Agent-Based Modelling: A Bridge Between Psychology and Macro-social Science

Abstract

Agent-based modelling is the practice of creating artificial agents and environments and monitoring how these interact over time. This provides a computational framework to study how individual agents influence and are influenced by the social systems that result from their interactions. These methods are useful to macropsychologists, as they might serve as a bridge between traditional, small-scale behavioural science and large-scale social and societal systems. The aim of this chapter is to provide the reader with a sense of how agent-based models and modelling can be used to generate novel insights by scaling up basic psychological processes in artificial environments. We will illustrate this process with an early and influential case study of agent-based modelling: how simulations of the iterated prisoner’s dilemma informed thinking about how cooperation is established, promoted, and challenged in society. We hope this provides an illustrative example of how agent-based modelling can be used and how these methods have matured over time. We conclude by moving beyond cooperation, to two contemporary examples which highlight how agent-based modelling can speak to issues that macropsychologists care about such as how to strengthen democratic societies and how to minimise structural bias against minorities.