Multi-agent Social Simulation Research Articles

Understanding dynamics of polarization via multiagent social simulation

It is widely recognized that the Web contributes to user polarization, and such polarization affects not just politics but also peoples’ stances about public health, such as vaccination. Understanding polarization in social networks is challenging because it depends not only on user attitudes but also their interactions and exposure to information. We adopt Social Judgment Theory to operationalize attitude shift and model user behavior based on empirical evidence from past studies. We design a social simulation to analyze how content sharing affects user satisfaction and polarization in a social network. We investigate the influence of varying tolerance in users and selectively exposing users to congenial views. We find that (1) higher user tolerance slows down polarization and leads to lower user satisfaction; (2) higher selective exposure leads to higher polarization and lower user reach; and (3) both higher tolerance and higher selective exposure lead to a more homophilic social network.

Evaluating agents’ trustworthiness within virtual societies in case of no direct experience

A great deal of effort has been made to introduce trust models to assess trustworthiness within virtual societies. The great majority of them makes extensive use of direct experience as the main source of information, considering recommendation/reputation and inferential processes just later, as a secondary mechanism to refine trust assessment. In this kind of networks, unfortunately, direct experience might not always represent the best solution to assess trustworthiness. In fact, their highly dynamic structure promotes an increase of the average number of interconnections among agents. This in turn negatively affects the degree of knowledge the agents possess about each specific individual, i.e. direct experience. To date, however, it has not been said much about how to face these situations.It is fundamental to find an effective approach for trust assessment even in lack of direct experience, which is the central focus of this research. By the means of a multi-agent social simulation, we consider the situation in which an agent can just access indirect knowledge for trust assessment, namely recommendations of specific individuals or whole categories of individuals. Then, we compare the efficiency of these two approaches in order to identify when it is more convenient to rely on the first or on the second one. As expected, our results confirm that the dynamic nature of these networks strongly affects the role of categories. We modeled this feature introducing the “turnover” in the simulations, whereby the higher is the turnover the more convenient it is relying on categories. Besides this confirmatory result, our simulations highlight the higher degree of robustness of categories in the presence of unreliable recommenders. Such a result is even more significant if there is no available information about how reliable the recommenders are.The results we obtained are in accordance with the current literature and can be of important interest for the development of this sector.

Multiagent Social Simulation for Emergency Evacuation

Multiagent Social Simulation for Emergency Evacuation

Roadmap and research issues of multiagent social simulation using high-performance computing

In this article, we present roadmaps and research issues pertaining to multiagent social simulation to illustrate the directions of technological achievements in that domain. Compared with physical simulation, social simulation is still in the phase of establishing simulation models. We focus on four issues, namely “undetermined model”, “awareness effects”, “obscure boundary”, and “incomplete data”, and consider ways to overcome these issues using the massive computational power of high-performance computing. We select three applications, namely evacuation, road traffic, and market, and estimate the required computational cost of real applications. Moreover, we investigate research issues on the application side and categorize possible future works on multiagent social simulations.

Growing Social Structure: An Empirical Multiagent Excursion into Kinship in Rural North-West Frontier Province

Author(s): Geller, Armando; Harrison, Joseph F.; Revelle, Matthew | Abstract: Kinship is an essential factor in human social life. Many years of research devoted to develop a better understanding of kinship bear witness to this fact. Important advances were made on conceptual, modeling and empirical grounds. Computational social science---in particular through social network analysis and social simulation---contributed its part to it. Notwithstanding, multiagent simulations of social systems rarely take into account kinship-based social interactions, especially when claiming to be empirical. We combine generative social science's basal argument grow it! with the concept that social structure is not reified, but a pattern emerging from interactions between individuals, and introduce a multiagent social simulation that grows kinship structures on the basis of socio-demographic and marriage interactions in Pakistan's Rural North-West Frontier Province. The modeling proposed has generalizable demonstrator value in that it is shown how published ethnographic data can be used in building credible and cross-validateable formal models that then can be re-used in modular model building of larger models of society. We conclude with a critical discussion of taking into account endogenized social structure in formal models of conflict.

Combining Empirical Data with Multi Agent Social Simulation: Investigating Micro-Macro Links in Stock Markets

This paper investigates micro-macro links in stock markets. Empirical investigations on individual investors' decision-making and interactions are used to formalize agent rules for an artificial stock market. Simulation of the social interactions of multiple agents in this market generates macro results, like stock market prices and returns over time. The outcomes of the artificial market are finally compared to empirical facts from real stock markets. Taken together, the paper is the first example of an Axtell & Epstein level 3 model in financial markets.