On Designing Economic Agents That Behave Like Human Agents

This paper explores the idea of constructing theoretical economic agents that behave like actual human agents and using them in neoclassical economic models. It does this in a repeated-choice setting by postulating "artificial agents" who use a learning algorithm calibrated against human learning data from psychological experiments. The resulting calibrated algorithm appears to replicate human learning behavior to a high degree and reproduces several "stylized facts" of learning. It can, therefore, be used to replace the idealized, perfectly rational agents in appropriate neoclassical models with "calibrated agents" that represent actual human behavior. The paper discusses the possibilities of using the algorithm to represent human learning in normal-form stage games and in more general neoclassical models in economics. It explores the likelihood of convergence to long-run optimality and to Nash behavior, and the "characteristic learning time" implicit in human adaptation in the economy.