Nigel Gilbert
Journal of Artificial Societies and Social Simulation 2 (1) 3

Abstract: A package of Lisp functions is described which implements a simple multi-level simulation toolkit, MLS. Its design owes a great deal to MIMOSE. MLS runs within Lisp-Stat. It offers a set of functions, macros and objects designed to make the specification of multi-level models straightforward and easy to understand. Lisp-Stat provides a Lisp environment, statistical functions and easy to use graphics, such as histograms, scatterplots and spin-plots, to make the results of multi-level simulations easy to visualise.

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 2 (3) 2

Abstract: An investigation into the conditions conducive to the emergence of heterogeneity amoung agents is presented. This is done by using a model of creative artificial agents to investigate some of the possibilities. The simulation is based on Brian Arthurs `El Farol Bar model but extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of strategies inside each agent. This evolutionary learning process is based on a Genetic Programming algorithm. This is chosen to make the agents as creative as possible and thus allow the outside edge of the simulation trajectory to be explored. A detailed case study from the simulations show how the agents have differentiated so that by the end of the run they had taken on qualitatively different roles. It provides some evidence that the introduction of a flexible learning process and an expressive internal representation has facilitated the emergence of this heterogeneity.

Nigel Gilbert, Andreas Pyka and Petra Ahrweiler
Journal of Artificial Societies and Social Simulation 4 (3) 8

Abstract: A multi-agent simulation embodying a theory of innovation networks has been built and used to suggest a number of policy-relevant conclusions. The simulation animates a model of innovation (the successful exploitation of new ideas) and this model is briefly described. Agents in the model representing firms, policy actors, research labs, etc. each have a knowledge base that they use to generate \'artefacts\' that they hope will be innovations. The success of the artefacts is judged by an oracle that evaluates each artefact using a criterion that is not available to the agents. Agents are able to follow strategies to improve their artefacts either on their own (through incremental improvement or by radical changes), or by seeking partners to contribute additional knowledge. It is shown though experiments with the model's parameters that it is possible to reproduce qualitatively the characteristics of innovation networks in two sectors: personal and mobile communications and biotechnology.

David Hales, Juliette Rouchier and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 6 (4) 5

Abstract: In recent years there has been an explosion of published literature utilising Multi-Agent-Based Simulation (MABS) to study social, biological and artificial systems. This kind of work is evidenced within JASSS but is increasingly becoming part of mainstream practice across many disciplines. However, despite this plethora of interesting models, they are rarely compared, built-on or transferred between researchers. It would seem there is a dearth of "model-to-model" analysis. Rather researchers tend to work in isolation, designing all their models from scratch and reporting their results without anyone else reproducing what they found. Although the opposite extreme, where all that seems to happen is the next twist on an existing model, is not to be wished for, there are considerable dangers if everybody only works on their own model. Part of the reason for this is that models tend to be very seductive – especially to the person who has built the model. What is needed is a third person to check the results. However it is not always clear how people who are not the modeller can interpret or utilise such results, because it is very difficult to replicate simulation models from what is reported in papers. It was for these reasons that we called on the MABS community to submit papers for a model-to-model (M2M) workshop. The aim of the workshop was to gather researchers in MABS who were interested in understanding and furthering the transferability of knowledge between models. We received fourteen submissions from which (after a process of peer review) eight were presented at the workshop. Of the six articles that comprise this special issue, five were presented at the workshop.

Bruce Edmonds and David Hales
Journal of Artificial Societies and Social Simulation 6 (4) 11

Abstract: A published simulation model (Riolo et al. 2001) was replicated in two independent implementations so that the results as well as the conceptual design align. This double replication allowed the original to be analysed and critiqued with confidence. In this case, the replication revealed some weaknesses in the original model, which otherwise might not have come to light. This shows that unreplicated simulation models and their results can not be trusted – as with other kinds of experiment, simulations need to be independently replicated.

Andreas Pyka and Petra Ahrweiler
Journal of Artificial Societies and Social Simulation 7 (2) 6

Abstract: At the third European Meeting on Applied Evolutionary Economics in Augsburg almost 120 participants from all over Europe, North and South America, and South Africa discussed the latest developments in applied Evolutionary Economics. In addition to the nine keynote lectures covering a wide range of topics addressed to the conference theme, 72 papers were presented in the parallel sessions. Due to the general high quality of papers and also an increasing share of simulation work we decided to have this time not only our conference proceedings (Pyka and Hanusch 2004)) but also a special issue in a well recognized journal. And of course, no other journal than JASSS would fit better to our EMAEE initiative. Finally, out of the 72 papers eight jointly suggested by the EMAEE scientific committee were chosen to be included in the regular referee process of JASSS. In the end, five dealing innovatively with simulation models were chosen for this special issue.

Bruce Edmonds and David Hales
Journal of Artificial Societies and Social Simulation 7 (2) 9

Abstract: We present a computational simulation which captures aspects of negotiation as the interaction of agents searching for an agreement over their own mental model. Specifically this simulation relates the beliefs of each agent about the action of cause and effect to the resulting negotiation dialogue. The model highlights the difference between negotiating to find any solution and negotiating to obtain the best solution from the point of view of each agent. The later case corresponds most closely to what is commonly called "haggling". This approach also highlights the importance of what each agent thinks is possible in terms of actions causing changes and in what the other agents are able to do in any situation to the course and outcome of a negotiation. This simulation greatly extends other simulations of bargaining which usually only focus on the case of haggling over a limited number of numerical indexes. Three detailed examples are considered. The simulation framework is relatively well suited for participatory methods of elicitation since the "nodes and arrows" representation of beliefs is commonly used and thus accessible to stakeholders and domain experts.

Ana Maria Ramanath and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 7 (4) 1

Abstract: It is becoming widely accepted that applied social simulation research is more effective if potential users and stakeholders are closely involved in model specification, design, testing and use, using the principles of participatory research. In this paper, a review of software engineering principles and accounts of the development of simulation models are used as the basis for recommendations about some useful techniques that can aid in the development of agent-based social simulation models in conjunction with users. The authors' experience with scenario analysis, joint analysis of design workshops, prototyping and user panels in a collaborative participatory project is described and, in combination with reviews from other participatory projects, is used to suggest how these techniques might be used in simulation-based research.

Frank Dignum, Bruce Edmonds and Liz Sonenberg
Journal of Artificial Societies and Social Simulation 7 (4) 8

Abstract: [No abstract for this editorial]

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

Abstract: This contribution deals with the assessment of the quality of a simulation by discussing and comparing "real-world" and scientific social simulations. We use the example of the Caffè Nero in Guildford as a 'real-world' simulation of a Venetian café. The construction of everyday simulations like Caffè Nero has some resemblance to the construction procedure of scientific social simulations. In both cases, we build models from a target by reducing the characteristics of the latter sufficiently for the purpose at hand; in each case, we want something from the model we cannot achieve easily from the target. After briefly discussing the 'ordinary' method of evaluating simulations called the 'standard view' and its adversary, a constructivist approach asserting that 'anything goes', we heed these similarities in the construction process and apply evaluation methods typically used for everyday simulations to scientific simulation and vice versa. The discussion shows that a 'user community view' creates the foundation for every evaluation approach: when evaluating the Caffè Nero simulation, we refer to the expert community (customers, owners) who use the simulation to get from it what they would expect to get from the target; similarly, for science, the foundation of every validity discussion is the ordinary everyday interaction that creates an area of shared meanings and expectations. Therefore, the evaluation of a simulation is guided by the expectations, anticipations and experience of the community that uses it – for practical purposes (Caffè Nero), or for intellectual understanding and for building new knowledge (science simulation).

Scott Moss and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 8 (4) 13

Abstract: The paper investigates what is meant by "good science" and "bad science" and how these differ as between the natural (physical and biological) sciences on the one hand and social sciences on the other. We conclude on the basis of historical evidence that the natural science are much more heavily constrained by evidence and observation than by theory while the social sciences are constrained by prior theory and hardly at all by direct evidence. Current examples of the latter proposition are taken from recent issues of leading social science journals. We argue that agent based social simulations can be used as a tool to constrain the development of a new social science by direct (what economists dismiss as anecdotal) evidence and that to do so would make social science relevant to the understanding and influencing of social processes. We argue that such a development is both possible and desirable. We do not argue that it is likely.

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 9 (1) 10

Abstract: This paper presents a evolutionary simulation where the presence of 'tags' and an inbuilt specialisation in terms of skills result in the development of 'symbiotic' sharing within groups of individuals with similar tags. It is shown that the greater the number of possible sharing occasions there are the higher the population that is able to be sustained using the same level of resources. The 'life-cycle' of a particular cluster of tag-groups is illustrated showing: the establishment of sharing; a focusing-in of the cluster; the exploitation of the group by a particular skill-group and the waning of the group. This simulation differs from other tag-based models in that is does not rely on either the forced donation of resources to individuals with the same tag and where the tolerance mechanism plays a significant part. These 'symbiotic' groups could provide the structure necessary for the true emergence of artificial societies, supporting a division of labour similar to that found in human societies.

Nigel Gilbert, Matthijs den Besten, Akos Bontovics, Bart G.W. Craenen, Federico Divina, A.E. Eiben, Robert Griffioen, György Hévízi, Andras Lõrincz, Ben Paechter, Stephan Schuster, Martijn C. Schut, Christian Tzolov, Paul Vogt and Lu Yang
Journal of Artificial Societies and Social Simulation 9 (2) 9

Abstract: The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs.

J. Gareth Polhill and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 10 (3) 10

Abstract: We consider here issues of open access to social simulations, with a particular focus on software licences, though also briefly discussing documentation and archiving. Without any specific software licence, the default arrangements are stipulated by the Berne Convention (for those countries adopting it), and are unsuitable for software to be used as part of the scientific process (i.e. simulation software used to generate conclusions that are to be considered part of the scientific domain of discourse). Without stipulating any specific software licence, we suggest rights that should be provided by any candidate licence for social simulation software, and provide in an appendix an evaluation of some popularly used licences against these criteria.

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

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.

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

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.

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 13 (1) 8

Abstract: There are considerable difficulties in the way of the development of useful and reliable simulation models of social phenomena, including that any simulation necessarily includes many assumptions that are not directly supported by evidence. Despite these difficulties, many still hope to develop quite general models of social phenomena. This paper argues that such hopes are ill-founded, in other words that there will be no short-cut to useful and reliable simulation models. However this paper argues that there is a way forward, that simulation modelling can be used to "boot-strap" useful knowledge about social phenomena. If each bit of simulation work can result in the rejection of some of the possible processes in observed social phenomena, even if this is about a very specific social context, then this can be used as part of a process of gradually refining our knowledge about such processes in the form of simulation models. Such a boot-strapping process will only be possible if simulation models are more carefully judged, that is a greater selective pressure is applied. In particular models which are just an analogy of social processes in computational form should be treated as "personal" rather than "scientific" knowledge. Such analogical models are useful for informing the intuition of its developers and users, but do not help the community of social simulators and social scientists to "boot-strap" reliable social knowledge. However, it is argued that both participatory modelling and evidence-based modelling can play a useful part in this process. Some kinds of simulation model are discussed with respect to their suitability for the boot-strapping of social knowledge. The knowledge that results is likely to be of a more context-specific, conditional and mundane nature than many social scientists hope for.

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 14 (4) 7

Abstract: This briefly reviews some philosophy of science that might be relevant to simulating the social processes of science. It also includes a couple of examples from the sociology of science because these are inextricable from the philosophy.

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

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.

Warren Thorngate and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 16 (2) 4

Abstract: Most traditional strategies of assessing the fit between a simulation's set of predictions (outputs) and a set of relevant observations rely either on visual inspection or squared distances among averages. Here we introduce an alternative goodness-of-fit strategy, Ordinal Pattern Analysis (OPA) that will (we argue) be more appropriate for judging the goodness-of-fit of simulations in many situations. OPA is based on matches and mismatches among the ordinal properties of predictions and observations. It does not require predictions or observations to meet the requirements of interval or ratio measurement scales. In addition, OPA provides a means to assess prediction-observation fits case-by-case prior to aggregation, and to map domains of validity of competing simulations. We provide examples to illustrate how OPA can be employed to assess the ordinal fit and domains of validity of simulations of share prices, crime rates, and happiness ratings. We also provide a computer programme for assisting in the calculation of OPA indices.

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

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.

Tina Balke and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 17 (4) 13

Abstract: When designing an agent-based simulation, an important question to answer is how to model the decision making processes of the agents in the system. A large number of agent decision making models can be found in the literature, each inspired by different aims and research questions. In this paper we provide a review of 14 agent decision making architectures that have attracted interest. They range from production-rule systems to psychologically- and neurologically-inspired approaches. For each of the architectures we give an overview of its design, highlight research questions that have been answered with its help and outline the reasons for the choice of the decision making model provided by the originators. Our goal is to provide guidelines about what kind of agent decision making model, with which level of simplicity or complexity, to use for which kind of research question.

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 18 (1) 17

Abstract: A structure for analysing narrative data is suggested, one that distinguishes three parts in sequence: context (a heuristic to identify what knowledge is relevant given a kind of situation), scope (what is possible within that situation) and narrative elements (the detailed conditional and sequential structure of actions and events given the context and scope). This structure is first motivated and then illustrated with some simple examples taken from Sukaina Bhawani’s thesis (Bhawani 2004). It is suggested that such a structure might be helpful in preserving more of the natural meaning of such data, as well as being a good match to a context-dependent computational architecture, and thus facilitate the process of using narrative data to inform the specification of behavioural rules in an Agent-Based Simulation. This suggestion only solves part of the “Narrative Data to Agent Behaviour” puzzle – this structure needs to be combined and improved by other methods and appropriate computational architectures designed to suit it.

Bruce Edmonds
Journal of Artificial Societies and Social Simulation 18 (1) 18

Abstract: This is an introduction to the special section of JASSS on the above topic. It argues for the importance of qualitative evidence in social science, and particularly in the specification of agent-based models. It ends by suggesting some criteria for judging methods for using qualitative evidence for this purpose.

Petra Ahrweiler, Michel Schilperoord, Andreas Pyka and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 18 (4) 5

Abstract: This paper presents the agent-based model INFSO-SKIN, which provides ex-ante evaluation of possible funding policies in Horizon 2020 for the European Commission’s DG Information Society and Media (DG INFSO). Informed by a large dataset recording the details of funded projects, the simulation model is set up to reproduce and assess the funding strategies, the funded organisations and projects, and the resulting network structures of the Commission’s Framework 7 (FP7) programme. To address the evaluative questions of DG INFSO, this model, extrapolated into the future without any policy changes, is taken as an evidence-based benchmark for further experiments. Against this baseline scenario the following example policy changes are tested: (i) What if there were changes to the thematic scope of the programme? (ii) What if there were changes to the instruments of funding? (iii) What if there were changes to the overall amount of programme funding? (iv) What if there were changes to increase Small and Medium Enterprise (SME) participation? The results of these simulation experiments reveal some likely scenarios as policy options for Horizon 2020. The paper thus demonstrates that realistic modelling with a close data-to-model link can directly provide policy advice.

Nigel Gilbert, Petra Ahrweiler, Pete Barbrook-Johnson, Kavin Preethi Narasimhan and Helen Wilkinson
Journal of Artificial Societies and Social Simulation 21 (1) 14

Abstract: Computational models are increasingly being used to assist in developing, implementing and evaluating public policy. This paper reports on the experience of the authors in designing and using computational models of public policy (‘policy models’, for short). The paper considers the role of computational models in policy making, and some of the challenges that need to be overcome if policy models are to make an effective contribution. It suggests that policy models can have an important place in the policy process because they could allow policy makers to experiment in a virtual world, and have many advantages compared with randomised control trials and policy pilots. The paper then summarises some general lessons that can be extracted from the authors’ experience with policy modelling. These general lessons include the observation that often the main benefit of designing and using a model is that it provides an understanding of the policy domain, rather than the numbers it generates; that care needs to be taken that models are designed at an appropriate level of abstraction; that although appropriate data for calibration and validation may sometimes be in short supply, modelling is often still valuable; that modelling collaboratively and involving a range of stakeholders from the outset increases the likelihood that the model will be used and will be fit for purpose; that attention needs to be paid to effective communication between modellers and stakeholders; and that modelling for public policy involves ethical issues that need careful consideration. The paper concludes that policy modelling will continue to grow in importance as a component of public policy making processes, but if its potential is to be fully realised, there will need to be a melding of the cultures of computational modelling and policy making.

Bruce Edmonds, Christophe Le Page, Mike Bithell, Edmund Chattoe-Brown, Volker Grimm, Ruth Meyer, Cristina Montañola-Sales, Paul Ormerod, Hilton Root and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 22 (3) 6

Abstract: How one builds, checks, validates and interprets a model depends on its ‘purpose’. This is true even if the same model code is used for different purposes. This means that a model built for one purpose but then used for another needs to be re-justified for the new purpose and this will probably mean it also has to be re-checked, re-validated and maybe even re-built in a different way. Here we review some of the different purposes for a simulation model of complex social phenomena, focusing on seven in particular: prediction, explanation, description, theoretical exploration, illustration, analogy, and social interaction. The paper looks at some of the implications in terms of the ways in which the intended purpose might fail. This analysis motivates some of the ways in which these ‘dangers’ might be avoided or mitigated. It also looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. These distinctions clarify some previous debates as to the best modelling strategy (e.g. KISS and KIDS). The paper ends with a plea for modellers to be clear concerning which purpose they are justifying their model against.

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

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.

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

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.

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

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

Ozge Dilaver and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 26 (1) 4

Abstract: This paper aims to improve the transparency of agent-based social simulation (ABSS) models and make it easier for various actors engaging with these models to make sense of them. It studies what ABSS is and juxtaposes its basic conceptual elements with insights from the agency/structure debate in social theory to propose a framework that captures the ‘conceptual anatomy’ of ABSS models in a simple and intuitive way. The five elements of the framework are: agency, social structure, environment, actions and interactions, and temporality. The paper also examines what is meant by the transparency or opacity of ABSS in the rapidly growing literature on the epistemology of computer simulations. It deconstructs the methodological criticism that ABSS models are black boxes by identifying multiple categories of transparency/opacity. It argues that neither opacity nor transparency is intrinsic to ABSS. Instead, they are dependent on research habitus - practices that are developed in a research field that are shaped by structure of the field and available resources. It discusses the ways in which thinking about the conceptual anatomy of ABSS can improve its transparency.

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

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.

Yahya Gamal, Corinna Elsenbroich, Nigel Gilbert, Alison Heppenstall and Kashif Zia
Journal of Artificial Societies and Social Simulation 27 (4) 5

Abstract: The housing market in the UK features a mortgaging system where interest rates are either fixed for short periods (typically 2 or 5 years) or varied to track interest rates of the Bank of England base rate. The reactions of home buyers and investors to changes in the mortgage rate have impacts on the buy-to-let housing market, and this in turn impacts tenants who are renting from private landlords. Such reactions become more significant when there are financial shocks, as occurred in 2022, which create chain events that can affect house prices and rents. To explore the dynamics of the UK housing market, we introduce an Agent Based Model (ABM) featuring interactions between the mortgage, buy-to-let and rental housing markets. We use the model to understand the effects of interest rate and maximum loan-to-value shocks. The ABM demonstrates the complex associations between such shocks, house prices and rents. It shows that a sudden increase in mortgage interest rates decreases housing prices and steeply increases rent prices within 5 years. It also shows that a sudden decrease of the loan-to-value ratio significantly decreases housing prices.