Exploring interventions on social outcomes with in silico, agent-based experiments

Agent-Based Modeling (ABM) is a computational method used to examine social outcomes emerging from interaction between heterogeneous agents by computer simulation. It can be used to understand the effect of initial conditions on complex outcomes by exploring fine-grained (multiple-scale, spatial/temporal) observations on the aggregate consequences of agent interaction. By performing in silico experimental tests on policy interventions where ex ante predictions of outcomes are difficult, it can also reduce costs, explore assumptions and boundary conditions, as well as overcome ethical constraints associated with the use of randomized controlled trials in behavioral policy. Here, we introduce the essential elements of ABM and present two simple examples where we assess the hypothetical impact of certain policy interventions while considering different possible reactions of individuals involved in the context. Although highly abstract, these examples suggest that ABM can be either a complement or an alternative to behavioral policy methods, especially when understanding social processes and exploring direct and indirect effects of interventions are important. Prospects and critical problems of these in silico policy experiments are then discussed.