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37 articles matched your search for the keywords:
Social Networks, Music, Emotion

Introducing Emotions into the Computational Study of Social Norms: a First Evaluation

Alexander Staller and Paolo Petta
Journal of Artificial Societies and Social Simulation 4 (1) 2

Kyeywords: Social Norms, Appraisal Theory of Emotions ,Process Model of Emotions, Layered Agent Architecture, Simulation, JAM (BDI Agent Architecture), Micro-Macro Link, Aggression Control Case Study, Deontic Reasoning and Human Behaviour Models
Abstract: It is now generally recognised that emotions play an important functional role within both individuals and societies, thereby forming an important bond between these two levels of analysis. In particular, there is a bi-directional interrelationship between social norms and emotions, with emotions playing an instrumental role for the sustenance of social norms and social norms being an essential element of regulation in the individual emotional system. This paper lays the foundations for a computational study of this interrelationship, drawing upon the functional appraisal theory of emotions. We describe a first implementation of a situated agent architecture, TABASCOJAM, that incorporates a simple appraisal mechanism and report on its evaluation in a well-known scenario for the study of aggression control as a function of a norm, that was suitably extended. The simulation results reported in the original aggression control study were successfully reproduced, and consistent performances were achieved for extended scenarios with conditional norm obeyance. In conclusion, it is argued that the present effort indicates a promising lane towards the necessary abandonment of logical models for the explanation and simulation of human social behaviour.

"ArrierosAlife" a Multi-Agent Approach Simulating the Evolution of a Social System: Modeling the Emergence of Social Networks with "Ascape"

Klaus Auer and Timothy Norris
Journal of Artificial Societies and Social Simulation 4 (1) 6

Kyeywords: Cellular Automata, Multi-Agent Model, Evolution, Social Networks, Object Oriented Programming Language, Artificial Landscape
Abstract: The behavior of cellular automata is a very close representation of the evolution of complex social systems. We developed the simulation model "ArrierosAlife" to explore the behavior of changes in social networks over time. The model is based on empirical data, a result out of a longitudinal field work. The focus of this research is a comparison of network changes over time in the "real world" compared with the emergence of social networks in an artificial society. "Ascape" was used as a modeling frame work to facilitate the development and analysis of the simulation model. We will give a brief overview of the developed model and describe the experiences using "Ascape" as a framework.

Innovation Networks - a Simulation Approach

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

Kyeywords: Innovation, Simulation of Social Networks, Mobile Communications, Biotechnology, Kene
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.

Modelling the Dynamics of Youth Subcultures

Petter Holme and Andreas Grönlund
Journal of Artificial Societies and Social Simulation 8 (3) 3

Kyeywords: Youth Culture, Adolescence, Multiagent Systems, Complex Networks, Social Networks
Abstract: What are the dynamics behind youth subcultures such as punk, hippie, or hip-hop cultures? How does the global dynamics of these subcultures relate to the individual's search for a personal identity? We propose a simple dynamical model to address these questions and find that only a few assumptions of the individual's behaviour are necessary to regenerate known features of youth culture.

An Agent-Based Spatially Explicit Epidemiological Model in MASON

Jill Bigley Dunham
Journal of Artificial Societies and Social Simulation 9 (1) 3

Kyeywords: Epidemiology, Social Networks, Agent-Based Simulation, MASON Toolkit
Abstract: This paper outlines the design and implementation of an agent-based epidemiological simulation system. The system was implemented in the MASON toolkit, a set of Java-based agent-simulation libraries. This epidemiological simulation system is robust and extensible for multiple applications, including classroom demonstrations of many types of epidemics and detailed numerical experimentation on a particular disease. The application has been made available as an applet on the MASON web site, and as source code on the author\'s web site.

How Realistic Should Knowledge Diffusion Models Be?

Jean-Philippe Cointet and Camille Roth
Journal of Artificial Societies and Social Simulation 10 (3) 5

Kyeywords: Agent-Based Simulation, Complex Systems, Empirical Calibration and Validation, Knowledge Diffusion, Model Comparison, Social Networks
Abstract: Knowledge diffusion models typically involve two main features: an underlying social network topology on one side, and a particular design of interaction rules driving knowledge transmission on the other side. Acknowledging the need for realistic topologies and adoption behaviors backed by empirical measurements, it becomes unclear how accurately existing models render real-world phenomena: if indeed both topology and transmission mechanisms have a key impact on these phenomena, to which extent does the use of more or less stylized assumptions affect modeling results? In order to evaluate various classical topologies and mechanisms, we push the comparison to more empirical benchmarks: real-world network structures and empirically measured mechanisms. Special attention is paid to appraising the discrepancy between diffusion phenomena (i) on some real network topologies vs. various kinds of scale-free networks, and (ii) using an empirically-measured transmission mechanism, compared with canonical appropriate models such as threshold models. We find very sensible differences between the more realistic settings and their traditional stylized counterparts. On the whole, our point is thus also epistemological by insisting that models should be tested against simulation-based empirical benchmarks.

Simulating the Effect of Social Influence on Decision-Making in Small, Task-Oriented, Groups

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.

Social Circles: A Simple Structure for Agent-Based Social Network Models

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.

A Spatial Approach to Network Generation for Three Properties: Degree Distribution, Clustering Coefficient and Degree Assortativity

Jennifer Badham and Rob Stocker
Journal of Artificial Societies and Social Simulation 13 (1) 11

Kyeywords: Social Networks, Network Generation, Clustering Coefficient, Assortativity
Abstract: Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values.

The Third Way of Agent-Based Social Simulation and a Computational Account of Emergence

Roy Wilson
Journal of Artificial Societies and Social Simulation 13 (3) 8

Kyeywords: Agent-Based Social Simulation, Weak Emergence, Social Networks, Kolmogorov Complexity, Upward Causation, Downward Causation
Abstract: This paper interprets a particular agent-based social simulation (ABSS) in terms of the third way of understanding agent-based simulation proposed by Conte. It is proposed that the normalized compression distance (derived from estimates of Kolmogorov complexity) between the initial and final macrolevel states of the ABSS provides a quantitative measure of the degree to which the results obtained via the ABSS might be obtained via a closed-form expression. If the final macrolevel state of an ABSS can only be obtained by simulation, this confers on agent-based social simulations a special status. Future empirical (computational) work and epistemological analyses are proposed.

Opinion Formation by Informed Agents

Mohammad Afshar and Masoud Asadpour
Journal of Artificial Societies and Social Simulation 13 (4) 5

Kyeywords: Social Networks, Informed Agents, Innovation Diffusion, Bounded Confidence, Opinion Dynamics, Opinion Formation
Abstract: Opinion formation and innovation diffusion have gained lots of attention in the last decade due to its application in social and political science. Control of the diffusion process usually takes place using the most influential people in the society, called opinion leaders or key players. But the opinion leaders can hardly be accessed or hired for spreading the desired opinion or information. This is where informed agents can play a key role. Informed agents are common people, not distinguishable from the other members of the society that act in coordination. In this paper we show that informed agents are able to gradually shift the public opinion toward a desired goal through microscopic interactions. In order to do so they pretend to have an opinion similar to others, but while interacting with them, gradually and intentionally change their opinion toward the desired direction. In this paper a computational model for opinion formation by the informed agents based on the bounded confidence model is proposed. The effects of different parameter settings including population size of the informed agents, their characteristics, and network structure, are investigated. The results show that social and open-minded informed agents are more efficient than selfish or closed-minded agents in control of the public opinion.

Zaller-Deffuant Model of Mass Opinion

Krzysztof Malarz, Piotr Gronek and Krzysztof Kulakowski
Journal of Artificial Societies and Social Simulation 14 (1) 2

Kyeywords: Mass Opinion; Computer Simulations; Social Networks;
Abstract: Recent formulation of the Zaller model of mass opinion is generalized to include the interaction between agents. The mechanism of interaction is close to the bounded confidence model. The outcome of the simulation is the probability distribution of opinions on a given issue as dependent on the mental capacity of agents. Former result was that a small capacity leads to a strong belief. Here we show that an intensive interaction between agents also leads to a consensus, accepted without doubts.

Challenges in Modelling Social Conflicts: Grappling with Polysemy

Martin Neumann, Andreas Braun, Eva-Maria Heinke, Mehdi Saqalli and Armano Srbljinovic
Journal of Artificial Societies and Social Simulation 14 (3) 9

Kyeywords: Social Conflicts, Conflict Models, Modelling Challenges, Polysemy, Rationality, Emotions
Abstract: This discussion paper originates from the preceding annual workshop of the Special Interest Group on Social Conflict and Social Simulation (SIG-SCSS) of the ESSA. The workshop especially focused on the need to identify and examine challenges to modeling social conflicts. It turned out that the polysemous nature of social conflicts makes it very difficult to get a grasp of their complexity. In order to deal with this complexity, various dimensions have to be taken into consideration, beginning with the question of how to identify a conflict in the first place. Other dimensions include the relation of conflict and rationality and how to include non-rational factors into conflict models. This involves a conception of organized action. Finally, guiding principles for model development are being discussed. We would like to invite readers of the Journal of Artificial Societies and Social Simulation to 'sow the seeds' of this debate.

For an Integrated Approach to Agent-Based Modeling of Science

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.

Computational Modelling of Trust and Social Relationships

Alistair Sutcliffe and Di Wang
Journal of Artificial Societies and Social Simulation 15 (1) 3

Kyeywords: Social Agents, Social Modelling, Trust, Social Networks
Abstract: A computational model for the development of social relationships is described. The model implements agent strategies for social interaction based on Dunbar's Social Brain Hypothesis (SBH). A trust related process controls the formation and decay of relationships as a function of interaction frequency, the history of interaction, and the agents' strategies. A good fit the SBH predictions was found across a range of model parameter settings, which varied the waning rate of trust, defect/cooperation rates for agents, and linear/log functions for trust increase and decay. Social interaction strategies which favour interacting with existing strong ties or a time variant strategy produced more SBH conformant results than strategies favour more weaker relationships. The prospects for modeling the emergence of social relationships are discussed.

Put Your Money Where Your Mouth Is: DIAL, A Dialogical Model for Opinion Dynamics

Piter Dykstra, Corinna Elsenbroich, Wander Jager, Gerard Renardel de Lavalette and Rineke Verbrugge
Journal of Artificial Societies and Social Simulation 16 (3) 4

Kyeywords: Dialogical Logic, Opinion Dynamics, Social Networks
Abstract: We present DIAL, a model of group dynamics and opinion dynamics. It features dialogues, in which agents gamble about reputation points. Intra-group radicalisation of opinions appears to be an emergent phenomenon. We position this model within the theoretical literature on opinion dynamics and social influence. Moreover, we investigate the effect of argumentation on group structure by simulation experiments. We compare runs of the model with varying influence of the outcome of debates on the reputation of the agents.

An Abstract Model Showing That the Spatial Structure of Social Networks Affects the Outcomes of Cultural Transmission Processes

Andrew White
Journal of Artificial Societies and Social Simulation 16 (3) 9

Kyeywords: Social Networks, Cultural Transmission, Spatial Analysis, Network Structure, Network Properties, Archaeology
Abstract: Space plays an important role in the transfer of information in most societies that archaeologists study. Social networks that mediate learning and the transmission of cultural information are situated in spatial environments. This paper uses an abstract agent-based model to represent the transmission of the value of a single "stylistic" variable among groups linked together within a social network, the spatial structure of which is varied using a few simple parameters. The properties of the networks are shown to clearly affect both the overall amount of variability that is produced by the cultural transmission process and the spatial organization of that variability. The relationships between network structure, network properties, and assemblage variability in this simple model are patterned and predictable. This suggests that changes in the spatial structure of social networks may have important implications for interpreting patterns of artifact variability in large-scale archaeological assemblages.

Segregated Cooperation

Roger Waldeck
Journal of Artificial Societies and Social Simulation 16 (4) 14

Kyeywords: Social Emotions, Norms, Prisoner, Spatial Interaction Structures, Segregation, Agent-Based Simulation
Abstract: Observations in experiments show that players in a prisoner's dilemma may adhere more or less to a cooperative norm. Adherence is defined by the intensity of pro-social emotions, like guilt, of deviating from the norm. Players consider also payoffs from defection as a motive to deviate. By combining both incentives, the modeling may explain conditional cooperation and the existence of polymorphic equilibria in which cooperators and defectors coexist. We then show by the use of simulations, that local interaction structures may produce segregation and the appearance of cooperative zones under these conditions.

Simulating Social and Economic Specialization in Small-Scale Agricultural Societies

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.

If We Work Together, I Will Have Greater Power: Coalitions in Networked Innovation

Rory Sie, Peter B. Sloep and Marlies Bitter-Rijpkema
Journal of Artificial Societies and Social Simulation 17 (1) 3

Kyeywords: Coalition Formation, Networked Innovation, Creativity, Simulation of Social Networks, Social Behaviour, Complex Networks
Abstract: The present article uses agent-based social simulation to study rational behaviour in networked innovation. A simulation model that includes network characteristics and network participant’s characteristics is run using parameter sweeping, yielding 1450 simulation cases. The notion of coalitions was used to denote partnerships in networked innovation. Coalitions compete against each other and several variables were observed for winning coalitions. Close analysis of the variations and their influence on the average power per winning coalition was analysed using stepwise multiple regression analysis. The analysis brought forward two main conclusions. First, as average betweenness centrality per winning coalition increases, the average power per winning coalition decreases. This implies that having high betweenness centrality as a network participant makes it easier to build a successful coalition, as a coalition needs lower average power to succeed. Second, as the number of network participants increases, the average power per winning coalition decreases. This implies that in a larger network, it may be easier to form a successful coalition. The results form the basis for the development of a utility-based recommendation system that helps people choose optimal partners in an innovation network.

The Production of Step-Level Public Goods in Structured Social Networks: An Agent-Based Simulation

Francisco J. León-Medina, Francisco José Miguel Quesada and Vanessa Alcaide Lozano
Journal of Artificial Societies and Social Simulation 17 (1) 4

Kyeywords: Public Goods, Collective Behaviour, Decision Making, Social Networks
Abstract: This paper presents a multi-agent simulation of the production of step-level public goods in social networks. In previous public goods experimental research the design of the sequence ordering of decisions have been limited because of the necessity of simplicity taking priority over realism, which means they never accurately reproduce the social structure that constrains the available information. Multi-agent simulation can help us to overcome this limitation. In our model, agents are placed in 230 different networks and each networks’ success rates are analyzed. We find that some network attributes -density and global degree centrality and heterogeneity-, some initial parameters of the strategic situation -the provision point- and some agents’ attributes -beliefs about the probability that others will cooperate-, all have a significant impact on the success rate. Our paper is the first approach to an explanation for the scalar variant of production of public goods in a network using computational simulation methodology, and it outlines three main findings. (1) A less demanding collective effort level does not entail more success: the effort should neither be as high as to discourage others, nor so low as to be let to others. (2) More informed individuals do not always produce a better social outcome: a certain degree of ignorance about other agents’ previous decisions and their probability of cooperating are socially useful as long as it can lead to contributions that would not have occurred otherwise. (3) Dense horizontal groups are more likely to succeed in the production of step-level public goods: social ties provide information about the relevance of each agent’s individual contribution. This simulation demonstrates the explanatory power of the structural properties of a social system because agents with the same decision algorithm produce different outcomes depending on the properties of their social network.

An Agent-Based Model of Urgent Diffusion in Social Media

William Rand, Jeffrey Herrmann, Brandon Schein and Neža Vodopivec
Journal of Artificial Societies and Social Simulation 18 (2) 1

Kyeywords: Urgent Diffusion, Diffusion of Information, News, Social Networks, Twitter
Abstract: During a crisis, understanding the diffusion of information throughout a population will provide insights into how quickly the population will react to the information, which can help those who need to respond to the event. The advent of social media has resulted in this information spreading quicker then ever before, and in qualitatively different ways, since people no longer need to be in face-to-face contact or even know each other to pass on information in an crisis situation. Social media also provides a wealth of data about this information diffusion since much of the communication happening within this platform is publicly viewable. This data trove provides researchers with unique information that can be examined and modeled in order to understand urgent diffusion. A robust model of urgent diffusion on social media would be useful to any stakeholders who are interested in responding to a crisis situation. In this paper, we present two models, grounded in social theory, that provide insight into urgent diffusion dynamics on social networks using agent-based modeling. We then explore data collected from Twitter during four major urgent diffusion events including: (1) the capture of Osama Bin Laden, (2) Hurricane Irene, (3) Hurricane Sandy, and (4) Election Night 2012. We illustrate the diffusion of information during these events using network visualization techniques, showing that there appear to be differences. After that, we fit the agent-based models to the observed empirical data. The results show that the models fit qualitatively similarly, but the diffusion patterns of these events are indeed quite different from each other.

A Multi-Agent Emotional Society Whose Melodies Represent its Emergent Social Hierarchy and Are Generated by Agent Communications

Alexis Kirke and Eduardo Miranda
Journal of Artificial Societies and Social Simulation 18 (2) 16

Kyeywords: Social Networks, Music, Emotion
Abstract: In this article a multi-agent system is presented which generates melody pitch sequences with a hierarchical structure. The agents have no explicit melodic intelligence and generate the pitches as a result of artificial emotional influence and communication between agents, and the melody’s hierarchical structure is a result of the emerging agent social structure. The system is not a mapping from multi-agent interaction onto musical features, but actually utilizes music for the agents to communicate artificial emotions. Each agent in the society learns its own growing tune during the interaction process. Experiments are presented demonstrating that diverse and non-trivial melodies can be generated, as well as a hierarchical musical structure.

Modeling Education and Advertising with Opinion Dynamics

Thomas Moore, Patrick Finley, Nancy Brodsky, Theresa Brown, Benjamin Apelberg, Bridget Ambrose and Robert Glass
Journal of Artificial Societies and Social Simulation 18 (2) 7

Kyeywords: Opinion Dynamics, Social Networks, Media, Advertising
Abstract: We present a modified Deffuant-Weisbuch opinion dynamics model that integrates the influence of media campaigns on opinion. Media campaigns promote messages intended to inform and influence the opinions of the targeted audiences through factual and emotional appeals. Media campaigns take many forms: brand-specific advertisements, promotions, and sponsorships, political, religious, or social messages, and public health and educational communications. We illustrate model-based analysis of campaigns using tobacco advertising and public health education as examples. In this example, “opinion” is not just an individual’s attitude towards smoking, but the integration of a wide range of factors that influence the likelihood that an individual will decide to smoke, such as knowledge, perceived risk, perceived utility and affective evaluations of smoking. This model captures the ability of a media campaign to cause a shift in network-level average opinion, and the inability of a media message to do so if it promotes too extreme a viewpoint for a given target audience. Multiple runs displayed strong heterogeneity in response to media campaigns as the difference between network average initial opinion and broadcasted media opinion increased, with some networks responding ideally and others being largely unaffected. In addition, we show that networks that display community structure can be made more susceptible to be influenced by a media campaign by a complementary campaign focused on increasing tolerance to other opinions in targeted nodes with high betweenness centrality. Similarly, networks can be “inoculated” against advertising campaigns by a media campaign that decreases tolerance.

Degrees of Separation, Social Learning, and the Evolution of Cooperation in a Small-World Network

Nicholas Seltzer and Oleg Smirnov
Journal of Artificial Societies and Social Simulation 18 (4) 12

Kyeywords: Cooperation, Social Networks, Small-World, Modern Society, Simulation, Agent-Based
Abstract: We analyze a novel agent-based model of a social network in which agents make contributions to others conditional upon the social distance, which we measure in terms of the “degrees of separation” between the two players. On the basis of a simple imitation model, the emerging strategy profile is characterized by high levels of cooperation with those who are directly connected to the agent and lower but positive levels of cooperation with those who are indirectly connected to the agent. Increasing maximum interaction distance decreases cooperation with close neighbors but increases cooperation with distant neighbors for a net negative effect. On the other hand, allowing agents to learn and imitate socially distant neighbors increases cooperation for all types of interaction. Combining greater interaction distance with greater learning distance leads to a positive change in the total social welfare produced by the agents’ contributions.

The Effects of Network Structure on the Emergence of Norms in Adaptive Populations

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.

Modeling Pre-European Contact Coast Salish Seasonal Social Networks and Their Impacts on Unbiased Cultural Transmission

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.

Modeling Interaction Effects in Polarization: Individual Media Influence and the Impact of Town Meetings

Eric Pulick, Patrick Korth, Patrick Grim and Jiin Jung
Journal of Artificial Societies and Social Simulation 19 (2) 1

Kyeywords: Polarization, Media, Opinion, Social Networks, Town Meetings, Reinforcement
Abstract: We are increasingly exposed to polarized media sources, with clear evidence that individuals choose those sources closest to their existing views. We also have a tradition of open face-to-face group discussion in town meetings, for example. There are a range of current proposals to revive the role of group meetings in democratic decision-making. Here, we build a simulation that instantiates aspects of reinforcement theory in a model of competing social influences. What can we expect in the interaction of polarized media with group interaction along the lines of town meetings? Some surprises are evident from a computational model that includes both. Deliberative group discussion can be expected to produce opinion convergence. That convergence may not, however, be a cure for extreme views polarized at opposite ends of the opinion spectrum. In a large class of cases, we show that adding the influence of group meetings in an environment of self-selected media produces not a moderate central consensus but opinion convergence at one of the extremes defined by polarized media.

A Psychologically-Motivated Model of Opinion Change with Applications to American Politics

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.

Friendships and Social Networks in an Individual-Based Model of Primate Social Behaviour

Ivan Puga-Gonzalez and Cedric Sueur
Journal of Artificial Societies and Social Simulation 20 (3) 10

Kyeywords: Individual-Based Model, Friendships, Social Networks, Grooming, Aggression, Macaque Societies
Abstract: The individual-based model GrooFiWorld proposes a parsimonious theory explaining the complex behavior of macaque societies. It suggests that the socio-spatial structure of the group underlies the emergence of complex behaviour. A spatial structure with dominants at the center and subordinates at the periphery emerges due to aggression. This structure influences the distribution of social interactions: individuals interact more with close-by partners and thus several behavioural patterns emerge. In GrooFiWorld, however, individuals have no preferential interactions; whereas in primates, individuals prefer interactions with ‘friends’. The distribution of interactions, then, may be influenced by ‘friendships’ rather than spatial structure. To study this, here, we omitted space from the model and investigated the effects of ‘friendships’ on the emergence of social behaviour and network structure. Results show that ‘friendships’ promote cooperation but fail to produce other patterns characteristic of macaques. This highlights the importance that spatial structure may have in structuring macaque societies.

Emotion Modeling in Social Simulation: A Survey

Mathieu Bourgais, Patrick Taillandier, Laurent Vercouter and Carole Adam
Journal of Artificial Societies and Social Simulation 21 (2) 5

Kyeywords: Emotion, Social Simulation, Survey
Abstract: Emotions play a key role in human behavior. Being able to integrate them in models is thus a major issue to improve the believability of agent-based social simulations. However, even though these last years have seen the emergence of many emotional models usable for simulations, many modelers still tend to use simple ad hoc emotional models. To support this view, this article proposes a survey of the different practices of modelers in terms of implementations of emotional models. We then present different emotional architectures that already exist and that can be used by modelers. The main goal is to understand the way emotions are used today in social simulations, in order for the community to unify its uses of emotional agents.

Identifying Mechanisms Underlying Peer Effects on Multiplex Networks

Hang Xiong, Diane Payne and Stephen Kinsella
Journal of Artificial Societies and Social Simulation 21 (4) 6

Kyeywords: Peer Effects, Social Networks, Diffusion of Innovation, High-Value Crop
Abstract: We separately identify two mechanisms underlying peer effects in farm households' adoption of a new crop. A farmer can follow his peers to adopt a new crop because he learns knowledge about the new crop from them (social learning) and because he wants to avoid the damage caused by the practice conflicting with theirs (externalities). Using an agent-based model, we simulate the two mechanisms on a multiplex network consisting of two types of social relationships. The simulation model is estimated using detailed data of social networks, adoption and relevant socio-economic characteristics from 10 villages in China. We find that social learning -- in this case, the sharing of experiential resources -- among family members and production externalities between contiguous land plots both significantly influence farmers' adoption. Furthermore, sharing of experiential resources plays a significant role in the entire diffusion process and dominates the early phase, whereas externalities only matter in the late phase. This study shows the roles peer effects play in shaping diffusion can occur through different mechanisms and can vary as the diffusion proceeds. The work also suggests that agent-based models can help disentangle the role of social interactions in promoting or hindering diffusion.

Relational Integration in Schools Through Seating Assignments

Márta Radó and Károly Takács
Journal of Artificial Societies and Social Simulation 22 (4) 11

Kyeywords: Deskmates, Academic Performance, Intervention, Social Networks, Prejudice, Acting White
Abstract: Traditional desegregation policies have improved but not fully solved the problems associated with the reproduction of inequalities and interracial prejudice in schools. This is partly because social networks are inherently segregated within integrated schools and the benefits of contact have not fully materialized. Therefore, new kinds of policies are needed to further improve the situation. This paper investigates the consequences and efficiency of seating arrangements on academic outcomes and prejudice using an agent-based model that reflects real-life asymmetries. We model interpersonal dynamics and study behavior in the classroom in the hypothetical case of a single teacher who defines students’ seating arrangements. The model incorporates the mechanisms of peer influence on study behavior, on attitude formation, and homophilous selection in order to depict the interrelated dynamics of networks, behavior, and attitudes. We compare various seating arrangement scenarios and observe how GPA distribution and level of prejudice changes over time. Results highlight the advantages and disadvantages of seating strategies. In general, more heterogeneous deskmate pairs lead to a lower level of inequality and prejudice in the classroom, but this strategy does not favor talent management. Further, we evaluate outcomes compared to the absence of external intervention whereby students choose their own deskmates based on homophilous selection. Our model takes into account the fact that homophilous selection may be distorted due to the ‘Acting White’ phenomenon and pre-existing prejudice. Accounting for these factors implies slower convergence between advantaged and disadvantaged students.

Homophily as a Process Generating Social Networks: Insights from Social Distance Attachment Model

Szymon Talaga and Andrzej Nowak
Journal of Artificial Societies and Social Simulation 23 (2) 6

Kyeywords: Social Networks, Homophily, Social Distance Attachment, Configuration Model
Abstract: Real-world social networks often exhibit high levels of clustering, positive degree assortativity, short average path lengths (small-world property) and right-skewed but rarely power law degree distributions. On the other hand homophily, defined as the propensity of similar agents to connect to each other, is one of the most fundamental social processes observed in many human and animal societies. In this paper we examine the extent to which homophily is sufficient to produce the typical structural properties of social networks. To do so, we conduct a simulation study based on the Social Distance Attachment (SDA) model, a particular kind of Random Geometric Graph (RGG), in which nodes are embedded in a social space and connection probabilities depend functionally on distances between nodes. We derive the form of the model from first principles based on existing analytical results and argue that the mathematical construction of RGGs corresponds directly to the homophily principle, so they provide a good model for it. We find that homophily, especially when combined with a random edge rewiring, is sufficient to reproduce many of the characteristic features of social networks. Additionally, we devise a hybrid model combining SDA with the configuration model that allows generating homophilic networks with arbitrary degree sequences and we use it to study interactions of homophily with processes imposing constraints on degree distributions. We show that the effects of homophily on clustering are robust with respect to distribution constraints, while degree assortativity can be highly dependent on the particular kind of enforced degree sequence.

Halting SARS-CoV-2 by Targeting High-Contact Individuals

Gianluca Manzo and Arnout van de Rijt
Journal of Artificial Societies and Social Simulation 23 (4) 10

Kyeywords: Agent-Based Computational Models, Complex Social Networks, Virus Diffusion, Immunization Strategies, Epidemiological Models
Abstract: Network scientists have proposed that infectious diseases involving person-to-person transmission could be effectively halted by interventions targeting a minority of highly connected individuals. Could this strategy be effective in combating a virus partly transmitted in close-range contact, as many believe SARS-CoV-2 to be? Effectiveness critically depends on high between-person variability in the number of close-range contacts. We analyzed population survey data showing that the distribution of close-range contacts across individuals is indeed characterized by a small proportion of individuals reporting very high frequency contacts. Strikingly, we found that the average duration of contact is mostly invariant in the number of contacts, reinforcing the criticality of hubs. We simulated a population embedded in a network with empirically observed contact frequencies. Simulations showed that targeting hubs robustly improves containment.

BEN: An Architecture for the Behavior of Social Agents

Mathieu Bourgais, Patrick Taillandier and Laurent Vercouter
Journal of Artificial Societies and Social Simulation 23 (4) 12

Kyeywords: Social Simulation, Agent Architecture, BDI, Emotions, Personality, Emotional Contagion
Abstract: Over the last few years, the use of agent-based simulations to study social systems has spread to many domains (e.g., geography, ecology, sociology, economy). These simulations aim to reproduce real life situations involving human beings and thus need to integrate complex agents to match the behavior of the simulated people. Therefore, notions such as cognition, emotions, personality, social relationships or norms have to be taken into account, but there is currently no agent architecture that could incorporate all these features and be used by the majority of modelers, including those with low levels of skills in programming. In this paper, the BEN (Behavior with Emotions and Norms) architecture is introduced to tackle this issue. It is a modular architecture based on the BDI model of cognition and featuring modules to add emotions, emotional contagion, personality, social relationships and norms to agent behavior. This architecture is integrated into the GAMA simulation platform. An application of BEN to the simulation of the evacuation of a nightclub on fire is presented and shows the complexity of behaviors that may be developed with this architecture to create credible and expressive simulations.

Seed Selection Strategies for Information Diffusion in Social Networks: An Agent-Based Model Applied to Rural Zambia

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.