simulation outcomes. This paper aims to analyse and quantify the impact of the change in the order of sub-models on an evolving population modelled using TransMob. This software simulates the evolution of the population of a metropolitan area in South East of Sydney (Australia). It includes five principal models: ageing, death, birth, marriage and divorce. Each possible order implies slightly different results mainly driven by how agents' ageing is defined with respect to death. Furthermore, we present a calendar-based approach for the ordering that decreases the variability of final populations. Finally, guidelines are provided proposing general advices and recommendations for researchers designing discrete time agent-based models.

Atesmachew Hailegiorgis, Andrew Crooks and Claudio Cioffi-Revilla
Journal of Artificial Societies and Social Simulation 21 (4) 4

Kyeywords: Climate Change Adaptation, Agent-Based Modeling, Socio-Cognitive Behavior
Abstract: Future climate change is expected to have greater impacts on societies whose livelihoods rely on subsistence agricultural systems. Adaptation is essential for mitigating adverse effects of climate change, to sustain rural livelihoods and ensure future food security. We present an agent-based model, called OMOLAND-CA, which explores the impact of climate change on the adaptive capacity of rural communities in the South Omo Zone of Ethiopia. The purpose of the model is to answer research questions on the resilience and adaptive capacity of rural households with respect to variations in climate, socioeconomic factors, and land-use at the local level. Our model explicitly represents the socio-cognitive behavior of rural households toward climate change and resource flows that prompt agents to diversify their production strategy under different climatic conditions. Results from the model show that successive episodes of extreme events (e.g., droughts) affect the adaptive capacity of households, causing them to migrate from the region. Nonetheless, rural communities in the South Omo Zone, and in the model, manage to endure in spite of such harsh climatic change conditions.

F. LeRon Shults, Ross Gore, Wesley Wildman, Christopher Lynch, Justin E. Lane and Monica Toft
Journal of Artificial Societies and Social Simulation 21 (4) 7

Kyeywords: Agent-Based Model, Religious Violence, Identity Fusion, Social Identity, Terror Management, Xenophobia
Abstract: We propose a generative agent-based model of the emergence and escalation of xenophobic anxiety in which individuals from two different religious groups encounter various hazards within an artificial society. The architecture of the model is informed by several empirically validated theories about the role of religion in intergroup conflict. Our results identify some of the conditions and mechanisms that engender the intensification of anxiety within and between religious groups. We define mutually escalating xenophobic anxiety as the increase of the average level of anxiety of the agents in both groups over time. Trace validation techniques show that the most common conditions under which longer periods of mutually escalating xenophobic anxiety occur are those in which the difference in the size of the groups is not too large and the agents experience social and contagion hazards at a level of intensity that meets or exceeds their thresholds for those hazards. Under these conditions agents will encounter out-group members more regularly, and perceive them as threats, generating mutually escalating xenophobic anxiety. The model’s capacity to grow the macro-level emergence of this phenomenon from micro-level agent behaviors and interactions provides the foundation for future work in this domain.

Lukasz Kowalski
Journal of Artificial Societies and Social Simulation 22 (1) 1

Kyeywords: Agent-Based Model, Spatial Interaction Model, Hybrid Model, Firm Location, Time-Space
Abstract: Aggregated models, such as spatial interaction (SIM) models are widely used in location analysis. Despite their popularity, there are certain limitations to their use. In particular, the method struggles to account for the passing-by population and multi-purpose trips of retail clients, temporal changes in accessibility and some bottom-up processes potentially important for services. Agent-based modelling (ABM) is a promising technique that attempts to address all these problems. However, it still lacks examples of real-world applications. This article aims to provide an example of how hybrid ABM (H-ABM) can be built on a SIM foundation, by incorporating most of its ideas, such as distance-decay function, facility attractiveness parameters and demand elasticity. The author aligns the two models as close as possible and compares their input data, calibration procedures and results. In the final analysis, the hybrid agent-based model proved to be more realistic because it incorporated the time-space variability of supply (i.e., limited numbers of available places in swimming pools), demand (the popularity of certain entry hours) and transport (traffic jams during rush hours). The spatial interaction model was much faster to execute and turned out to be more convenient for more straightforward applications, which do not require detailed data concerning individuals.

Francesco Pasimeni
Journal of Artificial Societies and Social Simulation 22 (1) 11

Kyeywords: Micro-Grids, Agent-Based Model, Innovation Diffusion, Energy Transition
Abstract: The electricity generation and distribution system in many developed economies is based primarily on the centralised grid. However, there is a need to shift from this traditional system to a newly more decentralised electricity system. This paper explores possible scenarios of adoption and diffusion of Micro-Grids (MGs) in Italy. An agent-based model is formulated to simulate the diffusion process as function of regional factors, subsidies and people's attitude. It assumes that MGs are purchased directly by communities of neighbours, which benefit from cost sharing. Results show high dependence of the diffusion process on regional factors: electricity demand, renewable potential and population. The model confirms that subsidies boost diffusion, mainly when they are regional-based rather than national-based. Higher green attitude accelerates diffusion and reduces environmental impact of the electricity system.

Hyesop Shin and Mike Bithell
Journal of Artificial Societies and Social Simulation 22 (1) 12

Kyeywords: PM10, Exposure, Health Vulnerability, Agent-Based Model (ABM), Seoul
Abstract: This study presents a proof-of-concept agent-based model (ABM) of health vulnerability to long-term exposure to airborne particulate pollution, specifically to particles less than 10 micrometres in size (PM10), in Seoul, Korea. We estimated the differential effects of individual behaviour and social class across heterogeneous space in two districts, Gwanak and Gangnam. Three scenarios of seasonal PM10 change (business as usual: BAU, exponential increase: INC, and exponential decrease: DEC) and three scenarios of resilience were investigated, comparing the vulnerability rate both between and within each district. Our first result shows that the vulnerable groups in both districts, including those aged over 65, aged under 15, and with a low education level, increased sharply after 5,000 ticks (each tick corresponding to 1 day). This implies that disparities in health outcomes can be explained by socioeconomic status (SES), especially when the group is exposed over a long period. Additionally, while the overall risk population was larger in Gangnam in the AC100 scenarios, the recovery level from resilience scenarios decreased the risk population substantially, for example from 7.7% to 0.7%. Our second finding from the local-scale analysis indicates that most Gangnam sub-districts showed more variation both spatially and in different resilience scenarios, whereas Gwanak areas showed a uniform pattern regardless of earlier prevention. The implication for policy is that, while some areas, such as Gwanak, clearly require urgent mitigating action, areas like Gangnam may show a greater response to simpler corrections, but aggregating up to the district scale may miss particular areas that are more at risk. Future work should consider other pollutants as well as more sophisticated population and pollution modelling, coupled with explicit representation of transport and more careful treatment of individual doses and the associated health responses.

Roberto Calisti, Primo Proietti and Andrea Marchini
Journal of Artificial Societies and Social Simulation 22 (1) 2

Kyeywords: Sustainable Consumption, Agent-Based Modelling, Farmers’ Market, Consumer Behaviour, Consumer Networks, Location-Allocation Problem
Abstract: A useful way of promoting sustainable food consumption is to consider the spread of food retail operations focused on food diversification, food specialization, and fresh and local products. These food shops are generally small, which is a great problem for survival against ruthless competition from supermarkets. Our research objective was to construct a simulation with an agent-based model, reproducing the local food consumption market and to investigate how a new, small food retailing shop interacts with this market. As a case study, the model simulates the opening of a small farmers’ market. The intent of the model is to reproduce the current status of consumption for food products within a certain territorial context and given time period, and to investigate how consumers’ behaviour changes with the opening of the new shop. As a result, we could predict changes in consumers’ habits, the economic positioning of new, small shops and its best location. This information is of considerable interest for farmers’ markets and also for policymakers.

Mostafa Shaaban, Jürgen Scheffran, Jürgen Böhner and Mohamed S. Elsobki
Journal of Artificial Societies and Social Simulation 22 (1) 4

Kyeywords: Energy Security, Energy Landscape, Egypt, Multi-Criteria Decision Analysis, Agent-Based Modeling, Geographic Information System
Abstract: To respond to the emerging challenge of climate change, feasible strategies need to be formulated towards sustainable development and energy security on a national and international level. Lacking a dynamic sustainability assessment of technologies for electricity planning, this paper fills the gap with a multi-criteria and multi-stakeholder evaluation in an integrated assessment of energy systems. This allows to select the most preferred strategies for future planning of energy security in Egypt, with a focus on alternative energy pathways and a sustainable electricity supply mix up to 2100. A novel prototype model is used to integrate multi-criteria decision analysis (MCDA) as a premium decision support approach with agent-based modeling (ABM). This tool is popular in analyzing dynamic complex systems. A GIS-based spatial ABM analyzes future pathways for energy security in Egypt, depending on the preferences of agents for selected criteria to facilitate the transformation of energy landscapes. The study reveals significant temporal variations in the spatial ranking of technologies between actors in the energy sector over this period. We conclude that in order to attain a sustainable energy landscape, we should involve relevant stakeholders and analyze their interactions while considering local spatial conditions and key dimensions of sustainable development.

Fábio Neves, Pedro Campos and Sandra Silva
Journal of Artificial Societies and Social Simulation 22 (1) 8

Kyeywords: Agent-Based Model, Innovation, Automation, Employment
Abstract: While the effects of innovation on employment have been a controversial issue in economic literature for several years, this economic puzzle is particularly relevant nowadays. We are witnessing tremendous technological developments which threaten to disrupt the labour market, due to their potential for significantly automating human labour. As such, this paper presents a qualitative study of the dynamics underlying the relationship between innovation and employment, using an agent-based model developed in Python. The model represents an economy populated by firms able to perform either Product Innovation (leading to the discovery of new tasks, which require human labour) or Process Innovation (leading to the automation of tasks previously performed by humans). The analysis led to three major conclusions, valid in this context. The first takeaway is that the Employment Rate in a given economy is dependent on the automation potential of the tasks in that economy and dependent on the type of innovation performed by firms in that economy (with Product Innovation having a positive effect on employment and Process Innovation having a negative effect). Second, in any given economy, if firms’ propensity for product and process innovation, as well as the automation potential of their tasks are stable over time, the Employment Rate in that economy will tend towards stability over time. The third conclusion is that higher levels of Process Innovation and lower levels of Product Innovation, lead to a more intense decline of wage shares and to a wider gap between employee productivity growth and wage growth.

Rijk Mercuur, Virginia Dignum and Catholijn Jonker
Journal of Artificial Societies and Social Simulation 22 (1) 9

Kyeywords: Human Values, Norms, Ultimatum Game, Empirical Data, Agent-Based Model
Abstract: Social simulations gain strength when agent behaviour can (1) represent human behaviour and (2) be explained in understandable terms. Agents with values and norms lead to simulation results that meet human needs for explanations, but have not been tested on their ability to reproduce human behaviour. This paper compares empirical data on human behaviour to simulated data on agents with values and norms in a psychological experiment on dividing money: the ultimatum game. We find that our agent model with values and norms produces aggregate behaviour that falls within the 95% confidence interval wherein human behaviour lies more often than other tested agent models. A main insight is that values serve as a static component in agent behaviour, whereas norms serve as a dynamic component.

Noudéhouénou Lionel Jaderne Houssou, Juan Durango Cordero, Audren Bouadjio-Boulic, Lucie Morin, Nicolas Maestripieri, Sylvain Ferrant, Mahamadou Belem, Jose Ignacio Pelaez Sanchez, Melio Saenz, Emilie Lerigoleur, Arnaud Elger, Benoit Gaudou, Laurence Maurice and Mehdi Saqalli
Journal of Artificial Societies and Social Simulation 22 (2) 1

Kyeywords: Ecuadorian Amazon, Oil Pollution Exposure, Agent-Based Modeling, Colonization Demography, Historical Modeling Reconstruction
Abstract: Since the 1970s, the northern part of the Amazonian region of Ecuador has been colonized with the support of intensive oil extraction that has opened up roads and supported the settlement of people from Outside Amazonia. These dynamics have caused important forest cuttings but also regular oil leaks and spills, contaminating both soil and water. The PASHAMAMA Model seeks to simulate these dynamics on both environment and population by examining exposure and demography over time thanks to a retro-prospective and spatially explicit agent-based approach. The aim of the present paper is to describe this model, which integrates two dynamics: (a) Oil companies build roads and oil infrastructures and generate spills, inducing leaks and pipeline ruptures affecting rivers, soils and people. This infrastructure has a probability of leaks, ruptures and other accidents that produce oil pollution affecting rivers, soils and people. (b) New colonists settled in rural areas mostly as close as possible to roads and producing food and/or cash crops. The innovative aspect of this work is the presentation of a qualitative-quantitative approach explicitly addressed to formalize interdisciplinary modeling when data contexts are almost always incomplete.

Francisco J. León-Medina
Journal of Artificial Societies and Social Simulation 22 (2) 4

Kyeywords: Opinion Dynamics, Mechanism Explanation, Agent-Based Modeling, Homophily, Social Influence, Social Network
Abstract: Opinion dynamics models usually center on explaining how macro-level regularities in public opinion (uniformity, polarization or clusterization) emerge as the effect of local interactions of a population with an initial random distribution of opinions. However, with only a few exceptions, the understanding of patterns of public opinion change has generally been dismissed in this literature. To address this theoretical gap in our understanding of opinion dynamics, we built a multi-agent simulation model that could help to identify some mechanisms underlying changes in public opinion. Our goal was to build a model whose behavior could show different types of endogenously (not induced by the researcher) triggered transitions (rapid or slow, radical or soft). The paper formalizes a situation where agents embedded in different types of networks (random, small world and scale free networks) interact with their neighbors and express an opinion that is the result of different mechanisms: a coherence mechanism, in which agents try to stick to their previously expressed opinions; an assessment mechanism, in which agents consider available external information on the topic; and a social influence mechanism, in which agents tend to approach their neighbor’s opinions. According to our findings, only scale-free networks show fluctuations in public opinion. Public opinion changes in this model appear as a diffusion process of individual opinion shifts that is triggered by an opinion change of a highly connected agent. The frequency, rapidity and radicalness of the diffusion, and hence of public opinion fluctuations, positively depends on how influential external information is in individual opinions and negatively depends on how homophilic social interactions are.

Tuong Manh Vu, Christian Wagner and Peer-Olaf Siebers
Journal of Artificial Societies and Social Simulation 22 (2) 7

Kyeywords: Agent-Based Modelling and Simulation, Continuous-Time Public Goods Game, Software Engineering, Agent-Based Computational Economics, Object-Oriented Analysis and Design
Abstract: Public Goods Games (PGGs) are a standard experimental economic approach to studying cooperative behaviour. There are two types of games: discrete-time and continuous-time PGGs. While discrete-time PGGs (one-shot decisions about contributions to public goods) can be easily done as lab experiments, continuous-time PGGs (where participants can change contributions at any time) are much harder to realise within a lab environment. This is mainly because it is difficult to consider events happening in continuous time in lab experiments. Simulation offers an opportunity to support real-world lab experiments and is well suited to explore continuous-time PGGs. In this paper, we show how to apply our recently developed ABOOMS (Agent-Based Object-Oriented Modelling and Simulation) development framework to create models for simulation-supported continuous-time PGG studies. The ABOOMS framework utilizes Software Engineering techniques to support the development at the macro level (considering the overall study lifecycle) and at the micro level (considering individual steps related to simulation model development). Our case study shows that outputs from the simulation-supported continuous-time PGG generate dynamics that do not exist in discrete-time setting, highlighting the fact that it is important to study both, discrete and continuous-time PGGs.

Serge Wiltshire, Asim Zia, Christopher Koliba, Gabriela Bucini, Eric Clark, Scott Merrill, Julie Smith and Susan Moegenburg
Journal of Artificial Societies and Social Simulation 22 (2) 8

Kyeywords: Agent-Based Modeling, Network Analytics, Computational Epidemiology, Evolutionary Computation, Livestock Production
Abstract: We developed an agent-based susceptible/infective model which simulates disease incursions in the hog production chain networks of three U.S. states. Agent parameters, contact network data, and epidemiological spread patterns are output after each model run. Key network metrics are then calculated, some of which pertain to overall network structure, and others to each node's positionality within the network. We run statistical tests to evaluate the extent to which each network metric predicts epidemiological vulnerability, finding significant correlations in some cases, but no individual metric that serves as a reliable risk indicator. To investigate the complex interactions between network structure and node positionality, we use a genetic programming (GP) algorithm to search for mathematical equations describing combinations of individual metrics — which we call "meta-metrics" — that may better predict vulnerability. We find that the GP solutions — the best of which combine both global and node-level metrics — are far better indicators of disease risk than any individual metric, with meta-metrics explaining up to 91% of the variability in agent vulnerability across all three study areas. We suggest that this methodology could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions, and also that the meta-metric approach may be useful to study a wide range of complex network phenomena.

Hung Khanh Nguyen, Raymond Chiong, Manuel Chica, Richard Middleton and Dung Thi Kim Pham
Journal of Artificial Societies and Social Simulation 22 (3) 1

Kyeywords: Agent-Based Modeling, Contract Farming, Agricultural Supply Chain, Computational Simulation
Abstract: In this paper, we use agent-based modeling (ABM) to study different obstacles to the expansion of contract rice farming in the context of Mekong Delta (MKD)'s rice supply chain. ABM is a bottom-up approach for modeling the dynamics of interactions among individuals and complex combinations of various factors (e.g., economic, social or environmental). Our agent-based contract farming model focuses on two critical components of contractual relationship, namely financial incentives and trust. We incorporate the actual recurrent fluctuations of spot market prices, which induce both contractor and farmer agents to renege on the agreement. The agent-based model is then used to predict emergent system-wide behaviors and compare counterfactual scenarios of different policies and initiatives on maintaining the contract rice farming scheme. Simulation results firstly show that a fully-equipped contractor who opportunistically exploits a relatively small proportion (less than 10%) of the contracted farmers in most instances can outperform spot market-based contractors in terms of average profit achieved for each crop. Secondly, a committed contractor who offers lower purchasing prices than the most typical rate can obtain better earnings per ton of rice as well as higher profit per crop. However, those contractors in both cases could not enlarge their contract farming scheme, since either farmers' trust toward them decreases gradually or their offers are unable to compete with the benefits from a competitor or the spot market. Thirdly, the results are also in agreement with the existing literature that the contract farming scheme is not a cost-effective method for buyers with limited rice processing capacity, which is a common situation among the contractors in the MKD region. These results yield significant insights into the difficulty in expanding the agricultural contracting program in the MKD's rice supply chain.

Junhyok Jang, Xiaofeng Ju, Unsok Ryu and Hyonchol Om
Journal of Artificial Societies and Social Simulation 22 (3) 3

Kyeywords: Knowledge Diffusion, Knowledge Network, Coevolutionary, Genetic Algorithm, Agent-Based Modeling
Abstract: The co-evolutionary dynamics of knowledge diffusion and network structure in knowledge management is a recent research trend in the field of complex networks. The aim of this study is to improve the knowledge diffusion performance of knowledge networks including personnel, innovative organizations and companies. In order to study the co-evolutionary dynamics of knowledge diffusion and network structure, we developed a genetic algorithm-agent based model (GA-ABM) by combining a genetic algorithm (GA) and an agent-based model (ABM). Our simulations show that our GA-ABM improved the average knowledge stock and knowledge growth rate of the whole network, compared with several other models. In addition, it was shown that the topological structure of the optimal network obtained by GA-ABM has the property of a random network. Finally, we found that the clustering coefficients of agents are not significant to improve knowledge diffusion performance.

Antoni Perello-Moragues, Pablo Noriega and Manel Poch
Journal of Artificial Societies and Social Simulation 22 (4) 1

Kyeywords: Agent-Based Modeling, Innovation Diffusion, Policy-Making, Irrigation Agriculture, Socio-Hydrology
Abstract: Of all the uses of water, agriculture is the one that requires the greatest proportion of resources worldwide. Consequently, it is a salient subject for environmental policy-making, and adoption of modern irrigation systems is a key means to improve water use efficiency. In this paper we present an agent-based model of the adoption process —known as "modernisation"— of a community constituted by farmer agents. The phenomenon is approached as a contingent innovation adoption: a first stage to reach a collective agreement followed by an individual adoption decision. The model is based on historical data from two Spanish irrigation communities during the period 1975-2010. Results suggest that individual profits and farm extension (as proxy of social influence) are suitable assumptions when modelling the modernisation of communities in regions where agriculture is strongly market-oriented and water is scarce. These encouraging results point towards the interest of more sophisticated socio-cognitive modelling within a more realistic socio-hydrologic context.

Juste Raimbault, Clémentine Cottineau, Marion Le Texier, Florent Le Nechet and Romain Reuillon
Journal of Artificial Societies and Social Simulation 22 (4) 10

Kyeywords: Space, Initial Conditions, Sensitivity, Agent-Based Models
Abstract: Although simulation models of socio-spatial systems in general and agent-based models in particular represent a fantastic opportunity to explore socio-spatial behaviours and to test a variety of scenarios for public policy, the validity of generative models is uncertain unless their results are proven robust and representative of 'real-world' conditions. Sensitivity analysis usually includes the analysis of the effect of stochasticity on the variability of results, as well as the effects of small parameter changes. However, initial spatial conditions are usually not modified systematically in socio-spatial models, thus leaving unexplored the effect of initial spatial arrangements on the interactions of agents with one another as well as with their environment. In this article, we present a method to assess the effect of variation of some initial spatial conditions on simulation models, using a systematic geometric structures generator in order to create density grids with which socio-spatial simulation models are initialised. We show, with the example of two classical agent-based models (Schelling's model of segregation and Sugarscape's model of unequal societies) and a straightforward open-source workflow using high performance computing, that the effect of initial spatial arrangements is significant on the two models. We wish to illustrate the potential interest of adding spatial sensitivity analysis during the exploration of models for both modellers and thematic specialists.

Jie Yan, Renjing Liu, Zhengwen He and Xiaobo Wan
Journal of Artificial Societies and Social Simulation 22 (4) 2

Kyeywords: Forgetting, Knowledge Management Strategy, Exploration-Exploitation, Agent-Based Modeling
Abstract: The creation, transfer and retention of knowledge in an organization has always been the focus of knowledge management researchers; however, one aspect of the dynamics of knowledge, i.e., forgetting, has received comparatively limited attention. To fill this research gap, we extend the basic simulation model proposed by March by incorporating forgetting and three knowledge management strategies, i.e., personalization, codification, and mixed, to explore the impacts of different knowledge management strategies and forgetting on the organizational knowledge level. The simulation results not only clarify the specific measures used to manage individual forgetting in each knowledge management strategy but also identify the boundary conditions under which knowledge management strategies should be adopted under different conditions.

Marco Civico
Journal of Artificial Societies and Social Simulation 22 (4) 3

Kyeywords: Language, Multilingualism, Minority, Complexity, Agent-Based Modelling, Population Dynamics
Abstract: This article discusses the adoption of a complexity theory approach to study the dynamics of language contact within multilingual communities. It develops an agent-based model that simulates the dynamics of communication within a community where a minority and a majority group coexist. The individual choice of language for communication is based on a number of simple rules derived from a review of the main literature on the topic of language contact. These rules are then combined with different variables, such as the rate of exogamy of the minority group and the presence of relevant education policies, to estimate the trends of assimilation of the minority group into the majority one. The model is validated using actually observed data from the case of Romansh speakers in the canton of Grisons, Switzerland. The data collected from the simulations are then analysed by means of regression techniques. This paper shows that macro-level language contact dynamics can be explained by relatively simple micro-level behavioural patterns and that intergenerational transmission is crucial for the long-term survival of minority-language groups.

Nicolas Houy
Journal of Artificial Societies and Social Simulation 22 (4) 4

Kyeywords: Identity, Immigration, Democracy, Mobility, Schelling Model, Agent-Based Model
Abstract: We look at the dynamics of identity and immigration in a setting in which political decisions regarding immigration are made by a majoritarian democratic process and location is endogenous. We introduced an agent-based model that allowed us to explain the following facts: When individuals are not allowed to choose their own location, the ratio of immigrants in the population is close to optimal and assimilation works well. On the contrary, when individuals are allowed to move, clusters of different types of populations form. This has the following consequences: assimilation becomes more difficult by formation of closed communities and therefore the native identity can only survive if a large level of immigration is supported by individuals protected from its consequences and vote with local information or consideration. Even in the latter case, temporary outbursts of anti-immigration policy can occur. These results should be understood in the recent context of increasing salience of identity concerns and the following positive electoral results for the so-called populist movements in Western countries.

Christopher Weimer, J.O. Miller, Raymond Hill and Douglas Hodson
Journal of Artificial Societies and Social Simulation 22 (4) 5

Kyeywords: Opinion Dynamics, Agent-Based Modeling, Scheduling, Asynchronous, Synchronous
Abstract: Opinion dynamics models are an important field of study within the agent-based modeling community. Agent scheduling elements within existing opinion dynamics models vary but are largely unjustified and only minimally explained. Furthermore, previous research on the impact of scheduling is scarce, partially due to a lack of a common taxonomy with which to discuss and compare schedules. The Synchrony, Actor type, Scale (SAS) taxonomy is presented, which aims to provide a common lexicon for agent scheduling in opinion dynamics models. This is demonstrated using a generalized repeated averaging model (GRAM) and a generalized bounded confidence model (GBCM). Significant differences in model outcomes with varied schedules are given, along with the results of intentional model biasing using only schedule variation. We call on opinion dynamics modelers to make explicit their choice of schedule and to justify that choice based on realistic social phenomena.

Mart van der Kam, Annemijn Peters, Wilfried van Sark and Floor Alkemade
Journal of Artificial Societies and Social Simulation 22 (4) 7

Kyeywords: Electric Vehicles, Intermittent Renewables, Smart Charging, Environmental Self-Identity, Range Anxiety, Agent-Based Model
Abstract: The combination of electric vehicles (EVs) and intermittent renewable energy sources has received increasing attention over the last few years. Not only does charging electric vehicles with renewable energy realize their true potential as a clean mode of transport, charging electric vehicles at times of peaks in renewable energy production can help large scale integration of renewable energy in the existing energy infrastructure. We present an agent-based model that investigates the potential contribution of this combination. More specifically, we investigate the potential effects of different kinds of policy interventions on aggregate EV charging patterns. The policy interventions include financial incentives, automated smart charging, information campaigns and social charging. We investigate how well the resulting charging patterns are aligned with renewable energy production and how much they affect user satisfaction of EV drivers. Where possible, we integrate empirical data in our model, to ensure realistic scenarios. We use recent theory from environmental psychology to determine agent behaviour, contrary to earlier simulation models, which have focused only on technical and financial considerations. Based on our simulation results, we articulate some policy recommendations. Furthermore, we point to future research directions for environmental psychology scholars and modelers who want to use theory to inform simulation models of energy systems.

Peer-Olaf Siebers, Zhi En Lim, Grazziela P. Figueredo and James Hey
Journal of Artificial Societies and Social Simulation 23 (1) 10

Kyeywords: Integrated Assessment Modelling, Climate Change, Agent-Based Modelling, System Dynamics Modelling, Methodological Advance, Hybridisation, Scalability
Abstract: Modelling and simulation play an increasingly significant role in exploratory studies for informing policy makers on climate change mitigation strategies. There is considerable research being done in creating Integrated Assessment Models (IAMs), which focus on examining the human impacts on climate change. Many popular IAMs are created as steady state optimisation models. They typically employ a nested structure of neoclassical production functions to represent the energy-economy system, holding aggregate views on variables, and hence are unable to capture a finer level of details of the underlying system components. An alternative approach that allows modelling populations as a collection of individual and unevenly distributed entities is Agent-Based Modelling, often used in the field of Social Simulation. But simulating huge numbers of individual entities can quickly become an issue, as it requires large amounts of computational resources. The goal of this paper is to introduce a conceptual framework for developing hybrid IAMs. This novel modelling approach allows us to reuse existing rigid, but well-established IAMs, and adds more flexibility by replacing aggregate stocks with a community of vibrant interacting entities. We provide a proof-of-concept of the application of this conceptual framework in form of an illustrative example. Our test case takes the settings of the US. It is solely created for the purpose of demonstrating our hybrid modelling approach; we do not claim that it has predictive powers.

Yue Dou, Guolin Yao, Anna Herzberger, Ramon Felipe Bicudo da Silva, Qian Song, Ciara Hovis, Mateus Batistella, Emilio Moran, Wenbin Wu and Jianguo Liu
Journal of Artificial Societies and Social Simulation 23 (1) 11

Kyeywords: Telecoupling, Agent-Based Model, Land System, Land-Use Change, Soybean Trade, ODD+D
Abstract: International agricultural trade has changed land uses in trading countries, altering global food security and environmental sustainability. Studies have concluded that local land-use drivers are largely from global sources (e.g., trade increases deforestation in exporting countries). However, little is known about how these local land-use changes affect distant locations, namely the feedback between them. Yet these distant impacts and feedbacks can be significant for governing local land systems. The framework of telecoupling (i.e., socioeconomic-environmental interactions between distant places) has been shown to be an effective conceptual tool to study international trade and the associated socio-economic and environmental impacts. However, a systems simulation tool to quantify the telecoupled causes and effects is still lacking. Here, we construct a new type of agent-based model (ABM) that can simulate land-use changes at multiple distant places (namely TeleABM, telecoupled agent-based model). We use soybean trade between Brazil and China as an example, where Brazil is the sending system and China is the receiving system because they are the world’s largest soybean exporter and importer respectively. We select one representative county in each country to calibrate and validate the model with spatio-temporal analysis of historical land-use changes and the empirical analysis of household survey data. We describe the model following the ODD+D protocol, and validate the model results in each location respectively. We then illustrate how the aggregated farmer agents’ land-use behaviors in the sending system result in land-use changes in the receiving system, and vice versa. One scenario example (i.e., a high-tariff scenario) is given to demonstrate the results of TeleABM. Such a model allows us to advance the understanding of telecoupling features and the influence on land system science, and to test hypotheses about complex coupled human-natural systems (e.g., cascading effect).

Li An, Volker Grimm and Billie L. Turner II
Journal of Artificial Societies and Social Simulation 23 (1) 13

Kyeywords: Agent-Based Modeling, Complex Systems, System Integration, Social-Ecological Systems, Overview
Abstract: This editorial paper reviews the state of the science about agent-based modeling (ABM), pointing out the strengths and weaknesses of ABM. This paper also highlights several impending tasks that warrant special attention in order to improve the science and application of ABM: Modeling human decisions, ABM transparency and reusability, validation of ABM, ABM software and big data ABM, and ABM theories. Six innovative papers that are included in the special issue are summarized, and their connections to the ABM impending tasks are brought to attention. The authors hope that this special issue will help prioritize specific resources and activities in relation to ABM advances, leading to coordinated, joint efforts and initiatives to advance the science and technology behind ABM.

Wenwu Tang and Jianxin Yang
Journal of Artificial Societies and Social Simulation 23 (1) 15

Kyeywords: Agent-Based Model, Land Use and Land Cover Change, Critical Threshold, Water Quality, North Carolina
Abstract: Land use and land cover change has been recognized to have significant environmental impacts in a watershed, such as regulation of water quality. However, the identification of potential regions that are sensitive to land change activities for the protection of water quality poses a grand challenge particularly in a large watershed. These potential regions are often associated with critical thresholds in terms of, for example, water quality. In this study, we developed an agent-based land change model to investigate the relationship between land development activities and water quality in eight North Carolina counties that cover the lower High Rock Lake Watershed area. This agent-based model, which is empirically calibrated, is used to identify space-time locations of those regions at critical thresholds of water quality in this study area. Our experimental results suggest that land development as a form of system stress is of pivotal importance in affecting water quality at sub watershed level and the state transition of water quality. The agent-based model developed in this study provides solid support for investigations on the impact of land development under alternative scenarios in a large watershed.

Diana Suleimenova and Derek Groen
Journal of Artificial Societies and Social Simulation 23 (1) 2

Kyeywords: Refugee Modelling, Agent-Based Modelling, Automation Toolkit, Policy Decisions, Validation, Sensitivity Analysis
Abstract: Forced displacement has a huge impact on society today, as more than 68 million people are forcibly displaced worldwide. Existing methods for forecasting the arrival of migrants, especially refugees, may help us to better allocate humanitarian support and protection. However, few researchers have investigated the effects of policy decisions, such as border closures, on the movement of these refugees. Recently established simulation development approaches have made it possible to conduct such a study. In this paper, we use such an approach to investigate the effect of policy decisions on refugee arrivals for the South Sudan refugee crisis. To make such a study feasible in terms of human effort, we rely on agent-based modelling, and have automated several phases of simulation development using the FabFlee automation toolkit. We observe a decrease in the average relative difference from 0.615 to 0.499 as we improved the simulation model with additional information. Moreover, we conclude that the border closure and a reduction in camp capacity induce fewer refugee arrivals and more time spend travelling to other camps. While a border opening and an increase in camp capacity result in a limited increase in refugee arrivals at the destination camps. To the best of our knowledge, we are the first to conduct such an investigation for this conflict.

Steven Manson, Li An, Keith C. Clarke, Alison Heppenstall, Jennifer Koch, Brittany Krzyzanowski, Fraser Morgan, David O'Sullivan, Bryan C Runck, Eric Shook and Leigh Tesfatsion
Journal of Artificial Societies and Social Simulation 23 (1) 3

Kyeywords: Spatial, Agent-Based Model, Methods, Human-Environment Systems
Abstract: Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling.

Arthur Hjorth, Bryan Head, Corey Brady and Uri Wilensky
Journal of Artificial Societies and Social Simulation 23 (1) 4

Kyeywords: Multi-Level, Agent-Based Modeling, Modeling Tools, Netlogo
Abstract: Multi-Level Agent-Based Modeling (ML-ABM) has been receiving increasing attention in recent years. In this paper we present LevelSpace, an extension that allows modelers to easily build ML-ABMs in the popular and widely used NetLogo language. We present the LevelSpace framework and its associated programming primitives. Based on three common use-cases of ML-ABM – coupling of heterogeneous models, dynamic adaptation of detail, and cross-level interaction - we show how easy it is to build ML-ABMs with LevelSpace. We argue that it is important to have a unified conceptual language for describing LevelSpace models, and present six dimensions along which models can differ, and discuss how these can be combined into a variety of ML-ABM types in LevelSpace. Finally, we argue that future work should explore the relationships between these six dimensions, and how different configurations of them might be more or less appropriate for particular modeling tasks.

Li An, Judy Mak, Shuang Yang, Rebecca Lewison, Douglas A. Stow, Hsiang Ling Chen, Weihua Xu, Lei Shi and Yu Hsin Tsai
Journal of Artificial Societies and Social Simulation 23 (1) 5

Kyeywords: Agent-Based Modeling, Payments for Ecosystem Services, Complex Human-Environment Systems, Guizhou Snub-Nosed Monkey, Migration, Land Use
Abstract: The theory and practice associated with payments for ecosystem services (PES) feature a variety of piecemeal studies related to impacts of socioeconomic, demographic, and environmental variables, lacking efforts in understanding their mutual relationships in a spatially and temporally explicit manner. In addition, PES literature is short of ecological metrics that document the consequences of PES other than land use and land cover and its change. Building on detailed survey data from Fanjingshan National Nature Reserve (FNNR), China, we developed and tested an agent-based model to study the complex interactions among human livelihoods (migration and resource extraction in particular), PES, and the Guizhou golden monkey habitat occupancy over 20 years. We then performed simulation-based experiments testing social and ecological impacts of PES payments as well as human population pressures. The results show that with a steady increase in outmigration, the number of land parcels enrolled in one of China’s major PES programs tends to increase, reach a peak, and then slowly decline, showing a convex trend that converges to a stable number of enrolled parcels regardless of payment levels. Simulated monkey occupancy responds to changes in PES payment levels substantially in edge areas of FNNR. Our model is not only useful for FNNR, but also applicable as a platform to study and further understand human and ecological roles of PES in many other complex human-environment systems, shedding light into key elements, interactions, or relationships in the systems that PES researchers and practitioners should bear in mind. Our research contributes to establishing a scientific basis of PES science that incorporates features in complex systems, offering more realistic, spatially and temporally explicit insights related to PES policy or related interventions.

Arika Ligmann-Zielinska, Peer-Olaf Siebers, Nicholas R Magliocca, Dawn C. Parker, Volker Grimm, Jing Du, Martin Cenek, Viktoriia Radchuk, Nazia N. Arbab, Sheng Li, Uta Berger, Rajiv Paudel, Derek T. Robinson, Piotr Jankowski, Li An and Xinyue Ye
Journal of Artificial Societies and Social Simulation 23 (1) 6

Kyeywords: Sensitivity Analysis, Agent-Based Model, Individual-Based Model, Review
Abstract: Designing, implementing, and applying agent-based models (ABMs) requires a structured approach, part of which is a comprehensive analysis of the output to input variability in the form of uncertainty and sensitivity analysis (SA). The objective of this paper is to assist in choosing, for a given ABM, the most appropriate methods of SA. We argue that no single SA method fits all ABMs and that different methods of SA should be used based on the overarching purpose of the model. For example, abstract exploratory models that focus on a deeper understanding of the target system and its properties are fed with only the most critical data representing patterns or stylized facts. For them, simple SA methods may be sufficient in capturing the dependencies between the output-input spaces. In contrast, applied models used in scenario and policy-analysis are usually more complex and data-rich because a higher level of realism is required. Here the choice of a more sophisticated SA may be critical in establishing the robustness of the results before the model (or its results) can be passed on to end-users. Accordingly, we present a roadmap that guides ABM developers through the process of performing SA that best fits the purpose of their ABM. This roadmap covers a wide range of ABM applications and advocates for the routine use of global methods that capture input interactions and are, therefore, mandatory if scientists want to recognize all sensitivities. As part of this roadmap, we report on frontier SA methods emerging in recent years: a) handling temporal and spatial outputs, b) using the whole output distribution of a result rather than its variance, c) looking at topological relationships between input data points rather than their values, and d) looking into the ABM black box – finding behavioral primitives and using them to study complex system characteristics like regime shifts, tipping points, and condensation versus dissipation of collective system behavior.

Ernesto Carrella, Richard Bailey and Jens Koed Madsen
Journal of Artificial Societies and Social Simulation 23 (1) 7

Kyeywords: Agent-Based Models, Indirect Inference, Estimation, Calibration, Simulated Minimum Distance, Approximate Bayesian Computation
Abstract: In this paper, we introduce a simple way to parametrize simulation models by using regularized linear regression. Regressions bypass the three major challenges of calibrating by minimization: selecting the summary statistics, defining the distance function and minimizing it numerically. By substituting regression with classification, we can extend this approach to model selection. We present five example estimations: a statistical fit, a biological individual-based model, a simple real business cycle model, a non-linear biological simulation and heuristics selection in a fishery agent-based model. The outcome is a method that automatically chooses summary statistics, weighs them and uses them to parametrize models without running any direct minimization.

Inyoung Hwang
Journal of Artificial Societies and Social Simulation 23 (1) 9

Kyeywords: Agent-Based Modelling, Prisoner’s Dilemma, Pavlovian Cooperation, Collaborative Innovation, Firm Size Distribution, ICT Industry
Abstract: ICT-based Collaborative innovation has a significant impact on the economy by facilitating technological convergence and promoting innovation in other industries. However, research on innovation suggests that polarization in firm size distribution, which has grown since the early 2000s, can interfere with collaborative innovation among firms. In this paper, I modelled firms’ decision-making processes that led to collaborative innovation as a spatial N-person iterated Prisoner’s dilemma (NIPD) game using collaborative innovation data from Korean ICT firms. Using an agent-based model, I experimented with the effects of firm size heterogeneity on collaborative innovation. The simulation experiment results reveal that collaborative innovation in the industry increases as the size heterogeneity decreases. Findings suggest that policies promoting collaborative innovation should focus on mitigating structural inequalities in the industry.

Flaminio Squazzoni, J. Gareth Polhill, Bruce Edmonds, Petra Ahrweiler, Patrycja Antosz, Geeske Scholz, Emile Chappin, Melania Borit, Harko Verhagen, Francesca Giardini and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 23 (2) 10

Kyeywords: COVID-19, Pandemic Disease, Agent-Based Models, Modelling, Policy, Data
Abstract: The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.

Alireza Mansouri and Fattaneh Taghiyareh
Journal of Artificial Societies and Social Simulation 23 (2) 3

Kyeywords: Opinion Formation, Noise, Agent-Based Modeling, Social Impact Model, Phase Transition
Abstract: Human interactions and opinion exchanges lead to social opinion dynamics, which is well described by opinion formation models. In these models, a random parameter is usually considered as the system noise, indicating the individual's inexplicable opinion changes. This noise could be an indicator of any other influential factors, such as public media, affects, and emotions. We study phase transitions, changes from one social phase to another, for various noise levels in a discrete opinion formation model based on the social impact theory with a scale-free random network as its interaction network topology. We also generate another similar model using the concept of social power based on the agents' node degrees in the interaction network as an estimation for their persuasiveness and supportiveness strengths and compare both models from phase transition viewpoint. We show by agent-based simulation and analytical considerations how opinion phases, including majority and non-majority, are formed in terms of the initial population of agents in opinion groups and noise levels. Two factors affect the system phase in equilibrium when the noise level increases: breaking up more segregated groups and dominance of stochastic behavior of the agents on their deterministic behavior. In the high enough noise levels, the system reaches a non-majority phase in equilibrium, regardless of the initial combination of opinion groups. In relatively low noise levels, the original model and the model whose agents' strengths are proportional to their centrality have different behaviors. The presence of a few high-connected influential leaders in the latter model consequences a different behavior in reaching equilibrium phase and different thresholds of noise levels for phase transitions.

Qing Xu, Sylvie Huet, Eric Perret and Guillaume Deffuant
Journal of Artificial Societies and Social Simulation 23 (2) 4

Kyeywords: Organic Farming, Adaptation, Theory of Reasoned Action, Agent-Based Model, Social Influence, Credibility
Abstract: The drivers of conversion to organic farming, which is still a residual choice in agriculture, are poorly understood. Many scholars argue that farm characteristics can determine this choice but do not exclude the role of social dynamics. To study this issue, we developed an agent-based model in which agents' decisions to shift to organic farming are based on a comparison between satisfaction with the current situation and potential satisfaction with an alternative farming strategy. A farmer agent’s satisfaction is modelled using the Theory of Reasoned Action. This makes it necessary to compare an agent's productions over time with those of other agents to whom the former attributes considerable credibility (“important others”). Moreover, farmers make technical changes that affect their productions by imitating other credible farmers. While we first used this model to examine simple and abstract farm populations, here we also adapted it for use with data from an Agricultural Census concerning the farm characteristics of dairy farming in 27 French “cantons”. Based on domain expertise, data and previous research, we propose certain laws for modelling the impact of conversion on the farm production of milk and the environment. The simulations with “real” populations of farms confirm the important impact of farm characteristics. However, our results also suggest a complex impact of social dynamics that can favour or impede the diffusion of organic farming through dynamic implicit networks of similarity and credibility. We confirm the great importance of demographic changes in farm characteristics.

Victorien Barbet, Juliette Rouchier, Noé Guiraud and Vincent Laperrière
Journal of Artificial Societies and Social Simulation 23 (2) 5

Kyeywords: Agent-Based Model, Communication, Opinion Dynamics, Democracy, Non-Profit Organization, Short Food Chain
Abstract: We present a model showing the evolution of an organization of agents who discuss democratically about good practices. This model feeds on a field study we did for about twelve years in France where we followed NPOs, called AMAP, and observed their construction through time at the regional and national level. Most of the hypothesis we make are here either based on the literature on opinion diffusion or on the results of our field study. By defining dynamics where agents influence each other, make collective decision at the group level, and decide to stay in or leave their respective groups, we analyse the effect of different forms of vertical communication that is meant to spread good practices within the organization. Our main indicators of the good functioning of the democratic dynamics are stability and representativeness. We show that if communication about norms is well designed, it has a positive impact on both stability and representativeness. Interestingly the effect of communication increases with the number of dimensions discussed in the groups. Communication about norms is thus a valuable tool to use in groups that wish to improve their democratic practices without jeopardizing stability.

Volker Grimm, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Jürgen Groeneveld, Alice S.A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, J. Gareth Polhill, Viktoriia Radchuk, Marie-Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele and Daniel Ayllón
Journal of Artificial Societies and Social Simulation 23 (2) 7

Kyeywords: Agent-Based Model, Individual-Based Model, Best Practice, Simulation Model, Standardization, Documentation
Abstract: The Overview, Design concepts and Details (ODD) protocol for describing Individual- and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design rationale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.

Katarzyna Growiec, Jakub Growiec and Bogumił Kamiński
Journal of Artificial Societies and Social Simulation 23 (2) 8

Kyeywords: Social Network Structure, Social Network Dynamics, Trust, Willingness to Cooperate, Economic Performance, Agent-Based Model
Abstract: We study the impact of endogenous creation and destruction of social ties in an artificial society on aggregate outcomes such as generalized trust, willingness to cooperate, social utility and economic performance. To this end we put forward a computational multi-agent model where agents of overlapping generations interact in a dynamically evolving social network. In the model, four distinct dimensions of individuals’ social capital: degree, centrality, heterophilous and homophilous interactions, determine their generalized trust and willingness to cooperate, altogether helping them achieve certain levels of social utility (i.e., utility from social contacts) and economic performance. We find that the stationary state of the simulated social network exhibits realistic small-world topology. We also observe that societies whose social networks are relatively frequently reconfigured, display relatively higher generalized trust, willingness to cooperate, and economic performance – at the cost of lower social utility. Similar outcomes are found for societies where social tie dissolution is relatively weakly linked to family closeness.

Tuong Manh Vu, Charlotte Probst, Alexandra Nielsen, Hao Bai, Petra S. Meier, Charlotte Buckley, Mark Strong, Alan Brennan and Robin Purhouse
Journal of Artificial Societies and Social Simulation 23 (3) 1

Kyeywords: Agent-Based Modelling, Social Simulation, Software Architecture, Analytical Sociology, Abductive Reasoning
Abstract: This paper introduces the MBSSM (Mechanism-Based Social Systems Modelling) software architecture that is designed for expressing mechanisms of social theories with individual behaviour components in a unified way and implementing these mechanisms in an agent-based simulation model. The MBSSM architecture is based on a middle-range theory approach most recently expounded by analytical sociology and is designed in the object-oriented programming paradigm with Unified Modelling Language diagrams. This paper presents two worked examples of using the architecture for modelling individual behaviour mechanisms that give rise to the dynamics of population-level alcohol use: a single-theory model of norm theory and a multi-theory model that combines norm theory with role theory. The MBSSM architecture provides a computational environment within which theories based on social mechanisms can be represented, compared, and integrated. The architecture plays a fundamental enabling role within a wider simulation model-based framework of abductive reasoning in which families of theories are tested for their ability to explain concrete social phenomena.

Angelos Chliaoutakis and Georgios Chalkiadakis
Journal of Artificial Societies and Social Simulation 23 (3) 10

Kyeywords: Agent-Based Modeling, Model-Based Archaeology, Spatial Interaction Model, GraphTheory, Trade Network, Minoan Civilization
Abstract: Social and computational archaeology focuses largely on the study of past societies and the evolution of human behaviour. At the same time, agent-based models (ABMs) allow the efficient modeling of human agency, and the quantitative representation and exploration of specific properties and patterns in archaeological information. In this work we put forward a novel agent-based trading model, for simulating the exchange and distribution of resources across settlements in past societies. The model is part of a broader ABM populated with autonomous, utility-seeking agents corresponding to households; with the ability to employ any spatial interaction model of choice. As such, it allows the study of the settlements’ trading ability and power, given their geo-location and their position within the trading network, and the structural properties of the network itself. As a case study we use the Minoan society during the Bronze Age, in the wider area of "Knossos" on the island of Crete, Greece. We instantiate two well-known spatial interaction sub-models, XTENT and Gravity, and conduct a systematic evaluation of the dynamic trading network that is formed over time. Our simulations assess the sustainability of the artificial Minoan society in terms of population size, number and distribution of agent communities, with respect to the available archaeological data and spatial interaction model employed; and, further, evaluate the resulting trading network’s structure (centrality, clustering, etc.) and how it affects inter-settlement organization, providing in the process insights and support for archaeological hypotheses on the settlement organization in place at the time. Our results show that when the trading network is modeled using Gravity, which focuses on the settlements' "importance" rather than proximity to each other, settlement numbers’ evolution patterns emerge that are similar to the ones that exist in the archaeological record. It can also be inferred by our simulations that a rather dense trading network, without a strict settlement hierarchy, could have emerged during the Late Minoan period, after the Theran volcanic eruption, a well documented historic catastrophic event. Moreover, it appears that the trading network's structure and interaction patterns are reversed after the Theran eruption, when compared to those in effect in earlier periods.

Xianhua Wei, Aiya Li and Zhou He
Journal of Artificial Societies and Social Simulation 23 (3) 2

Kyeywords: Blockchain, Consensus Protocol, Trade Network Topology, Agent-Based Model
Abstract: Blockchain can be viewed as a public ledger maintained collectively by a large number of participators based on consensus protocol. We are interested in how difference consensus protocols and trade network topologies affect the performance of a blockchain system, which has not been studied in the literature yet. In this paper, we proposed an agent-based model consisting of multiple trader and miner agents, and one system agent. We investigated three consensus protocols, namely proof-of-work (PoW), proof-of-stake (PoS), and delegated proof-of-stake (DPoS). We also examined three common trade network topologies: random, small-world, and scale-free. We find that both consensus protocol and trade network topology can impact the performance of blockchain system. PoS and DPoS are generally better than PoW in terms of increasing trade efficiency and equalizing wealth. Besides, scale-free trade network is not favorable because its trade efficiency is quite low, which moderates the price fluctuation and wealth inequality. Since connectivity inequality determines wealth inequality, it is crucial to increase the connectivity among participants when designing a sustainable blockchain system. We suggest that our findings could be useful to the designers, practitioner and researchers of blockchain system and token economy.

Nicolas Malleson, Kevin Minors, Le-Minh Kieu, Jonathan Ward, Andrew West and Alison Heppenstall
Journal of Artificial Societies and Social Simulation 23 (3) 3

Kyeywords: Agent-Based Modelling, Particle Filter, Data Assimilation, Crowd Simulation, Pedestrian Modelling
Abstract: Agent-based modelling is a valuable approach for modelling systems whose behaviour is driven by the interactions between distinct entities, such as crowds of people. However, it faces a fundamental difficulty: there are no established mechanisms for dynamically incorporating real-time data into models. This limits simulations that are inherently dynamic, such as those of pedestrian movements, to scenario testing on historic patterns rather than real-time simulation of the present. This paper demonstrates how a particle filter could be used to incorporate data into an agent-based model of pedestrian movements at run time. The experiments show that although it is possible to use a particle filter to perform online (real time) model optimisation, the number of individual particles required (and hence the computational complexity) increases exponentially with the number of agents. Furthermore, the paper assumes a one-to-one mapping between observations and individual agents, which would not be the case in reality. Therefore this paper lays some of the fundamental groundwork and highlights the key challenges that need to be addressed for the real-time simulation of crowd movements to become a reality. Such success could have implications for the management of complex environments both nationally and internationally such as transportation hubs, hospitals, shopping centres, etc.

Simon Schweighofer, Frank Schweitzer and David Garcia
Journal of Artificial Societies and Social Simulation 23 (3) 5

Kyeywords: Polarization, Balance Theory, Opinion Dynamics, Agent-Based Modeling
Abstract: Polarization is threatening the stability of democratic societies. Until now, polarization research has focused on opinion extremeness, overlooking the correlation between different policy issues. In this paper, we explain the emergence of hyperpolarization, i.e., the combination of extremeness and correlation between issues, by developing a new theory of opinion formation called "Weighted Balance Theory (WBT)". WBT extends Heider's cognitive balance theory to encompass multiple weighted attitudes. We validated WBT on empirical data from the 2016 National Election Survey. Furthermore, we developed an opinion dynamics model based on WBT, which, for the first time, is able to generate hyperpolarization and to explain the link between affective and opinion polarization. Finally, our theory encompasses other phenomena of opinion dynamics, including mono-polarization and backfire effects.

Marcin Czupryna, Christian Franzke, Sascha Hokamp and Jürgen Scheffran
Journal of Artificial Societies and Social Simulation 23 (3) 7

Kyeywords: Climate Change, Climate Protection, Integrated Assessment Model, Agent-Based Modelling
Abstract: There is an ongoing discussion concerning the relationship between social welfare and climate change, and thus the required level and type of measures needed to protect the climate. Integrated assessment models (IAMs) have been extended to incorporate technological progress, heterogeneity and uncertainty, making use of a (stochastic) dynamic equilibrium approach in order to derive a solution. According to the literature, the IAM class of models does not take all the relationships among economic, social and environmental factors into account. Moreover, it does not consider these interdependencies at the micro-level, meaning that all possible consequences are not duly examined. Here, we propose an agent-based approach to analyse the relationship between economic welfare and climate protection. In particular, our aim is to analyse how the decisions of individual agents, allowing for the trade-off between economic welfare and climate protection, influence the aggregated emergent economic behaviour. Using this model, we estimate a damage function, with values in the order 3% - 4%for 2 C temperature increase and having a linear (or slightly concave) shape. We show that the heterogeneity of the agents, technological progress and the damage function may lead to lower GDP growth rates and greater temperature-related damage than what is forecast by models with solely homogeneous (representative) agents.

Thomas Feliciani, Ramanathan Moorthy, Pablo Lucas and Kalpana Shankar
Journal of Artificial Societies and Social Simulation 23 (3) 8

Kyeywords: Peer Review, Grade Language, Agent-Based Modeling
Abstract: Simulation models have proven to be valuable tools for studying peer review processes. However, the effects of some of these models’ assumptions have not been tested, nor have these models been examined in comparative contexts. In this paper, we address two of these assumptions which go in tandem: (1) on the granularity of the evaluation scale, and (2) on the homogeneity of the grade language (i.e. whether reviewers interpret evaluation grades in the same fashion). We test the consequences of these assumptions by extending a well-known agent-based model of author and reviewer behaviour with discrete evaluation scales and reviewers’ interpretation of the grade language. In this way, we compare a peer review model with a homogeneous grade language, as assumed in most models of peer review, with a more psychologically realistic model where reviewers interpret the grades of the evaluation scale heterogeneously. We find that grade language heterogeneity can indeed affect the predictions of a model of peer review.

Chaitanya Kaligotla, Jonathan Ozik, Nicholson Collier, Charles M. Macal, Kelly Boyd, Jennifer Makelarski, Elbert S. Huang and Stacy T. Lindau
Journal of Artificial Societies and Social Simulation 23 (4) 1

Kyeywords: Agent-Based Modeling, Model Exploration, High-Performance Computing, Active Learning
Abstract: This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. The diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.

Saeed Moradi and Ali Nejat
Journal of Artificial Societies and Social Simulation 23 (4) 13

Kyeywords: Disaster Recovery, Recovery Modeling, Agent-Based Modeling, Perceived Community
Abstract: The housing sector is an important part of every community. It directly affects people, constitutes a major share of the building market, and shapes the community. Meanwhile, the increase of developments in hazard-prone areas along with the intensification of extreme events has amplified the potential for disaster-induced losses. Consequently, housing recovery is of vital importance to the overall restoration of a community. In this relation, recovery models can help with devising data-driven policies that can better identify pre-disaster mitigation needs and post-disaster recovery priorities by predicting the possible outcomes of different plans. Although several recovery models have been proposed, there are still gaps in the understanding of how decisions made by individuals and different entities interact to output the recovery. Additionally, integrating spatial aspects of recovery is a missing key in many models. The current research proposes a spatial model for simulation and prediction of homeowners’ recovery decisions through incorporating recovery drivers that could capture interactions of individual, communal, and organizational decisions. RecovUS is a spatial agent-based model for which all the input data can be obtained from publicly available data sources. The model is presented using the data on the recovery of Staten Island, New York, after Hurricane Sandy in 2012. The results confirm that the combination of internal, interactive, and external drivers of recovery affect households’ decisions and shape the progress of recovery.

Nicholas LaBerge, Aria Chaderjian, Victor Ginelli, Margrethe Jebsen and Adam Landsberg
Journal of Artificial Societies and Social Simulation 23 (4) 3

Kyeywords: Cultural Evolution, Cultural Transmission, Opinion Dynamics, Agent-Based Modeling, Cultural Dissemination
Abstract: The process by which beliefs, opinions, and other individual, socially malleable attributes spread across a society, known as "cultural dissemination," is a broadly recognized concept among sociologists and political scientists. Yet fundamental aspects of how this process can ultimately lead to cultural divergences between rural and urban segments of society are currently poorly understood. This article uses an agent-based model to isolate and analyze one very basic yet essential facet of this issue, namely, the question of how the intrinsic differences in urban and rural population densities influence the levels of cultural homogeneity/heterogeneity that emerge within each region. Because urban and rural cultures do not develop in isolation from one another, the dynamical interplay between the two is of particular import in their evolution. It is found that, in urban areas, the relatively high number of local neighbors with whom one can interact tends to promote cultural homogeneity in both urban and rural regions. Moreover, and rather surprisingly, the higher frequency of potential interactions with neighbors within urban regions promotes homogeneity in urban regions but tends to drive rural regions towards greater levels of heterogeneity.

Tae-Sub Yun and Il-Chul Moon
Journal of Artificial Societies and Social Simulation 23 (4) 5

Kyeywords: Housing Market, Macro-Prudential Policy, Loan-To-Value, Debt-To-Income, Agent-Based Modeling, Policy Impact Analysis
Abstract: This paper introduces an agent-based model of a housing market with macro-prudential policy experiments. Specifically, the simulation model is used to examine the effects of a policy setting on loan-to-value (LTV) and debt-to-income (DTI), which are policy instruments several governments use to regulate the housing market. The simulation model illustrates the interactions among the households, the house suppliers, and the real estate brokers. We model each household in the population as either seller or buyer, and some of households may behave as speculators in the housing market. To better understand the impact of the policies, we used the real-world observations from the Korean housing market, which include various economic conditions, policy variables, and Korean census data. Our baseline model is quantitatively validated to the price index and the transaction volume of the past Korean housing market. After validation, we show the empirical effectiveness of setting LTV and DTI towards house prices, transaction volumes, and the amount of households' mortgages. Furthermore, we investigate the simulation results for the owner-occupier rate of households. These investigations provide the policy analyses in Korea's housing market, and other governments with LTV and DTI regulations.

Wouter Vermeer, Arthur Hjorth, Samuel M. Jenness, C Hendrick Brown and Uri Wilensky
Journal of Artificial Societies and Social Simulation 23 (4) 7

Kyeywords: Replication, Agent-Based Models, Modular, High-Fidelity, HIV
Abstract: High-fidelity models are increasingly used to predict, and guide decision making. Prior work has emphasized the importance of replication in ensuring reliable modeling, and has yielded important replication strategies. However, this work is based on relatively simple theory generating models, and its lessons might not translate to high-fidelity models used for decision support. Using NetLogo we replicate a recently published high-fidelity model examining the effects of a HIV biomedical intervention. We use a modular approach to build our model from the ground up, and provide examples of the replication process investigating the replication of two sub-modules as well as the overall simulation experiment. For the first module, we achieved numerical identity during replication, whereas we obtained distributional equivalence in replicating the second module. We achieved relational equivalence among the overall model behaviors, with a 0.98 correlation across the two implementations for our outcome measure even without strictly following the original model in the formation of the sexual network. Our results show that replication of high-fidelity models is feasible when following a set of systematic strategies that leverage the modularity, and highlight the role of replication standards, modular testing, and functional code in facilitating such strategies.

Marco Cremonini and Samira Maghool
Journal of Artificial Societies and Social Simulation 23 (4) 8

Kyeywords: Stochastic Epidemic Model, Multi-Agent Simulation, Network Analysis, Agent-Based Model, Risk Analysis
Abstract: Lifting social restrictions is one of the most critical decisions that public health authorities have to face during a pandemic such as COVID-19. This work focuses on the risk associated with such a decision. We have called the period from the re-opening decision to epidemic expiration the ’final epidemic phase’, and considered the critical epidemic conditions which could possibly emerge in this phase. The factors we have considered include: the proportion of asymptomatic cases, a mitigation strategy based on testing and the average duration of infectious states. By assuming hypothetical configurations at the time of the re-opening decision and the partial knowledge concerning epidemic dynamics available to public health authorities, we have analyzed the risk of the re-opening decision based on possibly unreliable estimates. We have presented a discrete-time stochastic model with state-dependent transmission probabilities and multi-agent simulations. Our results show the different outcomes produced by different proportions of undetected asymptomatic cases, different probabilities of asymptomatic cases detected and contained, and a multivariate analysis of risk based on the average duration of asymptomatic and contained states. Finally, our analysis highlights that enduring uncertainty, typical of this pandemic, requires a risk analysis approach to complement epidemiological studies.

Beatrice Nöldeke, Etti Winter and Ulrike Grote
Journal of Artificial Societies and Social Simulation 23 (4) 9

Kyeywords: Information Diffusion, Social Networks, Agent-Based Modelling, Seeding, Zambia
Abstract: The successful adoption of innovations depends on the provision of adequate information to farmers. In rural areas of developing countries, farmers usually rely on their social networks as an information source. Hence, policy-makers and program-implementers can benefit from social diffusion processes to effectively disseminate information. This study aims to identify the set of farmers who initially obtain information (‘seeds’) that optimises diffusion through the network. It systematically evaluates different criteria for seed selection, number of seeds, and their interaction effects. An empirical Agent-Based Model adjusted to a case study in rural Zambia was applied to predict diffusion outcomes for varying seed sets ex ante. Simulations revealed that informing farmers with the most connections leads to highest diffusion speed and reach. Also targeting village heads and farmers with high betweenness centrality, who function as bridges connecting different parts of the network, enhances diffusion. An increased number of seeds improves reach, but the marginal effects of additional seeds decline. Interdependencies between seed set size and selection criteria highlight the importance of considering both seed selection criteria and seed set size for optimising seeding strategies to enhance information diffusion.

Ali Termos, Stefano Picascia and Neil Yorke-Smith
Journal of Artificial Societies and Social Simulation 24 (1) 2

Kyeywords: Rent-Gap Theory, Migration, Agent-Based Modelling, Urban Dynamics, Housing, Lebanon
Abstract: Rapid international migration of significant populations generates profound implications for countries in West Asia, Europe, and other regions. The motivation of this work is to develop an agent-based model (ABM) to capture the existence of such migrant and refugee flows, and to explore the effects of these flows on urban dynamics. Advances in agent-based modelling have led to theoretically-grounded spatial agent models of urban dynamics, capturing the dynamics of population, property prices, and regeneration. In this article we leverage such an extant agent-based model founded on the rent-gap theory, as a lens to study the effect of sizeable refugee migration upon a capital city in West Asia. In order to calibrate and validate the simulation model we construct indices for housing prices and other factors. Results from the model, implemented in NetLogo, show the impact of migration shock on the housing market, and identify the relative efficacy of housing intervention policies. Our work progresses towards a tool for policy makers asking what-if questions about the urban environment in the context of migration.

Alejandro Platas-López, Alejandro Guerra-Hernández and Francisco Grimaldo
Journal of Artificial Societies and Social Simulation 24 (1) 3

Kyeywords: Extortion, Macroeconomic Signals, BAM, Agent-Based Model
Abstract: This work proposes an agent-based approach to study the effect of extortion on macroeconomic aggregates, despite the fact that there is little data on this criminal activity given its hidden nature. We develop a Bottom-up Adaptive Macroeconomics (BAM) model that simulates a healthy economy, including a moderate inflation and a reasonable unemployment rate, and test the impact of extortion on various macroeconomic signals. The BAM model defines the usual interactions among workers, firms and banks in labour, goods and credit markets. Subsequently, crime is introduced by defining the propensity of the poorest workers to become extortionists, as well as the efficiency of the police in terms of their probability of capturing these extortionists. The definition of BAM under Extortion Racket Systems (BAMERS) model is completed with a threshold for the firms rejecting extortion. These parameters are explored extensively and independently. Results show that even low propensity towards extortion is enough to find considerable negative effects such as a marked contraction of Gross Domestic Product and increased unemployment, consistent with the little known data of the macroeconomic effects of extortion. The effects on consumption, Gini index, inflation and wealth distribution are also reported. Interestingly, our results suggest that it is more convenient to prevent extortion, rather than combat it once deployed, i.e., no police efficiency level achieves the healthy macroeconomic signals observed without extortion.

Francesca Giardini and Daniele Vilone
Journal of Artificial Societies and Social Simulation 24 (1) 4

Kyeywords: Risk Perceptions, Opinion Dynamics, Social Influence, Agent-Based Model
Abstract: The behavior of a heterogeneous population of individuals during an emergency, such as epidemics, natural disasters, terrorist attacks, is dynamic, emergent and complex. In this situation, reducing uncertainty about the event is crucial in order to identify and pursue the best possible course of action. People depend on experts, government sources, the media and fellow community members as potentially valid sources of information to reduce uncertainty, but their messages can be ambiguous, misleading or contradictory. Effective risk prevention depends on the way in which the population receives, elaborates and spread the message, and together these elements result in a collective perception of risk. The interaction between individuals' attitudes toward risk and institutions, the more or less alarmist way in which the information is reported and the role of the media can lead to risk perception that differs from the original message, as well as to contrasting opinions about risk within the same population. The aim of this study is to bridge a model of opinion dynamics with the issue of uncertainty and trust in the sources, in order to understand the determinants of collective risk assessment. Our results show that alarming information spreads more easily than reassuring one, and that the media plays a key role in this. Concerning the role of internal variables, our simulation results show that risk sensitiveness has more influence on the final opinion than trust towards the institutional message. Furthermore, the role of different network structures seemed to be negligible, even on two empirically calibrated network topologies, thus suggesting that knowing beforehand how much the public trusts their institutional representatives and how reactive they are to a certain risk might provide useful indications to design more effective communication strategies during crises.

Daniele Vernon-Bido and Andrew Collins
Journal of Artificial Societies and Social Simulation 24 (1) 6

Kyeywords: Agent-Based Modeling, Cooperative Game Theory, Modeling and Simulation, ABM, Cooperative Games
Abstract: Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. One area that ABM has rarely been applied is coalition formation. Traditionally, coalition formation is modelled using cooperative game theory. In this paper, a heuristic algorithm, which can be embedded into an ABM to allow the agents to find a coalition, is described. Our heuristic algorithm combines agent-based modeling and cooperative game theory to help find agent partitions that are members of a games' core solutions (if they exist). The accuracy of our heuristic algorithm can be determined by comparing its outcomes to the actual core solutions. This comparison is achieved by developing an experiment that uses a specific example of a cooperative game called the glove game. The glove game is a type of market economy game. Finding the traditional cooperative game solutions is computationally intensive for large numbers of players because each possible partition must be compared to each possible coalition to determine the core set; hence our experiment only considers up to nine-player games. The results indicate that our heuristic approach achieves a core solution over 90% of the games considered in our experiment.

Jennifer Badham, Pete Barbrook-Johnson, Camila Caiado and Brian Castellani
Journal of Artificial Societies and Social Simulation 24 (1) 8

Kyeywords: Agent-Based Modelling, Epidemic, COVID-19, Descriptive Model, Social Distancing, Justified Stories
Abstract: This paper presents JuSt-Social, an agent-based model of the COVID-19 epidemic with a range of potential social policy interventions. It was developed to support local authorities in North East England who are making decisions in a fast moving crisis with limited access to data. The proximate purpose of JuSt-Social is description, as the model represents knowledge about both COVID-19 transmission and intervention effects. Its ultimate purpose is to generate stories that respond to the questions and concerns of local planners and policy makers and are justified by the quality of the representation. These justified stories organise the knowledge in way that is accessible, timely and useful at the local level, assisting the decision makers to better understand both their current situation and the plausible outcomes of policy alternatives. JuSt-Social and the concept of justified stories apply to the modelling of infectious disease in general and, even more broadly, modelling in public health, particularly for policy interventions in complex systems.

Lucas Sage and Andreas Flache
Journal of Artificial Societies and Social Simulation 24 (2) 2

Kyeywords: Agent-Based Model, Social Simulation, Segregation, School-Segregation, School-Choice, Discrete-Choice-Model
Abstract: Schelling and Sakoda prominently proposed computational models suggesting that strong ethnic residential segregation can be the unintended outcome of a self-reinforcing dynamic driven by choices of individuals with rather tolerant ethnic preferences. There are only few attempts to apply this view to school choice, another important arena in which ethnic segregation occurs. In the current paper, we explore with an agent-based theoretical model similar to those proposed for residential segregation, how ethnic tolerance among parents can affect the level of school segregation. More specifically, we ask whether and under which conditions school segregation could be reduced if more parents hold tolerant ethnic preferences. We move beyond earlier models of school segregation in three ways. First, we model individual school choices using a random utility discrete choice approach. Second, we vary the pattern of ethnic segregation in the residential context of school choices systematically, comparing residential maps in which segregation is unrelated to parents’ level of tolerance to residential maps reflecting their ethnic preferences. Third, we introduce heterogeneity in tolerance levels among parents belonging to the same group. Our simulation experiments suggest that ethnic school segregation can be a very robust phenomenon, occurring even when about half of the population prefers segregated to mixed schools. However, we also identify a “sweet spot” in the parameter space in which a larger proportion of tolerant parents makes the biggest difference. This is the case when parents have moderate preferences for nearby schools and there is only little residential segregation. Further experimentation unraveled the underlying mechanisms.

Zhongtian Chen and Hanlin Lan
Journal of Artificial Societies and Social Simulation 24 (2) 8

Kyeywords: Opinion Dynamics, Social Media, Polarization, Agent-Based Modeling, Opinion Guidance
Abstract: Studies on the fundamental role of diverse media in the evolution of public opinion can protect us from the spreading of brainwashing, extremism, and terrorism. Many fear the information cocoon may result in polarization of the public opinion. Hence, in this work, we investigate how audiences' choices among diverse media might influence public opinion. Specifically, we aim to figure out how peoples' horizons (i.e., range of available media) and quantity, as well as the distribution of media, may shape the space of public opinion. We propose a novel model of opinion dynamics that considers different influences and horizons for every individual, and we carry out simulations using a real-world social network. Numerical simulations show that diversity in media can provide more choices to the people, although individuals only choose media within the bounds of their horizons, extreme opinions are more diluted, and no opinion polarizations emerge. Furthermore, we find that the distribution of media's opinions can effectively influence the space for public opinion, but when the number of media grows to a certain level, its effect will reach a limitation. Finally, we show that the effect of campaigns for consciousness or education can be improved by constructing the opinion of media, which can provide a basis for the policy maker in the new media age.

Matthew Koehler, David M Slater, Garry Jacyna and James R Thompson
Journal of Artificial Societies and Social Simulation 24 (2) 9

Kyeywords: Agent-Based Modeling, Covid-19, Contact Networks, Non-Pharmaceutical Interventions
Abstract: As a result of the COVID-19 worldwide pandemic, the United States instituted various non-pharmaceutical interventions (NPIs) in an effort to slow the spread of the disease. Although necessary for public safety, these NPIs can also have deleterious effects on the economy of a nation. State and federal leaders need tools that provide insight into which combination of NPIs will have the greatest impact on slowing the disease and at what point in time it is reasonably safe to start lifting these restrictions to everyday life. In the present work, we outline a modeling process that incorporates the parameters of the disease, the effects of NPIs, and the characteristics of individual communities to offer insight into when and to what degree certain NPIs should be instituted or lifted based on the progression of a given outbreak of COVID-19. We apply the model to the 24 county-equivalents of Maryland and illustrate that different NPI strategies can be employed in different parts of the state. Our objective is to outline a modeling process that combines the critical disease factors and factors relevant to decision-makers who must balance the health of the population with the health of the economy.

Jan-Philipp Fränken and Toby Pilditch
Journal of Artificial Societies and Social Simulation 24 (3) 1

Kyeywords: Echo Chambers, Source Credibility, Information Cascades, Agent-Based Modelling, Bayesian Modelling, Single Interaction
Abstract: Investigating how echo chambers emerge in social networks is increasingly crucial, given their role in facilitating the retention of misinformation, inducing intolerance towards opposing views, and misleading public and political discourse. Previously, the emergence of echo chambers has been attributed to psychological biases and inter-individual differences, requiring repeated interactions among network-users and rewiring or pruning of social ties. Using an idealised population of social network users, the present results suggest that when combined with positive credibility perceptions of a communicating source, social media users’ ability to rapidly share information with each other through a single cascade can be sufficient to produce echo chambers. Crucially, we show that this requires neither special psychological explanation (e.g., bias or individual differences), nor repeated interactions—though these may be exacerbating factors. In fact, this effect is made increasingly worse the more generations of peer-to-peer transmissions it takes for information to permeate a network. This raises important questions for social network architects, if truly opposed to the increasing prevalence of deleterious societal trends that stem from echo chamber formation.

J. Gareth Polhill, Matthew Hare, Tom Bauermann, David Anzola, Erika Palmer, Doug Salt and Patrycja Antosz
Journal of Artificial Societies and Social Simulation 24 (3) 2

Kyeywords: Prediction, Complex Systems, Wicked Systems, Agent-Based Modelling, Cellular Automata, Turing Machines
Abstract: This paper uses two thought experiments to argue that the complexity of the systems to which agent-based models (ABMs) are often applied is not the central source of difficulties ABMs have with prediction. We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful. ‘Wickedness’ is argued to be a more significant challenge to prediction than complexity. Critically, however, neither complexity nor wickedness makes prediction theoretically impossible in the sense of being formally undecidable computationally-speaking: intractable being the more apt term given the exponential sizes of the spaces being searched. However, endogenous ontological novelty in wicked systems is shown to render prediction futile beyond the immediately short term.

Arthur Feinberg, Elena Hooijschuur, Nicole Rogge, Amineh Ghorbani and Paulien Herder
Journal of Artificial Societies and Social Simulation 24 (3) 3

Kyeywords: Community Gardens, Agent-Based Model, Institutional Modelling, Theory of Reasoned Action, Design Principles for Collective Action
Abstract: This paper presents an agent-based model that explores the conditions for ongoing participation in community gardening projects. We tested the effects of Ostrom's well-known Design Principles for collective action and used an extensive database collected in 123 cases in Germany and two case studies in the Netherlands to validate it. The model used the Institutional Analysis and Development (IAD) framework and integrated decision mechanisms derived from the Theory of Reasoned Action (TRA). This allowed us to analyse volunteer participation in urban community gardens over time, based on the garden's institutions (Design Principles) and the volunteer's intention to join gardening. This intention was influenced by the volunteer's expectations and past experiences in the garden (TRA). We found that not all Design Principles lead to higher levels of participation but rather, participation depends on specific combinations of the Design Principles. We highlight the need to update the assumption about sanctioning in such systems: sanctioning is not always beneficial, and may be counter-productive in certain contexts.

Ben Vermeulen, Matthias Müller and Andreas Pyka
Journal of Artificial Societies and Social Simulation 24 (3) 6

Kyeywords: Epidemic, Agent-Based Model, Policy Laboratory, COVID-19, Coronavirus
Abstract: We present and use an agent-based model to study interventions for suppression, mitigation, and vaccination in coping with the COVID-19 pandemic. Unlike metapopulation models, our agent-based model permits experimenting with micro-level interventions in social interactions at individual sites. We compare common macro-level interventions applicable to everyone (e.g., keep distance, close all schools) to targeted interventions in the social network spanned by households based on specific (potential) transmission rates (e.g., prohibit visiting spreading hubs or bridging ties). We show that, in the simulation environment, micro-level measures of 'locking' of a number of households and ‘blocking’ access to a number of sites (e.g., workplaces, schools, recreation areas) using social network centrality metrics permits refined control on the positioning on the immunity-mortality curve. In simulation results, social network metric-based vaccination of households offers refined control and reduces the spread saliently better than random vaccination.

Ying Wang, Qi Zhang, Srikanta Sannigrahi, Qirui Li, Shiqi Tao, Richard Bilsborrow, Jiangfeng Li and Conghe Song
Journal of Artificial Societies and Social Simulation 24 (3) 7

Kyeywords: Spatially Explicit Agent-Based Model, Social-Ecological Systems, Land Use, Labor Allocation, Agro-Environmental Policies
Abstract: Understanding household labor and land allocation decisions under agro-environmental policies is challenging due to complex human-environment interactions. Here, we developed a spatially explicit agent-based model based on spatial and socioeconomic data to simulate households’ land and labor allocation decisions and investigated the impacts of two forest restoration and conservation programs and one agricultural subsidy program in rural China. Simulation outputs revealed that the forest restoration program accelerates labor out-migration and cropland shrink, while the forest conservation program promotes livelihood diversification via increasing non-farm employment. Meanwhile, the agricultural subsidy program keeps labor for cultivation on land parcels with good quality, but appears less effective for preventing marginal croplands from being abandoned. The policy effects on labor allocation substantially differ between rules based on bounded rational and empirical knowledge of defining household decisions, particularly on sending labor out-migrants and engaging in local off-farm jobs. Land use patterns showed that the extent to which households pursue economic benefits through shrinking cultivated land is generally greater under bounded rationality than empirical knowledge. Findings demonstrate nonlinear social-ecological impacts of the agro-environmental policies through time, which can deviate from expectations due to complex interplays between households and land. This study also suggests that the spatial agent-based model can represent adaptive decision-making and interactions of human agents and their interactions in dynamic social and physical environments.

Kasper Lange, Gijsbert Korevaar, Igor Nikolic and Paulien Herder
Journal of Artificial Societies and Social Simulation 24 (3) 8

Kyeywords: Circular Economy, Industrial Symbiosis, Cooperative Networks, Agent-Based Modelling, Theory of Planned Behaviour, Eco-Oriented Behaviour
Abstract: Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.

Matthew Gibson, Raphael Slade, Joana Portugal Pereira and Joeri Rogelj
Journal of Artificial Societies and Social Simulation 24 (3) 9

Kyeywords: Food Choice, Milk Consumption, Consumer Behaviour, Agent-Based Modelling, Calibration Optimisation, Global Temporal Sensitivity Analysis
Abstract: Substitution of food products will be key to realising widespread adoption of sustainable diets. We present an agent-based model of decision-making and influences on food choice, and apply it to historically observed trends of British whole and skimmed (including semi) milk consumption from 1974 to 2005. We aim to give a plausible representation of milk choice substitution, and test different mechanisms of choice consideration. Agents are consumers that perceive information regarding the two milk choices, and hold values that inform their position on the health and environmental impact of those choices. Habit, social influence and post-decision evaluation are modelled. Representative survey data on human values and long-running public concerns empirically inform the model. An experiment was run to compare two model variants by how they perform in reproducing these trends. This was measured by recording mean weekly milk consumption per person. The variants differed in how agents became disposed to consider alternative milk choices. One followed a threshold approach, the other was probability based. All other model aspects remained unchanged. An optimisation exercise via an evolutionary algorithm was used to calibrate the model variants independently to observed data. Following calibration, uncertainty and global variance-based temporal sensitivity analysis were conducted. Both model variants were able to reproduce the general pattern of historical milk consumption, however, the probability-based approach gave a closer fit to the observed data, but over a wider range of uncertainty. This responds to, and further highlights, the need for research that looks at, and compares, different models of human decision-making in agent-based and simulation models. This study is the first to present an agent-based modelling of food choice substitution in the context of British milk consumption. It can serve as a valuable pre-curser to the modelling of dietary shift and sustainable product substitution to plant-based alternatives in Britain.

Lígia Mori Madeira, Bernardo Alves Furtado and Alan Dill
Journal of Artificial Societies and Social Simulation 24 (4) 1

Kyeywords: Domestic Violence, Violence Against Women, Agent-Based Models, Pandemics, Simulation, Metropolitan Regions
Abstract: Violence against women occurs predominantly in the family and domestic context. The COVID-19 pandemic has led Brazil to recommend and at times, impose social distancing, with the partial closure of economic activities, schools, and restrictions on events and public services. Preliminary evidence shows that intense coexistence increases domestic violence, while social distancing measures may have prevented access to public services and networks, information, and help. We propose an agent-based model (ABM), called VIDA, to formalize and illustrate a multitude of factors that influence events which could trigger violence. A central part of the model is the construction of a stress indicator, created as a probability trigger of domestic violence occurring within the family environment. Having a formal model that replicates observed patterns of violence based on internal familial characteristics enables us to experiment with altering dynamics. We first tested the (a) absence or presence of the deterrence system of domestic violence against women and then (b) the existence of measures to increase social distancing. VIDA presents comparative results for metropolitan regions and neighborhoods considered in the experiments. Results suggest that social distancing measures, particularly those encouraging staying at home, may have increased domestic violence against women by about 10%. VIDA suggests further that more populated areas have comparatively fewer cases per hundred thousand women than less populous capitals or rural areas of urban concentrations. This paper contributes to the literature by formalizing, to the best of our knowledge, the first model of domestic violence through agent-based modeling, using empirical detailed socioeconomic, demographic, educational, gender, and race data at the intraurban (census sectors) and household level.

Amin Boroomand and Paul E. Smaldino
Journal of Artificial Societies and Social Simulation 24 (4) 10

Kyeywords: Teams, NK Landscape, Risk, Collective Decision Making, Agent-Based Model
Abstract: We studied an agent-based model of collective problem solving in which teams of agents search on an NK landscape and share information about newly found solutions. We analyzed the effects of team members’ behavioral strategies, team size, and team diversity on overall performance. Depending on the landscape complexity and a team’s features a team may eventually find the best possible solution or become trapped at a local maximum. Hard-working agents can explore more solutions per unit time, while risk-taking agents inject randomness in the solutions they test. We found that when teams solve complex problems, both strategies (risk-taking and hard work) have positive impacts on the final score, and the positive effect of moderate risk-taking is substantial. However, risk-taking has a negative effect on how quickly a team achieves its final score. If time restrictions can be relaxed, a moderate level of risk can produce an improved score. If the highest priority is instead to achieve the best possible score in the shortest amount of time, the hard work strategy has the greatest impact. When problems are simpler, risk-taking behavior has a negative effect on performance, while hard work decreases the time required to solve the problem. We also find that larger teams generally solved problems more effectively, and that some of this positive effect is due to the increase in diversity. We show more generally that increasing the diversity of teams has a positive impact on the team’s final score, while more diverse teams also require less time to reach their final solution. This work contributes overall to the larger literature on collective problem solving in teams.

Matthew Sottile, Richard Iles, Craig McConnel, Ofer Amram and Eric Lofgren
Journal of Artificial Societies and Social Simulation 24 (4) 11

Kyeywords: Agent-Based Model, Random Field Ising Model, Livestock Health, Rift Valley Fever, Contagious Bovine Pleuropneumonia, Economic Decision Making
Abstract: Economic and cultural resilience among pastoralists in East Africa is threatened by the interconnected forces of climate change, contagious diseases spread and evolving national and international trade. A key factor in the resilience of livestock that communities depend on is human decision making regarding vaccination against prevalent diseases such as Rift Valley fever and Contagious Bovine Pleuropneumonia. This paper describes an agent-based model that couples models of disease propagation, animal health, human decision making, and external GIS data sources capturing measures of foraging condition. We describe the design of the sub-models, their coupling, and demonstrate the sensitivity of the model to parameters that relate to controllable factors such as government and NGO information sources that can influence human decision making patterns. This model is intended to form the basis upon which richer economic and human factor models can be built.

JoAnn Lee and Andrew Crooks
Journal of Artificial Societies and Social Simulation 24 (4) 2

Kyeywords: Agent-Based Modeling, Antisocial Behaviors, Delinquency, Risk Factors, Youth, Social Work
Abstract: Risk assessments are designed to measure cumulative risk and promotive factors for delinquency and recidivism, and are used by criminal and juvenile justice systems to inform sanctions and interventions. Yet, these risk assessments tend to focus on individual risk and often fail to capture each individual’s environmental risk . This paper presents an agent-based model (ABM) which explores the interaction of individual and environmental risk on the youth. The ABM is based on an interactional theory of delinquency and moves beyond more traditional statistical approaches used to study delinquency that tend to rely on point-in-time measures, and to focus on exploring the dynamics and processes that evolve from interactions between agents (i.e., youths) and their environments. Our ABM simulates a youth’s day, where they spend time in schools, their neighborhoods, and families. The youth has proclivities for engaging in prosocial or antisocial behaviors , and their environments have likelihoods of presenting prosocial or antisocial opportunities. Results from systematically adjusting family, school, and neighborhood risk and promotive levels suggest that environmental risk and promotive factors play a role in shaping youth outcomes. As such the model shows promise for increasing our understanding of delinquency.

Ngan Nguyen, Hongfei Chen, Benjamin Jin, Walker Quinn, Conrad Tyler and Adam Landsberg
Journal of Artificial Societies and Social Simulation 24 (4) 5

Kyeywords: Cultural Dissemination, Agent-Based Modeling, Cultural Evolution, Opinion Dynamics, Cultural Transmission, Bounded Confidence Models
Abstract: We study cultural dissemination in the context of an Axelrod-like agent-based model describing the spread of cultural traits across a society, with an added element of social influence. This modification produces absorbing states exhibiting greater variation in number and size of distinct cultural regions compared to the original Axelrod model, and we identify the mechanism responsible for this amplification in heterogeneity. We develop several new metrics to quantitatively characterize the heterogeneity and geometric qualities of these absorbing states. Additionally, we examine the dynamical approach to absorbing states in both our Social Influence Model as well as the Axelrod Model, which not only yields interesting insights into the differences in behavior of the two models over time, but also provides a more comprehensive view into the behavior of Axelrod's original model. The quantitative metrics introduced in this paper have broad potential applicability across a large variety of agent-based cultural dissemination models.

Shaoni Wang, Kees Zoethout, Wander Jager and Yanzhong Dang
Journal of Artificial Societies and Social Simulation 24 (4) 9

Kyeywords: Individual Needs, Motivation, Group Performance, Self-Organisation, Task Allocation, Agent-Based Modelling
Abstract: Team performance can be considered a macro-level outcome that depends on three sets of micro-level factors: individual workers contributing to the task, team composition, and task characteristics. For a number of reasons, the complex dynamics between individuals in the task allocation process are difficult to systematically explore in traditional experimental settings: the motivational dynamics, the complex dynamics of task allocation processes, and the lack of experimental control over team composition imply an ABM-approach being more feasible. For this reason, we propose an updated version of the WORKMATE model that has been developed to explore the dynamics of team performance. In doing so, we added Deci and Ryan’s SDT theory, stating that people are motivated by three psychological needs, competence, autonomy, and belongingness. This paper is aimed at explaining the architecture of the model, and some first simulation runs as proof of concept. The experimental results show that: 1) an appropriate motivation threshold will help the team have the lowest performance time; 2) the time needed for the task allocation process is related to the importance of different motivations; 3) highly satisfied teams are more likely composed of members valuing autonomy.

Amir Hosein Afshar Sedigh, Martin Purvis, Tony Bastin Roy Savarimuthu, Christopher Konstantin Frantz and Maryam Purvis
Journal of Artificial Societies and Social Simulation 25 (1) 1

Kyeywords: Apprenticeship, Agent-Based Modelling, Social Simulations, Comparative Systems, Institutions, Historical Systems
Abstract: In this paper, we investigate the effects of different characteristics of apprenticeship programmes both in historical and contemporary societies. Apprenticeship is one of the major means to transfer skills in a society. We consider five societies: the Old Britain system (AD 1300s−1600s), the British East India Company (AD 1600s − 1800s), Armenian merchants of New-Julfa (AD 1600s − 1700s), contemporary German apprenticeship (1990s), and the “Modern Apprenticeship” in Britain (2001). In comparing these systems, using an agent-based simulation model, we identified six characteristics which impact the success of an apprenticeship programme in a society, which we measured by considering three parameters, namely the number of skilled agents produced by the apprenticeships, programme completion, and the contribution of programmes to the Gross Domestic Income (GDI) of the society. We investigate different definitions for success of an apprenticeship and some hypothetical societies to test some common beliefs about apprenticeships' performance. The simulations suggest that a) it is better to invest in a public educational system rather than subsidising private contractors to train apprentices, b) having a higher completion ratio for apprenticeship programme does not necessarily result in a higher contribution in the GDI, and c) governors (e.g. mayors or government) that face significant emigration should also consider employing policies that persuade apprentices to complete their programme and stay in the society after completion to improve apprenticeship efficacy.

Sebastián Pessah, Diego Omar Ferraro, Daniela Blanco and Rodrigo Castro
Journal of Artificial Societies and Social Simulation 25 (1) 5

Kyeywords: Land Use Change, Agent-Based Models, Cropping Systems, Emergy, Cell-DEVS
Abstract: Changes in agricultural systems are a multi-causal process involving climate change, globalization and technological change. These complex interactions regulate the landscape transformation process by imposing land use and cover change (LUCC) dynamics. In order to better understand and forecast the LUCC process we developed a spatially explicit agent-based model in the form of a Cellular Automata: the AgroDEVS model. The model was designed to project viable LUCC dynamics along with their associated economic and environmental changes. AgroDEVS is structured with behavioral rules and functions representing a) crop yields, b) weather conditions, c) economic profits, d) farmer preferences, e) adoption of technology levels and f) natural resource consumption based on embodied energy accounting. Using data from a typical location of the Pampa region (Argentina) for the period 1988-2015, simulation exercises showed that economic goals were achieved, on average, each 6 out of 10 years, but environmental thresholds were only achieved in 1.9 out of 10 years. In a set of 50-years simulations, LUCC patterns converge quickly towards the most profitable crop sequences, with no noticeable trade-off between economic and environmental conditions.

Bernardo Alves Furtado
Journal of Artificial Societies and Social Simulation 25 (1) 8

Kyeywords: Public Policies, Real Estate Market, Agent-Based Modeling, Simulation, Spatial Analysis, Metropolitan Regions
Abstract: Policymakers' role in decision making on alternative policies is facing restricted budgets and an uncertain future. The need to decide on priorities and handle effects across policies has made their task even more difficult. For instance, housing policies involve heterogeneous characteristics of the properties themselves and the intricacy of housing markets within the spatial context of cities. Here, we have proposed PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model. PS2 is a computer simulation that relies on empirically detailed spatial data to model real estate, along with labor, credit, and goods and services markets. Interaction among workers, firms, a bank, households and municipalities follow the literature benchmarks by integrating economic, spatial and transport research. PS2 is applied here as a comparison of three competing public policies aimed at reducing inequality and alleviating poverty: (a) house acquisition by the government and distribution to lower income households, (b) rental vouchers and (c) monetary aid. Within the model context, monetary aid, that is smaller amounts of help for a larger number of households, improves the economy in terms of production, consumption, reduction of inequality and maintenance of financial duties. PS2 is also a framework that can be further adapted to a number of related research questions.

Josie McCulloch, Jiaqi Ge, Jonathan Ward, Alison Heppenstall, J. Gareth Polhill and Nicolas Malleson
Journal of Artificial Societies and Social Simulation 25 (2) 1

Kyeywords: Calibration, Optimisation, History Matching, Proximate Bayesian Computation, Uncertainty, Agent-Based Modelling
Abstract: Agent-based models (ABMs) can be found across a number of diverse application areas ranging from simulating consumer behaviour to infectious disease modelling. Part of their popularity is due to their ability to simulate individual behaviours and decisions over space and time. However, whilst there are plentiful examples within the academic literature, these models are only beginning to make an impact within policy areas. Whilst frameworks such as NetLogo make the creation of ABMs relatively easy, a number of key methodological issues, including the quantification of uncertainty, remain. In this paper we draw on state-of-the-art approaches from the fields of uncertainty quantification and model optimisation to describe a novel framework for the calibration of ABMs using History Matching and Approximate Bayesian Computation. The utility of the framework is demonstrated on three example models of increasing complexity: (i) Sugarscape to illustrate the approach on a toy example; (ii) a model of the movement of birds to explore the efficacy of our framework and compare it to alternative calibration approaches and; (iii) the RISC model of farmer decision making to demonstrate its value in a real application. The results highlight the efficiency and accuracy with which this approach can be used to calibrate ABMs. This method can readily be applied to local or national-scale ABMs, such as those linked to the creation or tailoring of key policy decisions.

Matthew Gibson, Joana Portugal Pereira, Raphael Slade and Joeri Rogelj
Journal of Artificial Societies and Social Simulation 25 (2) 3

Kyeywords: Plant-Based Milk, Dairy Reduction, Sustainable Diets, Agent-Based Modelling, Calibration, Scenario Analysis
Abstract: A reduction in the production and consumption of meat and dairy across much of the world is critical for climate change mitigation, the alleviation of ecological stress, and improved health. We update an agent-based model (ABM) of historic UK milk consumption and apply it to scenarios of dairy reduction and adoption of plant-based milk (PBM) out to 2050. The updated model is comprised of a cognitive function, where agents perceive the physical, health and environmental characteristics of milk choice, which is modified by habit and social influence. We use European Social Survey 2018 and British Social Attitudes 2008 survey data to empirically inform the model. Taking a backcasting approach, we calibrate parameters against published UK dairy reduction targets (2030 and 2050), and test how different price relationships, and characterisations of environmental concern, may affect simulated milk consumption from 2020 to 2050. Scenarios for core targets (20% less dairy by 2030 and 35% by 2050) largely produced plausible consumption trajectories. However, at current pricing of dairy and PBM, simulated consumption was mostly unable to deliver on desired core targets, but this improved markedly with dairy prices set to organic levels. The influence of changing environmental concern on milk choice resulted in higher levels of dairy milk reduction. When modelled as transient, intense shocks to public concern, consumption patterns did not fundamentally change. However, small, incremental but permanent changes to concern did produce structural changes to consumption patterns, with dairy falling below plant-based alternatives at around 2030. This study is the first to apply an ABM in the context of scenarios for dairy reduction and PBM adoption in service to UK climate-related consumption targets. It can serve as valuable bottom-up, alternative, evidence on the feasibility of dietary shift targets, and poses policy implications for how to address impediments to behavioural change.

Anna Pagani, Francesco Ballestrazzi, Emanuele Massaro and Claudia R. Binder
Journal of Artificial Societies and Social Simulation 25 (2) 4

Kyeywords: Household Mobility, Household Relocation, Housing, Human-Environment Systems, Sustainability, Agent-Based Modelling
Abstract: Sustainable housing is a key priority for Switzerland. To provide both environmentally and socio-culturally sustainable housing, Swiss property owners need to navigate the complex and context-specific system that articulates the match between households’ preferences and the dwellings available to them-i.e. residential mobility. In response to this need, this paper outlines ReMoTe-S, an agent-based model of tenants’ residential mobility in Switzerland. The model design is based on empirical research conducted with the tenants of three multifamily housing providers. It accounts for the life course of dwellings and households, during which the latter attempt to maximise their satisfaction, which is calculated as the correspondence between their desired housing functions (e.g. a status symbol) and the functions of dwellings. To illustrate the model’s potential uses, we explore the sensitivity of its outputs to changes in dwellings’ and buildings’ qualitative and quantitative features by looking at two key indicators of housing sustainability: floor space per capita and vacancy rate. We firstly observe that a supply dominated by medium-to-large dwellings and the application of less strict occupancy rules can result in housing underoccupancy. Secondly, it emerges that certain combinations of housing features engender a lower vacancy rate inasmuch as they more successfully generate housing functions. We conclude that by enabling housing providers to explore the complex human-environment interactions of the housing system, ReMoTe-S can be used to inform a sustainable management of housing stock.

Woi Sok Oh, Álvaro Carmona-Cabrero, Rafael Muñoz-Carpena and Rachata Muneepeerakul
Journal of Artificial Societies and Social Simulation 25 (2) 7

Kyeywords: Agent-Based Model, Human Migration, Factor Configuration, Decision-Making Process, Social Ties
Abstract: Many researchers have addressed what factors should be included in their models of coupled natural-human systems (CNHSs). However, few studies have explored how these factors should be incorporated (factor configuration). Theoretical underpinning of the factor configuration may lead to a better understanding of systematic patterns and sustainable CNHS management. In particular, we ask: (1) can factor configuration explain CNHS behaviors based on its theoretical implications? and (2) when disturbed by shocks, do CNHSs respond differently under varying factor configurations? A proof-of-concept migration agent-based model (ABM) was developed and used as a platform to investigate the effects of factor configuration on system dynamics and outcomes. Here, two factors, social ties and water availability, were assumed to have alternative substitutable, complementary, or adaptable relationships in influencing migration decisions. We analyzed how populations are distributed over different regions along a water availability gradient and how regions are culturally mixed under different factor configurations. We also subjected the system to a shock scenario of dropping 50% of water availability in one region. We found that substitutability acted as a bu er against the effect of water deficiency and prevented cultural mixing of the population by keeping residents in their home regions and slowing down residential responses against the shock. Complementarity led to the sensitive migration behavior of residents, accelerating regional migration and cultural mixing. Adaptability caused residents to stay longer in new regions, which gradually led to a well-mixed cultural condition. All together, substitutability, complementarity, and adaptability gave rise to different emergent patterns. Our findings highlight the importance of how, not just what, factors are included in a CNHS ABM, a lesson that is particularly applicable to models of interdisciplinary problems where factors of diverse nature must be incorporated.

Alexander Michels, Jeon-Young Kang and Shaowen Wang
Journal of Artificial Societies and Social Simulation 25 (2) 8

Kyeywords: Agent-Based Modeling, Particle Swarm Optimization, Calibration, CyberGIS, Influenza
Abstract: A challenge in computational Agent-Based Models (ABMs) is the amount of time and resources required to tune a set of parameters for reproducing the observed patterns of phenomena being modeled. Well-tuned parameters are necessary for models to reproduce real-world multi-scale space-time patterns, but calibration is often computationally intensive and time consuming. Particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm that has found wide use for complex optimization including nonconvex and noisy problems. In this study, we propose to use PSO for calibrating parameters in ABMs. We use a spatially explicit ABM of influenza transmission based in Miami, Florida, USA as a case study. Furthermore, we demonstrate that a standard implementation of PSO can be used out-of-the-box to successfully calibrate models and out-performs Monte Carlo in terms of optimization and efficiency.

Tim G Williams, Daniel G Brown, Seth D Guikema, Tom M Logan, Nicholas R Magliocca, Birgit Müller and Cara E Steger
Journal of Artificial Societies and Social Simulation 25 (3) 1

Kyeywords: Agent-Based Model, Fairness, Justice, Reflexivity, Best Practice, Simulation
Abstract: Advancing equity is a complex challenge for society, science, and policy. Agent-based models are increasingly used as scientific tools to advance understanding of systems, inform decision-making, and share knowledge. Yet, equity has not received due attention within the agent-based modeling (ABM) literature. In this paper, we develop a conceptual framework and provide guidance for integrating equity considerations into ABM research and modeling practice. The framework conceptualizes ABM as interfacing with equity outcomes at two levels (the science-society interface and within the model itself) and the modeler as a filter and lens that projects knowledge between the target system and the model. Within the framework, we outline three complementary, equity-advancing action pathways: (1) engage stakeholders, (2) acknowledge positionality and bias, and (3) assess equity with agent-based models. For Pathway 1, we summarize existing guidance within the participatory modeling literature. For Pathway 2, we introduce the positionality and bias document as a tool to promote modeler and stakeholder reflexivity throughout the modeling process. For Pathway 3, we synthesize a typology of approaches for modeling equity and offer a set of preliminary suggestions for best practice. By engaging with these action pathways, modelers both reduce the risks of inadvertently perpetuating inequity and harness the opportunities for ABM to play a larger role in creating a more equitable future.

D. Cale Reeves, Nicholas Willems, Vivek Shastry and Varun Rai
Journal of Artificial Societies and Social Simulation 25 (3) 3

Kyeywords: Agent-Based Model, Diffusion Model, Empirical Data-Driven Model, Heterogeneous Population, Model Performance, COVID-19
Abstract: Modeling human behavior in the context of social systems in which we are embedded realistically requires capturing the underlying heterogeneity in human populations. However, trade-offs associated with different approaches to introducing heterogeneity could either enhance or obfuscate our understanding of outcomes and the processes by which they are generated. Thus, the question arises: how to incorporate heterogeneity when modeling human behavior as part of population-scale phenomena such that greater understanding is obtained? We use an agent-based model to compare techniques of introducing heterogeneity at initialization or generated during the model’s runtime. We show that initializations with unstructured heterogeneity can interfere with a structural understanding of emergent processes, especially when structural heterogeneity might be a key part of driving how behavioral responses dynamically shape emergence in the system. We find that incorporating empirical population heterogeneity – even in a limited sense – can substantially contribute to improved understanding of how the system under study works.

Florian Kotthoff and Thomas Hamacher
Journal of Artificial Societies and Social Simulation 25 (3) 4

Kyeywords: Agent-Based Modeling, Multi-Agent Simulation, Innovation Diffusion, Adoption Model, Decision Making, Calibration
Abstract: Consumer behavior and the decision to adopt an innovation are governed by various motives, which models find difficult to represent. A promising way to introduce the required complexity into modeling approaches is to simulate all consumers individually within an agent-based model (ABM). However, ABMs are complex and introduce new challenges. Especially the calibration of empirical ABMs was identified as a key difficulty in many works. In this work, a general ABM for simulating the Diffusion of Innovations is described. The ABM is differentiable and can employ gradient-based calibration methods, enabling the simultaneous calibration of large numbers of free parameters in large-scale models. The ABM and calibration method are tested by fitting a simulation with 25 free parameters to the large data set of privately owned photovoltaic systems in Germany, where the model achieves a coefficient of determination of R² ≃ 0.7.

Marcos Pinheiro
Journal of Artificial Societies and Social Simulation 25 (3) 5

Kyeywords: Hunter-Gatherers, Food Sharing, Evolution of Cooperation, Egalitarianism, Agent-Based Model
Abstract: Among social anthropologists, there is virtual consensus that the food-sharing practices of small-scale non-agricultural groups cannot be understood in isolation from the broader repertoire of leveling strategies that prevent would-be dominants from exercising power and influence over likely subordinates. In spite of that widespread view, quantitatively rigorous empirical studies of food sharing and cooperation in small-scale human groups have typically ignored the internal connection between leveling of income and political power, drawing inspiration instead from evolutionary models that are neutral about social role asymmetries. In this paper, I introduce a spatially explicit agent-based model of hunter-gatherer food sharing in which individuals are driven by the goal of maximizing their own income while minimizing income asymmetries among others. Model simulation results show that seven basic patterns of inter-household food transfers described in detail for the Hadza hunters of Tanzania can be simultaneously reproduced with striking accuracy under the assumption that agents selectively support and carry on sharing interactions in ways that maximize their income leveling potential.

Kurt Kreulen, Bart de Bruin, Amineh Ghorbani, René Mellema, Christian Kammler, Lois Vanhée, Virginia Dignum and Frank Dignum
Journal of Artificial Societies and Social Simulation 25 (3) 6

Kyeywords: COVID-19, Agent-Based Modelling, Culture, Values, Epidemiological Models, Pandemic
Abstract: Since its first appearance in Wuhan (China), countries have been employing, to varying degrees of success, a series of non-pharmaceutical interventions aimed at limiting the spread of SARS-CoV-2 within their populations. In this article, we build on scientific work that demonstrates that culture is part of the explanation for the observed variability between countries in their ability to effectively control the transmission of SARS-CoV-2. We present a theoretical framework of how culture influences decision-making at the level of the individual. This conceptualization is formalized in an agent-based model that simulates how cultural factors can combine to produce differences across populations in terms of the behavioral responses of individuals to the COVID-19 crisis. We illustrate that, within our simulated environment, the culturally-dependent willingness of people to comply with public health related measures might constitute an important determinant of differences in infection dynamics across populations. Our model generates the highest rates of non-compliance within cultures marked as individualist, progressive and egalitarian. Our model illustrates the potential role of culture as a population-level predictor of infections associated with COVID-19. In doing so, the model, and theoretical framework on which it is based, may inform future studies aimed at incorporating the effect of culture on individual decision-making processes during a pandemic within social simulation models.

David Anzola, Pete Barbrook-Johnson and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 25 (4) 1

Kyeywords: Agent-Based Modelling, Research Ethics, Ethical Standards, Responsible Science, Scientific Integrity, Code of Ethics
Abstract: The academic study and the applied use of agent-based modelling of social processes has matured considerably over the last thirty years. The time is now right to engage seriously with the ethics and responsible practice of agent-based social simulation. In this paper, we first outline the many reasons why it is appropriate to explore an ethics of agent-based modelling and how ethical issues arise in its practice and organisation. We go on to discuss different approaches to standardisation as a way of supporting responsible practice. Some of the main conclusions are organised as provisions in a draft code of ethics. We intend for this draft to be further developed by the community before being adopted by individuals and groups within the field informally or formally

Magnus Moglia, Christian A Nygaard, Stephen Glackin, Stephen Cook and Sorada Tapsuwan
Journal of Artificial Societies and Social Simulation 25 (4) 2

Kyeywords: Energy Transition, Renewable Energy, Sustainability, Agent-Based Model, Climate Mitigation, Socio-Technical Transition
Abstract: Understanding the processes of residential solar PV uptake is critical to developing planning and policy energy transition pathways. This paper outlines a novel hybrid Agent-Based-Modelling/statistical adoption prediction framework that addresses several drawbacks in current modelling approaches. Specifically, we extend the capabilities of similar previous models and incorporate empirical data, behavioural theory, social networks and explicitly considers the spatial context. We provide empirical data affecting households’ propensity to adopt, including perceptions of solar PV systems, the role of tenure and urban location. We demonstrate the approach in the context of Melbourne metropolitan region, Australia; and draw on housing approval data to demonstrate the role of housing construction in accelerating adoption. Finally, we explore the approach’s validity against real-world data with promising results that also indicate key areas for further research and improvement.

Marco A. Janssen, Daniel DeCaro and Allen Lee
Journal of Artificial Societies and Social Simulation 25 (4) 3

Kyeywords: Common-Pool Resource, Trust, Communication, Agent-Based Model, Laboratory Experiment
Abstract: An agent-based model is presented that aims to capture the involvement of inequality and trust in collective action in a classic commons dilemma before, during, and after communication. The model assumptions are based on the behavioral theory of collective action of Elinor Ostrom and the ‘humanistic rational choice theory’. The commons dilemma is represented as a spatially explicit renewable resource. Agent’s trust in others has an impact on the harvesting of shared resources, and trust is influenced by observed harvesting behavior and cheap talk. We calibrated the model using data from a prior set of lab experiments on inequality, trust, and communication. The best fit to the data consists of a population with a small share of altruistic and selfish agents and a majority of conditional cooperative agents sensitive to inequality and who would cooperate if others did. Communication increased trust explaining the better group performance when communication was introduced. The modeling results complement prior communication research and clarify the dynamics of reciprocal cooperation commonly observed in robust resource governance systems.

Tae-Sub Yun, Dongjun Kim, Il-Chul Moon and Jang Won Bae
Journal of Artificial Societies and Social Simulation 25 (4) 5

Kyeywords: Urban Traffic Simulation, Agent-Based Modeling, Traffic Dispersion Effect, Policy Analysis, Microscopic Analysis
Abstract: From the perspective of urban administration, simulation can be used as an evaluation tool. Specifically, it can provide an intuition to the current urban situation and quantitatively verify the effectiveness of urban policies. This study proposes a traffic simulation model for Sejong city in South Korea. The proposed model is developed as an agent-based model, which describes the movement behaviors of individual agents representing the whole population in the real city. In particular, to evaluate city-level administrative effects, the proposed model incorporates the multiple distributions of city reality by combining various types of observed real data. By aggregating the individual-level movement behaviors, the proposed model generates the demand for the city's transportation system, and the generated traffic demands were statistically validated with the real data. Based on the secured validity, we conducted a case study where the proposed model was used to compare and analyze the effect of traffic dispersion by taking the policy candidates of new bridge construction into account. From the policy experiment results, we discovered policy implications on an effective bridge construction. Furthermore, we found methodological implications of the urban transport model from the microscopic analysis, which is enabled by the virtue of the proposed model structure.

Vittorio Nespeca, Tina Comes and Frances Brazier
Journal of Artificial Societies and Social Simulation 26 (1) 10

Kyeywords: Research Design, Simulation Methodology, Empirical Agent-Based Models, Information Diffusion, Information Management, Crisis Management
Abstract: Qualitative research is a powerful means to capture human interactions and behavior. Although there are different methodologies to develop models based on qualitative research, a methodology is missing that enables to strike a balance between the comparability across cases provided by methodologies that rely on a common and context-independent framework and the flexibility to study any policy problem provided by methodologies that focus on capturing a case study without relying on a common framework. Additionally, a rigorous methodology is missing that enables the development of both theoretical and empirical models for supporting policy formulation and evaluation with respect to a specific policy problem. In this article, the authors propose a methodology targeting these gaps for ABMs in two stages. First, a novel conceptual framework centered on a particular policy problem is developed based on existing theories and qualitative insights from one or more case studies. Second, empirical or theoretical ABMs are developed based on the framework and generic models. This methodology is illustrated by an example application for disaster information management in Jakarta, resulting in an empirical descriptive agent-based model.

Marius von Essen and Eric F Lambin
Journal of Artificial Societies and Social Simulation 26 (1) 5

Kyeywords: Land Use Change, Socio-Ecological Systems, Tropical Forests, Environmental Governance, Sustainable Resource Use, Agent-Based Modeling
Abstract: Well-designed land use governance that involves multiple stakeholders is crucial to reducing deforestation in tropical commodity frontiers. The effectiveness of different policy mixes is difficult to assess due to long implementation times and challenges to conducting real-world experiments. Here we introduce an agent-based simulation of land use governance (ABSOLUG) to examine the interactions among governments, commodity producers, and civil society and assess the impacts of different land use governance approaches on deforestation. The model represents a generic commodity producing landscape in the tropics with a central marketplace and features four groups of agents: largeholders, smallholders, NGOs, and a government. The objective of largeholders and smallholders is to generate profits through the production of commodity crops. Statistical evaluation through local and global sensitivity analyses shows that the model is robust, and few parameters show threshold behaviors. We used a hands-off and a proactive-government scenario to evaluate the model operationally. The hands-off scenario was inspired by high rates of tropical deforestation in the second half of the 20th century and the pro-active government scenario by a few recent cases of forest transition countries. The hands-off scenario led to quasi-complete deforestation of the landscape at the end of the simulation period. Deforestation in the proactive-government scenario decreased and eventually stopped in the second half of the simulation period, followed by reforestation.

Charles Koll, Michael Lindell, Chen Chen and Haizhong Wang
Journal of Artificial Societies and Social Simulation 26 (1) 7

Kyeywords: Emergency Warnings, Multiplex Social Networks, Official Broadcasts, Information Contagion, Agent-Based Models
Abstract: Disasters vary in many characteristics, but their amount of forewarning—the amount of time remaining until the disaster strikes—is a crucial factor affecting the dissemination of emergency warnings. People can be warned by public safety officials through broadcast channels, such as commercial TV and radio, that transmit simultaneous warnings to mass audiences. In addition, however, warnings are also transmitted by peers through informal warning networks that operate through contagion from one person to another. This paper establishes an interdisciplinary agent-based model with Monte Carlo simulations to assess the relative effects of these broadcast and contagion processes in a multiplex social network. This multiplex approach models multiple channels of informal communication—phone, word-of-mouth, and social media—that vary in their attribute values. Each agent is an individual in a threatened community who, once warned, has a probability of warning others in their social network using one of these channels. The probability of an individual warning others is based on their warning source and the time remaining until disaster impact, among other variables. We model warning dissemination using simulation parameter values chosen from empirical studies of disaster warnings along with the spatial aspects of the Coos Bay, OR, USA and Seaside, OR, USA communities. Results indicate that the initial broadcast size has a negative correlation with the critical percolation threshold, which varies from approximately 1–5%, depending on the size of an initial broadcast. A sensitivity analysis on the model parameters indicates that, along with initial broadcast size and sharing probability, forewarning and confidence in the warning significantly affect the total number of warning recipients. The results generated from this study identify areas for future research and can inform community officials about the effects of event and community characteristics on the dissemination of emergency warnings in their communities.

Molood Ale Ebrahim Dehkordi, Jonas Lechner, Amineh Ghorbani, Igor Nikolic, Emile Chappin and Paulien Herder
Journal of Artificial Societies and Social Simulation 26 (1) 9

Kyeywords: Machine Learning, Agent-Based Modelling, Modelling Purpose, Structured Literature Review, Guidelines
Abstract: Agent-based modelling and simulation (ABMS), whether simple toy models or complex data-driven ones, is regularly applied in various domains to study the system-level patterns arising from individual behaviour and interactions. However, ABMS still faces diverse challenges such as modelling more representative agents or improving computational efficiency. Research shows that machine learning (ML) techniques, when used in ABMS can address such challenges. Yet, the ABMS literature is still marginally leveraging the benefits of ML. One reason is the vastness of the ML domain, which makes it difficult to choose the appropriate ML technique to overcome a specific modelling challenge. This paper aims to bring ML more within reach of the ABMS community. We first conduct a structured literature review to investigate how the ABMS process uses ML techniques. We focus specifically on articles where ML is applied for the structural specifications of models such as agent decision-making and behaviour, rather than just for analysing output data. Given that modelling challenges are mainly linked to the purpose a model aims to serve (e.g., behavioural accuracy is required for predictive models), we frame our analysis within different modelling purposes. Our results show that Reinforcement Learning algorithms may increase the accuracy of behavioural modelling. Moreover, Decision Trees, and Bayesian Networks are common techniques for data pre-processing of agent behaviour. Based on the literature review results, we propose guidelines for purposefully integrating ML in ABMS. We conclude that ML techniques are specifically fit for currently underrepresented modelling purposes of social learning and illustration; they can be used in a transparent and interpretable manner.

Joshua M. Epstein, Ivan Garibay, Erez Hatna, Matthew Koehler and William Rand
Journal of Artificial Societies and Social Simulation 26 (2) 10

Kyeywords: Inverse Generative Social Science, Agent-Based Models, Evolutionary Computation, Genetic Programming
Abstract: This is a guest editors' statement accompanying the publication of a special issue on "Inverse Generative Social Science", published in volume 26, issue 2, 2023 of JASSS-Journal of Artificial Societies and Social Simulation"

Alejandro Dinkelberg, Pádraig MacCarron, Paul J. Maher, David JP O'Sullivan and Michael Quayle
Journal of Artificial Societies and Social Simulation 26 (2) 2

Kyeywords: Social Identity Approach, Empirically-Driven Agent-Based Models, Opinion Change, Opinion Dynamics, Axelrod''s Model of Cultural Dissemination
Abstract: Group dynamics and inter-group relations influence the self-perception. The Social Identity Approach explains the role of multiple identities, derived from categories or group memberships, in social interaction and individual behaviour. In agent-based models, agents interact with their environment to make decisions and take actions. Thus, we examine to what extent the interaction in an agent-based model natively captures core principles of the Social Identity Approach. To do so, we extend the Axelrod model and the agreement-threshold model with explicit aspects of the Social Identity Approach to assess their influence on the simulation outcomes. We study the variants of the Axelrod model by using Monte Carlo simulations and compare the simulation results with longitudinal survey data of opinions. These extensive simulations favour the Axelrod model and the agreement-threshold model. These models fit, without the explicit embedding of features from the Social Identity Approach, the volatility of the opinion-based features better for the given data sets. Our two extensions of the Axelrod model formalise elements of the Social Identity Approach; however, they do not support the fitness of the model to the data. In the simulations, even in the standard Axelrod model, the social identity affects the development of the agents' identity through the homophily principle, and the agents, in turn, shape their own social identity by social influence. We argue that the Axelrod model and the agreement-threshold model implicitly include social identities as emerging properties of evolving opinion-based groups. In addition to that, the attitudinal data captures the hidden group structure in the attitude positions of the participants. In this way, core features of the Social Identity Approach already implicitly play a role in these empirically-driven agent-based models.

Tuong Manh Vu, Charlotte Buckley, João A. Duro, Alan Brennan, Joshua M. Epstein and Robin Purhouse
Journal of Artificial Societies and Social Simulation 26 (2) 4

Kyeywords: Agent-Based Modelling, Psychosocial Mechanisms of Alcohol Use, Inverse Generative Social Science
Abstract: Social psychological theory posits entities and mechanisms that attempt to explain observable differences in behavior. For example, dual process theory suggests that an agent's behavior is influenced by intentional (arising from reasoning involving attitudes and perceived norms) and unintentional (i.e., habitual) processes. In order to pass the generative sufficiency test as an explanation of alcohol use, we argue that the theory should be able to explain notable patterns in alcohol use that exist in the population, e.g., the distinct differences in drinking prevalence and average quantities consumed by males and females. In this study, we further develop and apply inverse generative social science (iGSS) methods to an existing agent-based model of dual process theory of alcohol use. Using iGSS, implemented within a multi-objective grammar-based genetic program, we search through the space of model structures to identify whether a single parsimonious model can best explain both male and female drinking, or whether separate and more complex models are needed. Focusing on alcohol use trends in New York State, we identify an interpretable model structure that achieves high goodness-of-fit for both male and female drinking patterns simultaneously, and which also validates successfully against reserved trend data. This structure offers a novel interpretation of the role of norms in formulating drinking intentions, but the structure's theoretical validity is questioned by its suggestion that individuals with low autonomy would act against perceived descriptive norms. Improved evidence on the distribution of autonomy in the population is needed to understand whether this finding is substantive or is a modeling artefact.

Chathika Gunaratne, Erez Hatna, Joshua M. Epstein and Ivan Garibay
Journal of Artificial Societies and Social Simulation 26 (2) 7

Kyeywords: Agent-Based Modeling, Residential Segregation, Genetic Programing, Simulation, Complex Systems, Machine Learning
Abstract: The Schelling model of residential segregation has demonstrated that even the slightest preference for neighbors of the same race can be amplified into community-wide segregation. However, these models are unable to simulate mixed, coexisting patterns of segregation and integration, which have been seen to exist in cities. Using evolutionary model discovery we demonstrate how including social factors beyond racial bias when modeling relocation behavior enables the emergence of strongly mixed patterns. Our results indicate that the emergence of mixed patterns is better explained by multiple factors influencing the decision to relocate; the most important being the interaction of nonlinear, rapidly diminishing racial bias with a recent, historical tendency to move. Additionally, preference for less isolated neighborhoods or preference for neighborhoods with longer residing neighbors may produce weaker mixed patterns. This work highlights the importance of exploring the influence of multiple hypothesized factors of decision making, and their interactions, within agent rules, when studying emergent outcomes generated by agent-based models of complex social systems.

Joshua M. Epstein
Journal of Artificial Societies and Social Simulation 26 (2) 9

Kyeywords: Agent-Based Modeling, Generative Social Science, Inverse Generative Social Science, Artificial Intelligence, Evolutionary Computing, Rational Choice Theory
Abstract: The agent-based model is the principal scientific instrument of generative social science. Typically, we design completed agents—fully endowed with rules and parameters—to grow macroscopic target patterns from the bottom up. Inverse generative science (iGSS) stands this approach on its head: Rather than handcrafting completed agents to grow a target—the forward problem—we start with the macro-target and evolve micro-agents that generate it, stipulating only primitive agent-rule constituents and permissible combinators. Rather than specific agents as designed inputs, we are interested in agents—indeed, families of agents—as evolved outputs. This is the backward problem and tools from Evolutionary Computing can help us solve it. As the overarching essay in the current JASSS Special Section, Part 1 discusses the goals of iGSS as distinct from other approaches. Part 2 discusses how to do it concretely, previewing the five iGSS applications that follow. Part 3 discusses several foundational issues for agent-based modeling and economics. Part 4 proposes a central future application of iGSS: to evolve explicit formal alternatives to the Rational Actor, with Agent_Zero as one possible point of evolutionary departure. Conclusions and future research directions are offered in Part 5. Looking ‘backward to the future,’ I also include, as Appendices, a pair of 1992 memoranda to the then President of the Santa Fe Institute on the forward (growing artificial societies from the bottom up) and backward (iGSS) problems.

Carlos Andres Devia and Giulia Giordano
Journal of Artificial Societies and Social Simulation 26 (3) 1

Kyeywords: Agent-Based Social Simulation, Agent-Based Model, Opinion Formation, Opinion Dynamics, Real Data Validation
Abstract: We propose an agent-based opinion formation model characterised by a two-fold novelty. First, we realistically assume that each agent cannot measure the opinion of its neighbours about a given statement with infinite resolution and accuracy, and hence it can only perceive the opinion of others as agreeing much more, or more, or comparably, or less, or much less (than itself) with that given statement. This leads to a classification-based rule for opinion update. Second, we consider three complementary agent traits suggested by significant sociological and psychological research: conformism, radicalism and stubbornness. We rely on World Values Survey data to show that the proposed model has the potential to predict the evolution of opinions in real life: the classification-based approach and complementary agent traits produce rich collective behaviours, such as polarisation, consensus, and clustering, which can yield predicted opinions similar to survey results.

Garry Sotnik, Serhii Choporov and Thaddeus Shannon
Journal of Artificial Societies and Social Simulation 26 (3) 10

Kyeywords: Agent-Based Model, Commitment, Multilevel Group Selection, Multilevel Neighborhood Selection, Prosocial Common-Pool Behavior, Social Identity
Abstract: Prosocial common-pool behavior – subtractable and non-excludable behavior that benefits others – is essential for the survival of any population of social individuals. The behavior, however, usually carries a cost to those who contribute it, placing them at a disadvantage with respect to those who freeride – those who do not contribute but still benefit. How populations sustain existing or adopt new prosocial common-pool behaviors remains unclear. We introduce a theoretical agent-based model and use it to study the role of social identity in a population’s adoption of such behavior. Social identity is relevant because it influences the behavior of individuals in a group, including their willingness to behave prosocially. Our model simulates the emergence of multiple and dynamic social identities of agents within self-organizing groups. Our simulation results suggest that the role of social identity may depend substantially on the density of the population and the commitment level of population members to their groups; that the relationship between density, commitment, and adoption may be nuanced; that, under lower density levels, mobility between groups may be essential; and that the persistence and adoption of prosocial behavior in a population may be overwhelmingly driven by individuals who are highly committed to their groups. The results caution against the influence of two looming worldwide trends – an increase in population density and a decrease in group commitment. The results suggest that, when combined, these two trends may produce the lowest adoption levels of prosocial behavior, the adverse and population-wide repercussions of which could be catastrophic. Finally, our results suggest that social identity may play a helpful role in offsetting the consequences of these trends, implying a need for further empirical and experimental study of the subject and future consideration of incorporating the role of social identity into policy analysis and design.

Jack Mitcham
Journal of Artificial Societies and Social Simulation 26 (3) 12

Kyeywords: Agent-Based Model, Crime, Criminology, Global Sensitivity Analysis, Public Policy, Policy Modelling
Abstract: There are mixed results in the literature when examining the impact of police spending and social welfare spending on crime rates. Here, we use an agent-based model to explore the potential impacts of the tradeoff between police spending and social welfare spending on crime by including parameters for heterogeneous hardship and views of police legitimacy in the model. The purpose of the model is to attempt to explain those mixed results and to provide guidance for policymakers who are implementing these funding decisions. We find that by including the hardship of the people and their view of police legitimacy in the model, the impact of increasing police spending has diminishing returns on the crime rate and under certain circumstances can lead to an increase in crime. This is a stepping stone for future models which can model systems in even more detail. Additionally, policymakers may want to incorporate hardship and police legitimacy into their decision analysis when evaluating programs and budgets.

Amin Boroomand and Paul E. Smaldino
Journal of Artificial Societies and Social Simulation 26 (3) 14

Kyeywords: Collective Intelligence, NK Landscape, Agent-Based Model, Error, Diversity
Abstract: Error affects most human judgments and communications. Here we consider two types of error: unbiased noise and directional biases, and consider their effects in the context of collective problem solving. We studied an agent-based model of networked agents collectively searching for solutions to simple and complex problems on an NK landscape. We implemented superiority bias as a reluctance to adopt solutions used by others unless they were substantially better than one’s own solution. We implemented communication error by injecting noise into solutions learned from others. These factors both reduce the short-term efficiency of social learning, as individuals are less likely to faithfully copy superior solutions. We find that when a team faces complex problems, both communication noise and superiority bias have a positive effect on the overall quality of the team’s collective solution, at the cost of increased time and resource usage. We find that when a team faces simple problems, a moderate level of communication noise leads to a decrease in the required time and resources for a team. We discuss these results in terms of tradeoffs between the quality of a collective solution and the time and resources needed to reach that solution.

Nanda Wijermans, Geeske Scholz, Martin Neumann, Rocco Paolillo, Anne Templeton, Vhonani Netshandama and Doris Neuberger
Journal of Artificial Societies and Social Simulation 26 (3) 15

Kyeywords: Social Identity, Self-Categorization, Behavioural Realism, Formalising, Social Theory, Agent-Based Modelling
Abstract: This is an editorial to the special section on “Social Identity Modelling”, published in Volume 26, Issues 2 and 3, 2023 of the Journal of Artificial Societies and Social Simulation. It provides information on how the Social Identity Approach (SIA) and research using its theoretical framework explains collective behaviour, tailored specifically for modellers. The discussion centres around describing and reflecting on the state of the art in modelling SIA. It ends with looking ahead towards formalising SIA as a means to enable more collective behavioural realism in agent-based social simulations.

Dehua Gao and Yumei Yang
Journal of Artificial Societies and Social Simulation 26 (3) 5

Kyeywords: Organizational Routines, Routine Dynamics, Artifacts, Exploration and Exploitation, Agent-Based Modeling (ABM)
Abstract: Organizational routines are at the core in capturing the typical way of how organizations accomplish their tasks. This paper primarily summarizes the development of scholars’ understanding of the crucial role that artifacts and the materiality play during the course of routines. We then focus on the material artifacts-based exploration and exploitation carried out by multiple human actors, and create a link between individual situated actions at the micro-level and the collective outcome as patterned routines. This discloses the underlying logic between human actors’ exploration and exploitation of material artifacts on the one hand, and the ‘(re)framing-overflowing’ interaction loop amidst routine performances and artifacts as artifactual representations (D’Adderio, 2008; 2011) on the other. Subsequently, this study used an agent-based approach to formalize routines formation dynamics from the ‘bottom-up’. Our simulation results highlighted the relationships between the three crucial aspects – which include the interdependences between situated-actions within and between organizational tasks, artifacts-based explorative and exploitative activities carried out by multiple human actors, and organizational structures or the power asymmetry characterizing interpersonal relationships within the routine system. The research work theoretically enriches people’s understanding of routines formation dynamics over time, and provides indications for managers in designing routine performances via the artifacts.

Inês Lobo, Joana Dimas, Samuel Mascarenhas, Diogo Rato and Rui Prada
Journal of Artificial Societies and Social Simulation 26 (3) 9

Kyeywords: Social Identity Approach, Social Context, Dynamic Identity, Agent-Based Model, Social Bias
Abstract: Individuals change who they are in response to their social environment. In other words, one's identity is dynamic, varying according to context (e.g., individuals present, place, task). Identity has a significant impact on an individual's behaviour. Researchers have been interested in understanding how contextual aspects shape identity and, in turn, how identity influences behaviour. Agent-based simulation models are great tools to identify and predict behaviour associated with these identity processes. In addition, agents can employ identity-related mechanisms based on social theories to become more socially believable and similar to humans. The Social Identity Approach (SIA) is one of the most influential theories covering social aspects of one's identity, with many of its concepts being applied in social simulation research. This paper formalizes the Dynamic Identity Model for Agents (DIMA), an existing agent-based model based on SIA, providing a detailed theoretical foundation of the model, as well as an overview of its integration as a component into a social agent architecture. In DIMA, agents perceive themselves either as distinct individuals (personal identity) or as members of a social group (social identity), acting according to their context-dependent active identity. Two simulation scenarios are presented here to illustrate the use of this model, one based on the Dictator Game and the other on a trash collection task. This work aims to guide other researchers who want to enhance their agents with the DIMA's identity salience mechanism. As a result, they would not only be able to assess how this mechanism influences behaviour based on the context, but they would also be able to explore the dynamics between personal and social identities.

Martina Testori, Francesca Giardini, Charlotte K Hemelrijk, Terence D Dores Cruz and Bianca Beersma
Journal of Artificial Societies and Social Simulation 26 (4) 1

Kyeywords: Gossip, Reputation, Cooperation, Agent-Based Model, Multi-Layer Reputation, Motives
Abstract: Gossip provides individuals a great volume of information, which allows them to make informed decisions and better adapt to the environment around them. Like all pieces of information, however, if not correctly interpreted, gossip can lead to harmful consequences for individuals. Indeed, computational models have portrayed a complex picture on how gossip impacts cooperation, identifying several limitations of the mechanism. Recent theoretical models and empirical studies have shown how interpreting the information received through gossip is a key component to understand how gossip influences individuals and groups. Thus, we built an agent-based model where we examine two reaction mechanisms for different reputation systems, in which agents first interpret the motive behind gossip and then react on the basis of this interpretation. While the first mechanism relies on an encompassing reputation system in which all pieces of information are used to inform future decisions with other group members, the second mechanism comprises a two-layer reputation system, in which agents’ actions are separate from agents’ reliability as gossipers. Our results support previous empirical findings asserting gossip as an effective way to sustain initial cooperation, and offer a solution for gossip driven by negative motives: as long as gossip receivers ignore the information provided by gossipers they deem unreliable and don’t punish them by refraining from cooperative interactions with them, cooperation can be sustained.

Rainer Hegselmann
Journal of Artificial Societies and Social Simulation 26 (4) 11

Kyeywords: Opinion Dynamics, Bounded Confidence Model, Floating-Point Arithmetic, Agent-Based Modelling
Abstract: In the bounded confidence model (BC-model) (Hegselmann and Krause 2002), period by period, each agents averages over all opinions that are no further away from their actual opinion than a given distance ε, i.e., their ‘bound of confidence’. With the benefit of hindsight, it is clear that we completely overlooked a crucial feature of our model back in 2002. That is for increasing values of ε, our analysis suggested smooth transitions in model behaviour. However, the transitions are in fact wild, chaotic and non-monotonic—as described by Lorenz (2006). The most dramatic example of these effects is a consensus that breaks down for larger values of ε. The core of this article is a fundamentally new approach to the analysis of the BC-model. This new approach makes the non-monotonicities unmissable. To understand this approach, we start with the question: how many different BC processes can we initiate with any given start distribution? The answer to this question is almost certainly for all possible start distributions and certainly in all cases analysed here, it is always a finite number of ε-values that make a difference for the processes we start. Moreover, there is an algorithm that finds, for any start distribution, the complete list of ε-values that make a difference. Using this list, we can then go directly through all the possible BC-processes given the start distribution. We can therefore check them for non-monotonicity of any kind, and will be able to find them all. This good news comes however with bad news. That is the algorithm that inevitably and without exception finds all the ϵ-values that matter requires exact arithmetics, without any rounding and without even the slightest rounding error. As a consequence, we have to abandon the usual floating-point arithmetic used in today’s computers and programming languages. What we need to use instead is absolutely exact fractional arithmetic with integers of arbitrary length. This numerical approach is feasible on all modern computers. The new analytical approach and results are likely to have implications for many applications of the BC-model.

Bruce Miller, Ivan Garibay, Jacopo Baggio and Edwin Nassiff
Journal of Artificial Societies and Social Simulation 26 (4) 4

Kyeywords: Innovation Diffusion, Outgroup Aversion, Innovation Adoption, Polarization, Social Networks, Agent-Based Model
Abstract: Individuals’ decisions to adopt an innovation can be influenced by the frequency of other adopters as well as by the group membership of previous adopters or non-adopters in their social network. In addition, adoption or non-adoption of some innovations has been characterized as a means of signaling identification with a group. While identity signaling and outgroup aversion effects on adoption and polarization have been considered in a geo-spatial environment, this work extends an existing model of outgroup aversion to a network-based environment. The results show that adoption levels in a network environment were higher, and polarization lower compared to the non-network environment with all other factors fixed. As more factors were varied, the associated change to adoption and polarization were amplified in network environments. The two most significant factors influencing adoption variability were the degree of outgroup aversion and the level of frequency dependence. Finally, in network environments with outgroup aversion, adoption was found to be higher when modularity and eigenvector centrality were high. In today’s polarized social environment, understanding these effects is critical to the adoption of emerging innovations such as mitigating climate change, combating novel viruses, or decentralizing financial transactions. While innovators are often focused on solving technical challenges to advance adoption of an innovation, equal emphasis on understanding and solving social and potential outgroup effects will be needed to achieve a desired adoption level.

Edoardo Baccini, Zoé Christoff, Stephan Hartmann and Rineke Verbrugge
Journal of Artificial Societies and Social Simulation 26 (4) 7

Kyeywords: Myside Bias, Group Deliberation, Agent-Based Modeling, Truth-Tracking, Wisdom of the Crowd
Abstract: The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. The first part of this paper develops and justifies a Bayesian model of myside bias at the level of individual reasoning. In the second part, this Bayesian model is implemented in an agent-based model of group discussion among myside-biased agents. The agent-based model is then used to perform a number of experiments with the objective to study whether the myside bias hinders or enhances the ability of groups to collectively track the truth, that is, to reach the correct answer to a given binary issue. An analysis of the results suggests the following: First, whether the truth-tracking ability of groups is helped or hindered by myside bias crucially depends on how the strength of myside bias is differentially distributed across subgroups of discussants holding different beliefs. Second, small groups are more likely to track the truth than larger groups, suggesting that increasing group size has a detrimental effect on collective truth-tracking through discussion.

Shuo Liu, Michael Mäs, Haoxiang Xia and Andreas Flache
Journal of Artificial Societies and Social Simulation 26 (4) 8

Kyeywords: Bounded-Confidence, Opinion Dynamics, Social Influence, Opinion Polarization, Agent-Based Modeling
Abstract: Since the first publication of the bounded-confidence models 20 years ago, hundreds of articles studying this class of social-influence models have been written. Bounded-confidence models proposed an intriguing solution to a pervasive research puzzle and have helped unveil and explain intriguing phenomena. Here, we reflect about remaining research problems and future modeling challenges, arguing that there remain counter-intuitive model implications to be understood. To illustrate that there remain uncovered model challenges, we extend the bounded-confidence model. We assume assimilative influence when agents connected by positive relationships hold sufficiently similar opinions, adopting the core assumption of the bounded-confidence models. We combine this with another influential modeling approach, the notion that if agents connected by negative social relationship disagree too much, opinion differences increase due to repulsive influence. This allows us to vary the relative strength of assimilation and repulsion in the influence dynamics, also allowing for the possibility that neither occurs in a particular interaction. Simulation experiments reveal three surprising findings: Counter the intuition that stronger assimilation decreases opinion diversity, we show that in the presence of repulsion, intensifying the strength of assimilation can actually generate more opinion bipolarization. Second, we show that if repulsion becomes weaker this may still result in more bipolarization. Third, it turns out that more negative social relationships between or within subgroups can result in less bipolarization. We demonstrate these effects in very simple and highly stylized settings, in order to show that intuition fails to capture the complexity arising from the interplay of assimilative and repulsive influence even in these simple settings. We discuss implications of our findings for the ongoing debate about societal conditions fostering bipolarization, including in particular the design of personalized online social networks. Further, we address how our results may inform future work comparing and integrating alternative models of social-influence dynamics.

Martin Gestefeld and Jan Lorenz
Journal of Artificial Societies and Social Simulation 26 (4) 9

Kyeywords: Agent-Based Modeling, Social Influence, Opinion Dynamics, Political Trust, Survey
Abstract: Agent-based models of opinion dynamics enable the investigation of societal phenomena that psychological theories of individual opinion change trigger in artificial societies. Many of such models are validated on the phenomenological level only (e.g., can they produce polarization) and not with respect to opinion data about real-world attitude distributions known from social surveys or individual attitude change observed in panel surveys. In this work, we use an existing agent-based model which builds on repeated random pairwise interaction as introduced in many early opinion dynamics models with a function of individual opinion change function that includes the contagious and assimilating opinion adjustment, idiosyncratic opinion change, and motivated cognition, which is a generalization of the concept of bounded confidence. Depending on its parameters, this model creates different opinion distributions. By arguing that institutional trust is formed by an exchange of experiences and opinions, we then match the opinion distributions from the seven institutional trust questions in the European Social Survey to the simulated ones. Goodness-of-fit measures allow to calibrate the model parameters to reproduce 1,235 empirical distributions (for different countries and years) and further on, the calibration procedure is extended to data about individual opinion change from the Swiss Household Panel where participants reported their trust in the government on a yearly basis. Overall the opinion dynamics model reproduces most of the empirical distributions to a high degree, additionally, matching agents to individuals shows also a high level of concordance. In the analysis, we show that the concept of motivated cognition is a crucial part to achieve this level of accuracy. However, in both calibrations, we expose the shortcoming of the model to reproduce individuals with extreme and neutral attitudes.

Jens Koed Madsen, Brian Powers, Richard Bailey, Ernesto Carrella, Nicolas Payette and Toby Pilditch
Journal of Artificial Societies and Social Simulation 27 (1) 1

Kyeywords: Agent-Based Models, Fisheries, Decision-Making, Behaviour, Belief Revision, Complex Systems
Abstract: To effectively manage complex human-environment fisheries systems, it is necessary to understand the psychology of fisher agents. While bio-economic models typically provide simple, abstract approaches for human behaviour (e.g. fully informed profit maximisers), fisher agents are of course neither simple nor perfect. Imperfections of learning, memory, and information availability, combined with the diversity of value preferences within populations, can lead to substantial deviations and unanticipated effects of interventions. This paper presents a computational model of fisher agents’ decision-making that draws on theoretical and empirical psychological insights to enrich this critical component. The model includes mechanisms for information integration (learning), social comparisons, and thresholds for economic satisfaction. In offering this enriched account, the model captures how fishers may adapt behaviourally given changes in policy, economic conditions, or social pressures. Furthermore, the model can be parameterised to capture the effects of different socio-cultural contexts can be simulated. The model of fisher agents has been implemented as part of POSEIDON (an agent-based fisheries management model), showing that fishers imbued with the model learn and adapt when responding dynamically to changing conditions. The model is thus demonstrated in a fisheries environment, but we discuss how its architecture could be implemented for simulation in other human-environment systems, such as designing policies to combat the human-environment problems.

Till Köster, Oliver Reinhardt, Martin Hinsch, Jakub Bijak and Adelinde M. Uhrmacher
Journal of Artificial Societies and Social Simulation 27 (1) 10

Kyeywords: Domain-Specific Language, Population-Based Models, Agent-Based Models, Continuous-Time Markov Chains, Simulation, Performance
Abstract: In agent-based simulation methods and applications, discrete timestep approaches prevail. To support continuous-time agent-based simulation, we analyze how methods for simulating population-based Con-tinu-ous-Time Markov Chains (CMTCs) can be adopted and derive implications for the concrete realization. To corroborate our findings, we develop an efficient internal domain-specific language (DSL) based on ML3, a modeling language for linked lives in demography. The design as an internal DSL, implemented within the Rust programming language, allows the modeler to exploit the complete feature set of the host language, such as data types and structures, when programming decision processes. A concise and expressive modeling of an agent's discrete decisions and behavior introducing exponentially distributed sojourn times can be supported by adapting the concept of guarded commands from population-based CTMCs. The execution of models relies on an optimized version of the direct method. This method is a variant of stochastic simulation algorithms, an established method for executing population-based CTMCs in other application areas, notably biochemistry. To efficiently handle the large set of possible transitions inherent to continuous-time agent-based models, we use a dependency graph whose updating scheme caters to the dynamic dependencies within agent-based models and the need for efficient implementation. The presented case studies include implementations of a continuous-time, agent-based migration model and a comparative performance study based on an extended SIR model of infection spread, allowing us to draw conclusions about the impact of different design choices on efficiency.

Andrew Collins, Matthew Koehler and Christopher Lynch
Journal of Artificial Societies and Social Simulation 27 (1) 11

Kyeywords: Agent-Based Modeling, Docking, Empirical Validation, Model Validation, Simulation Validation, Validation
Abstract: Validation is the process of determining if a model adequately represents the system under study for the model’s intended purpose. Validation is a critical component in building the credibility of a simulation model with its end-users. Effectively conducting validation can be a daunting task for both novice and experienced simulation developers. Further compounding the difficult task of conducting validation is that there is no universally accepted approach for assessing a simulation. These challenges are particularly relevant to the paradigm of Agent-Based Modeling and Simulation (ABMS) because of the complexity found in these models’ mechanisms and in the real-world situations they attempt to represent. To aid both the novice and expert in conducting a validation process for an agent-based simulation, this article reviews nine methods that are useful for this process, including foundational topics of docking, empirical validation, sampling, and visualization, as well as advanced topics of bootstrapping, causal analysis, inverse generative social science, and role-playing. Each method is reviewed with respect to its benefits and limitations as a validation-supporting method for ABMS. Suggestions that may support a validation plan for an agent-based simulations, are also provided. This article is an introductory guide for understanding and conducting ABMS validation for developers of all experience levels.

Álvaro Carmona-Cabrero, Rafael Muñoz-Carpena, Woi Sok Oh and Rachata Muneepeerakul
Journal of Artificial Societies and Social Simulation 27 (1) 16

Kyeywords: Global Sensitivity Analysis, Stochastic Model Analysis, Agent-Based Model, Stochastic Uncertainty, Input Importance
Abstract: Agent-based models (ABMs) are promising tools for improving our understanding of complex natural-human systems and supporting decision-making processes. ABM bottom-up approach is increasingly employed to recreate emergent behaviors that mimic real complex system dynamics. However, often the knowledge and data available for building and testing the ABM and its parts are scarce. Due to ABM output complexity, exhaustive analysis methods are required to increase ABM transparency and ensure that the ABM behavior mimics the real system. Global sensitivity analysis (GSA) is one of the most used model analysis methods, as it identifies the most important model inputs and their interactions and can be used to explore model behaviors that occur in certain regions of the parameter space. However, due to ABM’s stochastic nature, GSA application to ABMs can result in misleading interpretations of the ABM input importance. Here, we review 3 alternative GSA approaches identified in the literature for ABMs and other stochastic models. Using two study cases, a benchmark non-linear function and a proof-of-concept migration ABM, we illustrate the differences in input importance and the shortcomings of current approaches and propose a new GSA approach for the evaluation of stochastic models which separates inputs importance for deterministic and stochastic uncertainties. The former is related to changes in the expected value of model realizations and the latter to changes in the variance of model realizations. Our analysis of the proof-of-concept migration ABM finds that how factors are weighed is more important than the values of the inputs and identifies what inputs are more important for the deterministic and stochastic uncertainties. The analysis also identifies outputs for which the deterministic uncertainty is small, being almost random. This information allows the modeler to evaluate the optimal degree of model complexity and choose among alternative model structures.

Wesley Wildman, George Hodulik and F. LeRon Shults
Journal of Artificial Societies and Social Simulation 27 (1) 19

Kyeywords: Pandemic, COVID-19, Values, Agent-Based Model, Public Health
Abstract: The SARS-CoV-2 pandemic has made abundantly evident that human behavior is a critical factor in determining whether interventions intended to manage infection spread are effective. Human behavior is driven by decision processes about whether to comply with advice from public-health experts and instructions from officials charged with managing pandemic response within organizations and governmental regions. And guiding those decisions are personal values, often shared with others, which are understudied features of pandemic management. Here, we demonstrate the role of values in a pandemic simulation using The Artificial Organization (TAO), an existing, strongly validated, agent-based, decision-support tool for pandemic management. We enhance TAO by adding human values to create TAO-V, focusing particularly on values related to political ideology, the spread of those values, and the way political values and compliance decisions have interacted in the United States of America (and other nations, usually to a lesser extent). TAO-V confirms that human values are influential factors in a pandemic simulation, which invites testing against real-world data from pandemic-management efforts (we pursue this in a subsequent paper). Even before real-world testing, the results of this study suggest that public-health messaging might be more effective if it were to engage values rather than only stress compliance with public-health recommendations.

Anna Melnyk, Bruce Edmonds, Amineh Ghorbani and Ibo van de Poel
Journal of Artificial Societies and Social Simulation 27 (1) 20

Kyeywords: Values, Value Change, Socio-Technical Systems, Socio-Ecological Systems, Agent-Based Modelling
Abstract: This editorial paper for the special section on “Modelling Values in Socio/Technical/Ecological Systems” introduces interdisciplinary perspectives on values and reflects on growing appeals for modelling values. In public and academic discourses, values typically relate to the matter of importance (e.g., beliefs, priorities) and principles about what is considered to be good (e.g., moral values) and are often seen as shaping individual and collective behaviour. As shown by eight contributions to this special section, it is relevant for social simulation modelling to dive deeper into embedding values in models in order to explore behavioural change on different levels and across contexts. Our goal with this special section is to stimulate interest in developing various approaches that study and operationalise values in agent-based models to investigate the complex problems raised in social, socio-technical and socio-ecological systems. We conclude with a call for future research to be explicit in their modelling assumptions, thus fostering a vigorous foundation for scientific discourse.

Tristan de Wildt and Ibo van de Poel
Journal of Artificial Societies and Social Simulation 27 (1) 3

Kyeywords: Value Change, Moral Change, Agent-Based Modelling, Exploratory Modelling
Abstract: Value and moral change have increasingly become topics of interest in the philosophical literature. Several theoretical accounts have been proposed. These are usually based on certain theoretical and conceptual assumptions. Their strengths and weaknesses are often difficult to determine and compare because they are based on limited empirical evidence. We propose agent-based modeling to build simulation models that can theoretically help us explore accounts of value change. We can investigate whether a simulation model based on a specific account of value change can reproduce relevant phenomena. To illustrate this approach, we build a model based on the pragmatist account of value change proposed by Van De Poel and Kudina (2022). We show that this model can reproduce four relevant phenomena, namely 1) the inevitability and stability of values, 2) societies differ in openness and resistance to change, 3) moral revolutions, and 4) lock-in. This makes this account promising, although more research is needed to see how well it can explain other relevant phenomena and compare its strengths and weaknesses to other accounts. On a more methodological level, our contribution suggests that simulation models might be useful to theoretically explore accounts of value change and make further progress in this area.

Natalie Davis, Brian Dermody, Mark Koetse and George van Voorn
Journal of Artificial Societies and Social Simulation 27 (1) 4

Kyeywords: Agent-Based Model, Sustainable Diets, Social Networks, COM-b Framework, Food-Related Values, Attitude-Behavior Gap
Abstract: Understanding the drivers of dietary decisions is crucial for encouraging and facilitating environmentally sustainable consumption patterns. Previous work has focused on the utility that consumers place on factors such as price, quality, and ethics when making dietary decisions, or on the effects of personal values and peer influence on consumption of individual products. However, less attention has been paid to the interacting roles of values, perceptions, and social networks in dietary decision-making, and how these relate to mismatches between values and diet choice. Here, we develop an agent-based model of individual consumers making choices between five possible diets: omnivore, flexitarian, pescatarian, vegetarian, or vegan. Each consumer makes decisions based on personal constraints and values, and their perceptions of how well each diet matches with those values. Consumers can also be influenced by each other’s perceptions via interaction across three social networks: household members, friends, and acquaintances. We show that consumers primarily make decisions based on cost and taste, even when they value ethics and health, and illustrate three potential causes of the ‘attitude-behavior gap’ between ethical motivations and diet choice. This highlights the potential for both policy-driven changes to pricing structures, and increased awareness around sustainability and health attributes of different diets, in overcoming constraints and misperceptions to facilitate transitions to sustainable diets.

Kathyrn Fair and Omar Guerrero
Journal of Artificial Societies and Social Simulation 27 (1) 8

Kyeywords: Age Distributions, Agent-Based Modelling, Modelling Sub-Populations
Abstract: For many applications of agent-based models (ABMs), an agent's age influences important decisions (e.g. their contribution to/withdrawal from pension funds, their level of risk aversion in decision-making, etc.) and outcomes in their life cycle (e.g. their susceptibility to disease). These considerations make it crucial to accurately capture the age distribution of the population being considered. Often, empirical survival probabilities cannot be used in ABMs to generate the observed age structure. This may be due to discrepancies between samples (e.g. when empirical survival probabilities are calculated across the whole population, but only a sub-population is being modelled) or models (between the survival model underpinning the ABM and the statistical model used to produce empirical survival probabilities). In these cases, imputing empirical survival probabilities will not generate the observed age structure of the population, and assumptions such as exogenous agent inflows are necessary (but not necessarily empirically valid). In this paper, we propose a method that allows for the preservation of agent age-structure without the exogenous influx of agents, even when only a subset of the population is being modelled. We demonstrate the flexibility and accuracy of our methodology by performing simulations of several real-world age distributions. This method is a useful tool for those developing ABMs across a broad range of applications.

Hubert Fonteijn, Pepijn van Oort and Geerten Hengeveld
Journal of Artificial Societies and Social Simulation 27 (2) 1

Kyeywords: Food Security, Resilience, Agent-Based Modelling, Food Trade
Abstract: This paper presents a new agent-based modelling (ABM) framework to investigate the resilience of food systems against a variety of shocks. We modelled food security as an emergent property from a network of agents that produce, trade and consume food. The network consists of different regions (mimicking the different hemispheres, an equatorial region and a city state with corresponding growing seasons). Each region in turn consists of a rural and urban area. Rural consumers have access to only regionally produced goods, whereas urban consumers can access both intra- and inter-region trade. We studied food security in a hierarchy of 4 archetypical food systems (or ‘Worlds’) evolving from a simple food system in which regions are not urbanised and there is no trade between regions to a globalised World, with a fully connected food system, which is highly urbanised (including a city state with little national food production) and highly interconnected. We investigated the baseline performance of these food systems (no shocks) and the effect of an export ban of one region on food security. We showed first and second-order effects of such a shock in the short- and medium-term, and how these effects differ across food systems. We found that international trade increases food security in the baseline and shock scenario, but also that it can introduce the potential for poor populations to suffer from food system shocks of distant origins. Future work will extend the set of investigated shocks to provide a broader understanding of food systems resilience, possibly in more realistic scenarios.

Stan L. Rhodes, Stefani A. Crabtree and Jacob Freeman
Journal of Artificial Societies and Social Simulation 27 (2) 2

Kyeywords: Hierarchy, Environmental Changes, Agent-Based Model, Local Information, Management, Organizational Memory
Abstract: Most organizations use command hierarchies—the type of hierarchy depicted in a common organizational chart—but it is not well understood why and how environments make this structure useful. One possibility is that command hierarchies provide positive net benefits when groups of agents must respond to changes in the environment, particularly when each group member’s local conditions are similar and somewhat synchronous. We ask: How does the performance of hierarchical groups vary with changing environments? We build an agent-based model to better understand the strengths and weaknesses of hierarchy for groups faced with these changes in space and time. In these environments, a local worker has more information about local conditions, but a manager has more information about overall conditions. We show that command hierarchy outperforms non-hierarchy in many synchronous and asynchronous environments, including those where local conditions differ substantially and would seem to make a manager’s “big picture” input much less useful to workers. In these more asynchronous environments, a manager’s view of overall conditions does give useful information to workers, with crucial caveats: workers must have the autonomy to judge the accuracy and relevance of manager input to their local work, or they perform worse than non-hierarchical groups. This autonomy enables the organization to learn. Relatedly, we also find increased agent memory is important for performance in all environments. Our model reveals that environments that vary locally can cause unavoidable tension between the views of front-line workers and managers, or local offices and head offices; even perfect agents find themselves in an inevitable computational dilemma. The best organizational strategy to manage this dilemma is continuing to provide manager input while enabling some degree of worker autonomy.

Yannick Oswald, Nicolas Malleson and Keiran Suchak
Journal of Artificial Societies and Social Simulation 27 (2) 3

Kyeywords: Agent-Based Modelling, Policy Diffusion, Peer Mimicry, Data Assimilation, Particle Filter, COVID-19
Abstract: Global problems, such as pandemics and climate change, require rapid international coordination and diffusion of policy. These phenomena are rare however, with one notable example being the international policy response to the COVID-19 pandemic in early 2020. Here we build an agent-based model of this rapid policy diffusion, where countries constitute the agents and with the principal mechanism for diffusion being peer mimicry. Since it is challenging to predict accurately the policy diffusion curve, we utilize data assimilation, that is an “on-line” feed of data to constrain the model against observations. The specific data assimilation algorithm we apply is a particle filter because of its convenient implementation, its ability to handle categorical variables and because the model is not overly computationally expensive, hence a more efficient algorithm is not required. We find that the model alone is able to predict the policy diffusion relatively well with an ensemble of at least 100 simulation runs. The particle filter however improves the fit to the data, reliably so from 500 runs upwards, and increasing filtering frequency results in improved prediction.

Alessia Antelmi, Pasquale Caramante, Gennaro Cordasco, Giuseppe D'Ambrosio, Daniele De Vinco, Francesco Foglia, Luca Postiglione and Carmine Spagnuolo
Journal of Artificial Societies and Social Simulation 27 (2) 4

Kyeywords: Agent-Based Model, Agent-Based Simulation Engine, Model Exploration and Optimization, Reliability and Efficiency, Open-Source
Abstract: Agent-based models represent a primary methodology to untangle and study complex systems. Over the last decade, the need for more elaborate computing-demanding models gave rise to many frameworks and tools to run ABM simulations. Current state-of-the-art ABM tools either focus on ease of use, performance, or a trade-off between these two elements. Still, efficiency-oriented solutions (required for both large and small-scale simulations) are vulnerable to memory flaws which could invalidate the experiment results. This work aims to merge efficiency, reliability, and safeness under an innovative ABM software framework based on the Rust programming language. Our framework, krABMaga, is an open-source library that offers a high-level environment by exploiting metaprogramming and expandable visualization features. We equipped our library with a dynamic simulation monitoring system and model exploration and optimization capabilities over parallel, distributed, and cloud architectures. After having presented the overall architecture and functionalities of krABMaga, we discuss a performance comparison of our framework against the mostly adopted ABM software and the scalability potential of our simulation engine on a model calibration experiment running over an AWS EC2 virtual cluster machine. All code and examples models are available on GitHub.

Benjamin Karic, Jan Stenkamp, Michael Brüggemann, Simon Schröder, Christian Kray and Judith Verstegen
Journal of Artificial Societies and Social Simulation 27 (2) 5

Kyeywords: Agent-Based Modelling, Data Collection, Immersive Video Environment, COVID-19, Calibration, Policy Interventions
Abstract: Setting up any agent-based model (ABM) requires not only theory to define the agents' behavior, but also suitable methods for calibration, validation, and scenario analysis, which are highly dependent on the available data. When modelling aspects related to the COVID-19 pandemic during the pandemic itself, finding existing data and behavioral rules was rarely possible as conditions were fundamentally different from before and collecting data put people at risk. Here, we present a method to set up and calibrate an ABM using an immersive video environment (IVE). First, we collect data in this reproducible and safe setting. Based on derived behavior, we set up an ABM of pedestrians responding to one-way street signs, installed to stimulate physical distancing. Using bootstrapped regression, we integrate the IVE data into the ABM. Model experiments show that the street signs help to reduce pedestrian densities below critical distance-keeping thresholds, though only when the number of pedestrians is not too high. Our work contributes to the understanding of pedestrian movement dynamics during pandemics. In addition, the proposed data collection and calibration method using the IVE may be applied to other simulation models in which effects of interventions in the physical environment are modelled.

Kang Gao, Perukrishnen Vytelingum, Stephen Weston, Wayne Luk and Ce Guo
Journal of Artificial Societies and Social Simulation 27 (2) 8

Kyeywords: Agent-Based Model, Financial Market Simulator, High-Frequency Data, Flash Crash
Abstract: This paper describes simulations and analysis of flash crash scenarios in an agent-based modelling framework. We design, implement, and assess a novel high-frequency agent-based financial market simulator that generates realistic millisecond-level financial price time series for the E-Mini S&P 500 futures market. Specifically, a microstructure model of a single security traded on a central limit order book is provided, where different types of traders follow different behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the model has excellent capability of reproducing realistic stylised facts in financial markets. By introducing an institutional trader that mimics the real-world Sell Algorithm on May 6th, 2010, the proposed high-frequency agent-based financial market simulator is used to simulate the Flash Crash that took place on that day. We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. With the help of Monte Carlo simulations, we discover functional relationships between the amplitude of the simulated 2010 Flash Crash and three conditions: the percentage of volume of the Sell Algorithm, the market maker inventory limit, and the trading frequency of fundamental traders. Similar analyses are carried out for mini flash crash events. An innovative "Spiking Trader" is introduced to the model, replicating real-world scenarios that could precipitate mini flash crash events. We analyse the market dynamics during the course of a typical simulated mini flash crash event and study the conditions affecting its characteristics. The proposed model can be used for testing resiliency and robustness of trading algorithms and providing advice for policymakers.

Louise Dupuis de Tarlé, Matteo Michelini, AnneMarie Borg, Gabriella Pigozzi, Juliette Rouchier, Dunja Šešelja and Christian Straßer
Journal of Artificial Societies and Social Simulation 27 (3) 1

Kyeywords: MySide Bias, Abstract Argumentation, Agent-Based Models, Argumentative Exchange
Abstract: In this paper, we present an agent-based model for studying the impact of 'myside bias' on the argumentative dynamics in scientific communities. Recent insights in cognitive science suggest that scientific reasoning is influenced by `myside bias'. This bias manifests as a tendency to prioritize the search and generation of arguments that support one's views rather than arguments that undermine them. Additionally, individuals tend to apply more critical scrutiny to opposing stances than to their own. Although myside bias may pull individual scientists away from the truth, its effects on communities of reasoners remain unclear. The aim of our model is two-fold: first, to study the argumentative dynamics generated by myside bias, and second, to explore which mechanisms may act as a mitigating factor against its pernicious effects. Our results indicate that biased communities are epistemically less successful than non-biased ones, and that they also tend to be less polarized than non-biased ones. Moreover, we find that two socio-epistemic mechanisms help communities to mitigate the effect of the bias: the presence of a common filter on weak arguments, which can be interpreted as shared beliefs, and an equal distribution of agents for each alternative at the beginning.

Erez Hatna, Jeewoen Shin, Katelynn Devinney, Julia Latash, Vasudha Reddy, Beth Nivin, Alyssa Masor and Sharon K. Greene
Journal of Artificial Societies and Social Simulation 27 (3) 2

Kyeywords: Agent-Based Model, Shigellosis, Disease Transmission, Epidemiology, Infectious Disease, Handwashing Education
Abstract: Large outbreaks of Shigella sonnei among children in Haredi Jewish (ultra-Orthodox) communities in Brooklyn, New York have occurred every 3–5 years since at least the mid-1980s. These outbreaks are partially attributable to large numbers of young children in these communities, with transmission highest in child care and school settings, and secondary transmission within households. As these outbreaks have been prolonged and difficult to control, we developed an agent-based model of shigellosis transmission among children in these communities to support New York City Department of Health and Mental Hygiene staff. Simulated children were assigned an initial susceptible, infectious, or recovered (immune) status and interacted and moved between their home, child care program or school, and a community site. We calibrated the model according to observed case counts as reported to the Health Department. Our goal was to better understand the efficacy of existing interventions and whether limited outreach resources could be focused more effectively. We evaluated how well disseminating hand washing education in child care programs can reduce the number of infected children. The model indicated that intervention efficacy may be as high as 24% when all intervention parameters are at optimal values but only approximately 7% for a more realistic, less stringent scenario. We ranked intervention parameters according to their permutation importance using a random-forest regression analysis. The most important parameter was the minimum number of reported cases in a child care program that triggers a visit to disseminate hand washing education, followed by the use of non-antibacterial soap in hand washing education, the number of additional visits to child care programs, and the probability of successfully obtaining information on child care program attendance via patient interview. Additional strategies should be considered, such as working with community partners to assist with hand hygiene education at facilities during an outbreak.

Deborah Manzi and Francesco Calderoni
Journal of Artificial Societies and Social Simulation 27 (3) 3

Kyeywords: Organized Crime, Criminal Network, Resilience, Drug Trafficking, Disruption, Agent-Based Modeling, Asset Recovery
Abstract: The resilience and resistance of criminal networks, particularly drug trafficking organizations, remain crucial issues in contemporary society. Existing studies have unrealistically modelled law enforcement interventions and fail to capture the complexity of the adaptations of criminal networks. This study introduces MADTOR, the first agent-based model that examines the responses of drug trafficking organizations to different types of law enforcement interventions. MADTOR addresses previous research gaps by enabling more realistic simulations of law enforcement interventions, modeling adaptations by organizations based on real-world operations, and allowing comparisons of different interventions. To demonstrate the possible applications of MADTOR, we assess the impact of arresting varying proportions of members on the resilience of drug trafficking organizations. Our results reveal the disruptive impact of arresting even a few members, and a non-linear relationship between the share of arrested members and disruptive impact, with diminishing returns as the proportion increases. Surviving organizations face increasing recovery difficulties as more members are arrested. These findings contribute to the development of strategies for effective interventions against drug trafficking.

Cheick Amed Diloma Gabriel Traoré, Etienne Delay, Djibril Diop and Alassane Bah
Journal of Artificial Societies and Social Simulation 27 (3) 5

Kyeywords: Agent-Based Modelling (ABM), Algorithm, Complex Adaptive System, Multi Criteria Decision-Making, Optimization, Rationality
Abstract: Sahelian transhumance is a type of socio-economic and environmental pastoral mobility. It involves the movement of herds from their terroir of origin (i.e., their original pastures) to one or more host terroir, followed by a return to the terroir of origin. According to certain pastoralists, the mobility of herds is planned to prevent environmental degradation, given the continuous dependence of these herds on their environment. However, these herds emit Greenhouse Gases (GHGs) in the areas they cross. Given that GHGs contribute to global warming, our long-term objective is to quantify the GHGs emitted by Sahelian herds. The determination of these herds' GHG emissions requires: (1) the artificial replication of the transhumance, and (2) precise knowledge of the space used during their transhumance. This article presents the design of an artificial replication of this transhumance through an agent-based model called MSTRANS. MSTRANS determines the space used by transhumant herds, based on the decision-making process of Sahelian transhumants. MSTRANS integrates a constrained multi-objective optimization problem and algorithms into an agent-based model. The constrained multi-objective optimization problem encapsulates the rationality and adaptability of pastoral strategies. Interactions between transhumants and their socio-economic network are modelled using algorithms and diffusion processes within the multi-objective optimization problem. The dynamics of pastoral resources are formalized at various spatio-temporal scales using equations that are integrated into the algorithms. The results of MSTRANS have been validated using GPS data collected from transhumant herds in Senegal. The MSTRANS results highlight the relevance of integrated models and constrained multi-objective optimization for modelling and monitoring the movement of transhumant herds in the Sahel. We can state that specialists in calculating greenhouse gas emissions now have a reproducible and reusable tool for determining the space occupied by transhumant herds in a Sahelian country. In addition, decision-makers, pastoralists, veterinarians and traders have a reproducible and reusable tool to help them make environmental and socio-economic decisions.

Michael Belfrage, Emil Johansson, Fabian Lorig and Paul Davidsson
Journal of Artificial Societies and Social Simulation 27 (4) 4

Kyeywords: Policy-Modelling, Model Credibility, Accreditation, VV&A, Agent-Based Modelling & Simulation, ABM4Policy
Abstract: This paper explores the issue of model credibility of agent-based models and how it should be evaluated prior to application in policy-making. Specifically, the paper analyses related literature from different fields: (1) to establish a definition of model credibility – a measure of confidence in the model’s inferential capability – and (2) to assess how model credibility can be strengthened through Verification, Validation, and Accreditation (VV&A) prior to application and through post-application evaluation. Several studies have highlighted seriousshortcomings in how V&V of agent-based models is performed and documented, and few public administrations have an established model accreditation process. To address the first issue, we examine the literature on model V&V and, based on this review, introduce and outline the use of a V&V plan. To address the second issue, we draw inspiration from a practical use case of model accreditation applied by a government institution to propose a framework for the accreditation of agent-based models for policy-making. The paper concludes with a discussion of the risks associated with inappropriate assessments of model credibility.

Kärt Padur, Hervé Borrion and Stephen Hailes
Journal of Artificial Societies and Social Simulation 28 (1) 1

Kyeywords: Hybrid Threats, Agent-Based Modelling, Reinforcement Learning, Cyberattack, Misinformation
Abstract: Hybrid attacks coordinate the exploitation of vulnerabilities across domains to undermine trust in authorities and cause social unrest. Whilst such attacks have primarily been seen in active conflict zones, there is growing concern about the potential harm that can be caused by hybrid attacks more generally and a desire to discover how better to identify and react to them. In addressing such threats, it is important to be able to identify and understand an adversary's behaviour. Game theory is the approach predominantly used in security and defence literature for this purpose. However, the underlying rationality assumption, the equilibrium concept of game theory, as well as the need to make simplifying assumptions can limit its use in the study of emerging threats. To study hybrid threats, we present a novel agent-based model in which, for the first time, agents use reinforcement learning to inform their decisions. This model allows us to investigate the behavioural strategies of threat agents with hybrid attack capabilities as well as their broader impact on the behaviours and opinions of other agents. In this paper, we demonstrate the face validity of this approach and argue that its generality and adaptability render it an important tool in formulating holistic responses to hybrid threats, including proactive vulnerability identification, which does not necessarily emerge by considering the multiple threat vectors independently.

Ziyuan Zhang, Mohammad S. Jalali and Navid Ghaffarzadegan
Journal of Artificial Societies and Social Simulation 28 (1) 3

Kyeywords: Agent-Based Model, SARS-CoV-2, Vaccination Policy, Behavioral Modeling, Epidemic, Infectious Disease Dynamics
Abstract: Human behavior shapes epidemic trajectories, evolving as individuals reassess risks over time. Our study closes the loop between epidemic status, individual risk assessments, and interactions. We developed an agent-based model where the individuals can alter their decisions based on perceived risks. In our model, agents’ perceived risk is proxied by their full awareness of actual risks, such as the probability of infection or death. We conducted several simulations of COVID-19 spread for a large metropolitan city akin to New York City, covering the period from December 2020 to May 2021. Our model allows residents to decide daily on traveling to crowded city areas or stay in neighborhoods with relatively lower population density. Our base run simulations indicate that when individuals assess their own risk and understand how diseases spread, they adopt behaviors that slow the spread of virus, leading to fewer cumulative cases and deaths but extending the duration of the outbreak. This model was then simulated with various vaccination strategies such as random distribution, prioritizing older individuals, high-contact-rate individuals, or crowded area residents, all within a risk-response behavioral framework. Results show that, in the presence of agents’ behavioral response, there is only a marginal difference across different vaccination strategies. Specifically, vaccination in crowded areas slightly outperformed other vaccination strategies in reducing infections and prioritizing the elderly was slightly more effective in decreasing deaths. The lack of a universally superior vaccination strategy comes from the fact that lowering a risk leads to more risky behavior which partly compensates for vaccination effects. The comparable outcomes of random versus targeted vaccinations highlight the importance of equitable distribution as another key focus in pandemic responses.

Inan Bostanci and Tim Conrad
Journal of Artificial Societies and Social Simulation 28 (1) 5

Kyeywords: Hybrid Model, Coupled Models, Agent-Based Model, Compartmental Model, Epidemiology, Infectious Disease Model
Abstract: This study investigates the spatial integration of agent-based models (ABMs) and compartmental models for infectious disease modeling, presenting a novel hybrid approach and examining its implications. ABMs offer detailed insights by simulating interactions and decisions among individuals but are computationally expensive for large populations. Compartmental models capture population-level dynamics more efficiently but lack granular detail. We developed a hybrid model that aims to balance the granularity of ABMs with the computational efficiency of compartmental models, offering a more nuanced understanding of disease spread in diverse scenarios, including large populations. This model spatially couples discrete and continuous populations by integrating an ordinary differential equation model with a spatially explicit ABM. Our key objectives were to systematically assess the consistency of disease dynamics and the computational efficiency across various configurations. For this, we evaluated two experimental scenarios and varied the influence of each sub-model via spatial distribution. In the first, the ABM component modeled a homogeneous population; in the second, it simulated a heterogeneous population with landscape-driven movement. Results show that the hybrid model can significantly reduce computational costs but is sensitive to between-model differences, highlighting the importance of model equivalence in hybrid approaches. The code is available at http://github.com/iebos/hybrid_model1.

Zenith Arnejo, Benoit Gaudou, Mehdi Saqalli and Nathaniel Bantayan
Journal of Artificial Societies and Social Simulation 28 (2) 2

Kyeywords: Agent-Based Model (ABM), Communication Approach, Model Communication, Scoping Review, Stakeholder Participation
Abstract: Agent-based models (ABMs) are widely used in fields such as ecology, natural resource management, and policy planning to simulate complex systems. However, their adoption by stakeholders remains limited, primarily due to challenges in effectively communicating these models. Communicating ABMs to stakeholders involves the methods and tools used to convey a model’s structure, function, and results to facilitate understanding, trust, and application. This scoping review examines 65 articles from major databases, identifying key barriers and gaps in ABM communication. Three major issues emerge: (1) limited efforts to support the use of existing ABMs; (2) insufficient emphasis on explaining the emergence in ABMs; and (3) the absence of standardized methods to evaluate the effectiveness of communication strategies. By mapping current communication strategies and categorizing them based on levels of stakeholder participation—ranging from nominal to transformative—this review highlights the need for clearer, more transparent methods of conveying ABM complexity and emergent properties. It also emphasizes the importance of systematic evaluations of communication approaches to ensure that stakeholders fully understand and can apply these models. This paper contributes to the field by offering actionable recommendations to improve transparency, stakeholder engagement, and the reuse of ABMs, with the aim of fostering broader adoption and more effective application of these models across various domains.

Natalie Davis, Brian Dermody, Mark Koetse and George van Voorn
Journal of Artificial Societies and Social Simulation 28 (2) 3

Kyeywords: Agent-Based Modeling, Sustainable Diets, Social Networks, COM-b Framework, Food-Related Value, Attitude-Behavior Gap
Abstract: This corrigendum refers to 'Identifying Personal and Social Drivers of Dietary Patterns: An Agent-Based Model of Dutch Consumer Behavior', Journal of Artificial Societies and Social Simulation, 27 (1) 4, 2024.

Juan Francisco Robles, Enrique Bermejo, Manuel Chica and Óscar Cordón
Journal of Artificial Societies and Social Simulation ()

Kyeywords: Agent-Based Modelling, Model Validation, Automatic Calibration, Multimodal Optimisation, Multimodal Evolutionary Algorithms
Abstract: Agent-based modelling usually involves a calibration stage where a set of parameters needs to be estimated. The calibration process can be automatically performed by using calibration algorithms which search for an optimal parameter configuration to obtain quality model fittings. This issue makes the use of multimodal optimisation methods interesting for calibration as they can provide diverse solution sets with similar and optimal fitness. In this contribution, we compare nine competitive multimodal evolutionary algorithms, both classical and recent, to calibrate agent-based models. We analyse the performance of each multimodal evolutionary algorithm on 12 problem instances of an agent-based model for marketing (i.e. 12 different virtual markets) where we calibrate 24 to 129 parameters to generate two main outputs: historical brand awareness and word-of-mouth volume. Our study shows a clear dominance of SHADE, L-SHADE, and NichePSO over the rest of the multimodal evolutionary algorithms. We also highlight the benefits of these methods for helping modellers to choose from among the best calibrated solutions.