Abstract
In this chapter a review of the different uses of agent-based modeling for investigating social behaviors and dynamics is presented. Agent-based modeling is often used to study sociality both from a behavioral and evolutionary perspective.
The aim of this chapter is to present the strengths of this modeling approach, highlighting the usefulness of—and in some scientific domains, the need for—cognitively rich architectures, as, for instance, in studying the emergence of communicative systems and information sharing.
The main reasons supporting this perspective are different. On one hand, social behavior (and not collective behavior) of living organisms in most cases is possible because individuals have cognitive abilities and skills that allow them to interact in complex ways with other individuals and with the environment where they live. On the other hand, while analytical modeling simplifies properties and behaviors of a real system to find regularities and equilibria (and this may limit the applicability of this approach), agent-based modeling adopts the opposite philosophy: agents are heterogeneous—they behave in different ways and they use different strategies to deal with unexpected events and changing environments.
The use of agent-based modeling is consolidated in investigating complex social behaviors and dynamics in some specific scientific domains (e.g., the study of cooperative behavior in living organisms); but potentially, agent-based modeling may represent a useful scientific method and tool in many other scientific fields investigating complex systems and dynamics.
The study of social-ecological systems is presented as one of the main scientific fields where the use of agent-based modeling is gaining more and more attention and where it has already shown itself to be relevant.
Finally, another scientific field—namely, the study of Earth Systems and Dynamics—is presented to highlight the promising applications of agent-based models in helping to better understand complex and nonlinear phenomena (e.g., tipping points) where the social component (i.e., the human component) is crucial and plays a central role.
The scientific journey presented in this chapter should clarify the enormous potential of agent-based modeling from an interdisciplinary and multi-scale perspective, stressing the centrality of individual cognitive skills and abilities.
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Campennì, M. (2016). Cognitively Rich Architectures for Agent-Based Models of Social Behaviors and Dynamics: A Multi-Scale Perspective. In: Cecconi, F. (eds) New Frontiers in the Study of Social Phenomena. Springer, Cham. https://doi.org/10.1007/978-3-319-23938-5_2
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