2 articles matched your search for
Philosophy of Social Science, Causal Explanation, Functional Explanation, Mechanism Explanation, Analytic Sociology
Journal of Artificial Societies and Social Simulation 15 (3) 1
Kyeywords: Philosophy of Social Science, Causal Explanation, Functional Explanation, Mechanism Explanation, Analytic Sociology
Abstract: What kind of knowledge can we obtain from agent-based models? The claim that they help us to study the social world needs unpacking. I will defend agent-based modelling against a recent criticism that undermines its potential as a method to investigate underlying mechanisms and provide explanations of social phenomena. I show that the criticism is unwarranted and the problem can be resolved with an account of explanation that is associated with the social sciences anyway, the mechanism account of explanation developed in Machamer et al. (2000). I finish off discussing the mechanism account with relation to prediction in agent-based modelling.
Francisco J. León-Medina
Journal of Artificial Societies and Social Simulation 22 (2) 4
Kyeywords: Opinion Dynamics, Mechanism Explanation, Agent-Based Modeling, Homophily, Social Influence, Social Network
Abstract: Opinion dynamics models usually center on explaining how macro-level regularities in public opinion (uniformity, polarization or clusterization) emerge as the effect of local interactions of a population with an initial random distribution of opinions. However, with only a few exceptions, the understanding of patterns of public opinion change has generally been dismissed in this literature. To address this theoretical gap in our understanding of opinion dynamics, we built a multi-agent simulation model that could help to identify some mechanisms underlying changes in public opinion. Our goal was to build a model whose behavior could show different types of endogenously (not induced by the researcher) triggered transitions (rapid or slow, radical or soft). The paper formalizes a situation where agents embedded in different types of networks (random, small world and scale free networks) interact with their neighbors and express an opinion that is the result of different mechanisms: a coherence mechanism, in which agents try to stick to their previously expressed opinions; an assessment mechanism, in which agents consider available external information on the topic; and a social influence mechanism, in which agents tend to approach their neighbor’s opinions. According to our findings, only scale-free networks show fluctuations in public opinion. Public opinion changes in this model appear as a diffusion process of individual opinion shifts that is triggered by an opinion change of a highly connected agent. The frequency, rapidity and radicalness of the diffusion, and hence of public opinion fluctuations, positively depends on how influential external information is in individual opinions and negatively depends on how homophilic social interactions are.