Modeling Radical Uncertainty and Anticipating Uncertain Change with Models

In this paper, I argue that agent-based modeling and simulation in a complexity framework can be useful scientific tools to think about the uncertain future. I will argue that the usefulness of this approach depends on its capability to generate surprise and discuss what conditions are needed for surprises to happen. The main value of agent-based models and simulations is to produce possible futures. This is the anticipation function of a model. It is important to emphasize that anticipating possible futures is different from predicting the most likely future evolution of some variables and requires a different understanding of what the science of economics can achieve. Therefore, I will discuss some epistemological thoughts related to the economic study of uncertain futures. Building on these foundations, the paper presents three methodological approaches of how agent-based models and simulations could be used to anticipate potential futures.