A moderate role for cognitive models in agent-based modeling of cultural change

Abstract

Abstract Purpose Agent-based models are typically “simple-agent” models, in which agents behave according to simple rules, or “complex-agent” models which incorporate complex models of cognitive processes. I argue that there is also an important role for agent-based computer models in which agents incorporate cognitive models of moderate complexity. In particular, I argue that such models have the potential to bring insights from the humanistic study of culture into population-level modeling of cultural change. Methods I motivate my proposal in part by describing an agent-based modeling framework, POPCO, in which agents’ communication of their simulated beliefs depends on a model of analogy processing implemented by artificial neural networks within each agent. I use POPCO to model a hypothesis about causal relations between cultural patterns proposed by Peggy Sanday. Results In model 1, empirical patterns like those reported by Sanday emerge from the influence of analogies on agents’ communication with each other. Model 2 extends model 1 by allowing the components of a new analogy to diffuse through the population for reasons unrelated to later effects of the analogy. This illustrates a process by which novel cultural features might arise. Conclusions The inclusion of relatively simple cognitive models in agents allows modeling population-level effects of inferential and cultural coherence relations, including symbolic cultural relationships. I argue that such models of moderate complexity can illuminate various causal relationships involving cultural patterns and cognitive processes.