Agent-Based Social Simulation for Policy Making

Abstract

In agent-based social simulations (ABSS), an artificial population of intelligent agents that imitate human behavior is used to investigate complex phenomena within social systems. This is particularly useful for decision makers, where ABSS can provide a sandpit for investigating the effects of policies prior to their implementation. During the Covid-19 pandemic, for instance, sophisticated models of human behavior enable the investigation of the effects different interventions can have and even allow for analyzing why a certain situation occurred or why a specific behavior can be observed. In contrast to other applications of simulation, the use for policy making significantly alters the process of model building and assessment, and requires the modelers to follow different paradigms. In this chapter, we report on a tutorial that was organized as part of the ACAI 2021 summer school on AI in Berlin, with the goal of introducing agent-based social simulation as a method for facilitating policy making. The tutorial pursued six Intended Learning Outcomes (ILOs), which are accomplished by three sessions, each of which consists of both a conceptual and a practical part. We observed that the PhD students participating in this tutorial came from a variety of different disciplines, where ABSS is mostly applied as a research method. Thus, they do often not have the possibility to discuss their approaches with ABSS experts. Tutorials like this one provide them with a valuable platform to discuss their approaches, to get feedback on their models and architectures, and to get impulses for further research.