Agent-based simulation models are an increasingly popular tool for research and management in many, different and diverse fields. In executing such simulations the "speed" is one of the most general and important issues and the traditional answer to this issue is to invest resources in deploying a dedicated installation of dedicated computers, with highly specialized parallel applications, devoted to the purpose of achieving extreme computational performances. In this paper we present our experience with a distributed framework, D-MASON, that is a distributed version of MASON, a well-known and popular library for writing and running Agent-based simulations. D-MASON introduces the parallelization at framework level so that scientists that use the framework (domain expert but with limited knowledge of distributed programming) can be only minimally aware of such distribution. The framework allowed only a static decomposition of the work among workers, and was not able to cope with load unbalance among them, therefore incurring in serious performance degradation where, for example, many of the agents were concentrate on one specific part of the space. We elaborated two strategies for ameliorate the balancing and enhance the synchronization among workers. We present their design principles and the experimental tests that validate our approach.
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.
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