Introducing Forward-Looking Intertemporal Optimization in an Agent-Based Model

The paper proposes a computational approach for including forward-looking intertemporally optimizing agents in agent-based models. Optimization is implemented considering, on the one hand that revision of economic behavior does not occur continuously over time but only when individual circumstances suggest or impose it, and, on the other hand, that, given the inherent uncertainty and complexity of the economic system, the planning horizon is finite. We propose a macroeconomic model with a large population of household agents. Each period a random sample of them resets their propensities to consume and invest by maximizing their intertemporal utility. They then stick to these optimally set quantities until they are again selected for optimization. The study is a primer in considering the joint effect of heterogeneous agents' interaction and forward-looking behavior, and provides novel insights into the mechanism of transmission of individual choices to the macroeconomy. The heavy computational tasks are managed through the development of new programming tools. The coexistence of interaction and forward-looking behavior generates interesting coordination dynamics. The results suggest that even a tiny fraction of optimizing agents over the whole population has a significant effect of aggregate output, but this effect is nonlinear and conditional on the length of the planning horizon.