Real-world decision-makers are forced to be locally constructive; that is, their decisions are necessarily constrained by their interaction networks, information, beliefs, and physical states. This study transforms an otherwise standard dynamic macroeconomic model into an open-ended dynamic game by requiring consumers and firms with intertemporal utility and profit objectives to be locally constructive. Tested locally-constructive decision processes for the consumers and firms range from simple reactive reinforcement learning to adaptive dynamic programming (ADP). Computational experiments are used to explore macroeconomic performance under alternative decision-process combinations relative to a social planner benchmark solution. A key finding is that simpler decision processes can outperform more sophisticated decision processes such as ADP. However, memory length permitting some degree of adaptive foresight is critical for good performance.;Note: Accepted for publication, Journal of Economic Dynamics and Control, September, 2015