Sociality, Sanctions, Damaging Behaviors: A Distributed Implementation of an Agent-Based Social Simulation Model

摘要

The explanatory and predictive power of social simulations is more and more connected with the development of models accounting for the complexity of real (inter-individual and intra-individual) social dynamics. From this perspective, a promising research path is complementing very simple models, more suitable to illuminate core dynamics of social phenomena, with increasingly more complex and empirically grounded simulations (big data-driven models, higher number of agents, more detailed and realistic description of cognitive and communication mechanisms underlying individual and group behaviors). The choice has two strictly intertwined effects: not only a different modeling approach, but also the need for more powerful tools. The paper presents a distributed implementation of an agent-based model exploring the interplay between damaging behaviors, sanctions and social mechanisms of learning and imitation, a topic investigated in many areas of social science from economics to legal science. Taking cue from a previous work based on a simple NetLogo simulation, the work shows how distributed solutions can help in developing more complex, wide and semantically rich models.