Macroeconomies as constructively rational games

Real-world decision-makers are forced to be locally constructive, in the sense that their actions are constrained by their interaction networks, information, beliefs, and physical states. This study poses the following question: Suppose utility-seeking consumers and pro t-seeking rms in an otherwise standard dynamic macroeconomic model are required to be locally constructive decision-makers, unaided by the external imposition of coordination and optimality conditions. What combinations of locally-constructive decision rules result in good macroeconomic performance relative to a social planner benchmark model, and what are the game-theoretic properties of these decision-rule combinations? We begin our investigation of this question by specifying locally-constructive decision rules for the consumers and rms that range from simple reinforcement learning to sophisticated adaptive dynamic programming algorithms. We then use computational experiments to explore macroeconomic performance under alternative decision-rule combinations. A key nding is that simpler rules can outperform more sophisticated rules, but that adaptive foresight coupled with a relatively long memory permitting past observations to inform current decision-making is critical for good performance. JEL Codes: B4, C6, C7, E03, E2