Rosaria Conte and Frank Dignum
Journal of Artificial Societies and Social Simulation 4 (2) 7

Kyeywords: Norms, Multi Agent Systems, Imitation, Social Control, Social Cognition
Abstract: This paper is intended to analyse the concepts involved in the phenomena of social monitoring and norm-based social influence for systems of normative agents. These are here defined as deliberative agents, representing norms and deciding upon them. Normative agents can use the norms to evaluate others' behaviours and, possibly, convince them to comply with norms. Normative agents contribute to the social dynamics of norms, and more specifically, of norm-based social control and influence. In fact, normative intelligence allows agents to Check the efficacy of the norms (the extent to which a norm is applied in the system in which it is in force), and possibly Urge their fellows to obey the norms. The following issues are addressed: What is norm-based control? Why and how do agents exercise control on one another? What role does it play in the spread of norms?

David Chavalarias
Journal of Artificial Societies and Social Simulation 9 (2) 5

Kyeywords: Social Cognition, Imitation, Cultural Co-Evolution, Differentiation, Reflexivity, Metacognition, Stochastic Game Theory, Endogenous Distributions, Metamimetic Games, Counterfactual Equilibrium
Abstract: Imitation is fundamental in the understanding of social systems' dynamics. But the diversity of imitation rules employed by modelers proves that the modeling of mimetic processes cannot avoid the traditional problem of endogenization of all the choices, including the one of the mimetic rules. Starting from the remark that metacognition and human reflexive capacities are the ground for a new class of mimetic rules, we propose a formal framework, metamimetic games, that enables to endogenize the distribution of imitation rules while being human specific. The corresponding concepts of equilibrium — counterfactually stable state — and attractor are introduced. Finally, we give an interpretation of social differenciation in terms of cultural co-evolution among a set of possible motivations, which departs from the traditional view of optimization indexed to immutable criteria that exist prior to the activity of agents.

Klaus Jaffe and Roberto Cipriani
Journal of Artificial Societies and Social Simulation 10 (1) 7

Kyeywords: Social Simulation, Interactions, Group Size, Selfish Heard, Cultural Evolution, Biological Evolution
Abstract: A one dimensional cellular automata model describes the evolutionary dynamics of cooperation when grouping by cooperators provides protection against predation. It is used to compare the dynamics of evolution of cooperation in three settings. G: only vertical transmission of information is allowed, as an analogy of genetic evolution with heredity; H: only horizontal information transfer is simulated, through diffusion of the majority\'s opinion, as an analogy of opinion dynamics or social learning; and C: analogy of cultural evolution, where information is transmitted both horizontally (H) and vertically (V) so that learned behavior can be transmitted to offspring. The results show that the prevalence of cooperative behavior depends on the costs and benefits of cooperation so that: a- cooperation becomes the dominant behavior, even in the presence of free-riders (i.e., non-cooperative obtaining benefits from the cooperation of others), under all scenarios, if the benefits of cooperation compensate for its cost; b- G is more susceptible to selection pressure than H achieving a closer adaptation to the fitness landscape; c- evolution of cooperative behavior in H is less sensitive to the cost of cooperation than in G; d- C achieves higher levels of cooperation than the other alternatives at low costs, whereas H does it at high costs. The results suggest that a synergy between H and V is elicited that makes the evolution of cooperation much more likely under cultural evolution than under the hereditary kind where only V is present.

Matthew Francisco, Staša Milojevic and Selma Šabanovic
Journal of Artificial Societies and Social Simulation 14 (4) 13

Kyeywords: Science of Science, Conferences, Community-Based Complex Models, Group Size, Methodology
Abstract: We propose using community-centered analyses and agent-based models of scientific gatherings such as conferences, symposia and workshops as a way to understand how scientific practices evolve and transition between local, community, and systems levels in science. We suggest using robotics as a case study of global, cross-cultural, interdisciplinary scientific practice. What is needed is a set of modeling frameworks for simulating both the internal and population dynamics of scientific gatherings. In this paper we make the case for conference models as a mid-level unit of analysis that can advance the ways scientists and citizens design systems for transferring and producing knowledge.

Paul Smaldino, Cynthia Pickett, Jeffrey Sherman and Jeffrey Schank
Journal of Artificial Societies and Social Simulation 15 (4) 7

Kyeywords: Optimal Distinctiveness, ODT, Group Size, Social Cognition, Spatial Models
Abstract: According to optimal distinctiveness theory (ODT; Brewer 1991), individuals prefer social groups that are relatively distinct compared to other groups in the individuals' social environment. Distinctive groups (i.e., groups of moderate relative size) are deemed "optimal" because they allow for feelings of inclusion and social connection while simultaneously providing a basis for differentiating the self from others. However, ODT is a theory about individual preferences and, as such, does not address the important question of what types of groups are actually formed as a function of these individual-level preferences for groups of a certain size. The goal of the current project was to address this gap and provide insight into how the nature of the social environment (e.g., the size of the social neighborhood) interacts with individual-level group size preferences to shape group formation. To do so, we developed an agent-based model in which agents adopted a social group based on an optimal group size preference (e.g., a group whose size represented 20% of the social neighborhood). We show that the assumptions of optimal distinctiveness theory do not lead to individually satisfactory outcomes when all individuals share the same social environment. We were able to produce results similar to those predicted by ODT when social neighborhoods were local and overlapping. These results suggest that the effectiveness of a social identity decision strategy is highly dependent on sociospatial structure.

Ricardo Andrés Guzmán, Sammy Drobny and Carlos Rodríguez-Sickert
Journal of Artificial Societies and Social Simulation 21 (4) 10

Kyeywords: Social Stratification, Agricultural Intensification, Territorial War, Civil Wars, Malthusian Dynamics, Spatial Models
Abstract: We present a spatial agent-based model of the emergence and proliferation of premodern complex societies in an isolated region initially inhabited by simple societies. At the intrasocietal level, the model integrates scalar stress, social fission, sociocultural evolution, societal collapse, and Malthusian-Ricardian demographic dynamics. At the geographical level, the model includes warfare for territory and captives, territorial division due to social conflict, and territorial disintegration due to collapse. We found that a single variable---slow, continuous progress in intensive agriculture---drives the social and geographical dynamics. Consistent with the archaeological and historical record, the model produced three consecutive "eras": During the first era, simple societies dominate the region. They use extensive food production methods. Small complex societies of intensive agriculturists emerge intermittently in the core land, where intensification is feasible. Shortly after, they collapse or are annihilated by local simple societies. During the second era, some complex societies avert early collapse and annihilation. They expand by conquest. At all times, they coexist with simple societies. Some complex societies are destroyed in war; others collapse. From time to time, complex societies collapse en masse. During the third era, there are no more mass collapses. Complex societies slowly expand until they dominate the core land. Simple societies take refuge in the marginal land, where intensification is infeasible. Simple and complex societies coexist, separated by a moving frontier. In an ebb and flow, complex societies expand to the marginal land and withdraw to the core land. The results of the simulations are qualitatively consistent with prehistorical and historical case studies. The model replicates the progression from simple to more complex societies, and explains why that progression happened in fits and starts.