EMPIRICAL MODELS, RULES, AND OPTIMIZATION: TURNING POSITIVE ECONOMICS ON ITS HEAD

This paper considers supply decisions by firms in a dynamic setting with adjustment costsand compares the behavior of an optimal control model to that of a rule-based system whichrelaxes the assumption that agents are explicit optimizers. In our approach, the economicagent uses believably simple rules in coping with complex situations. We estimate rulesusing an artificially generated sample obtained by running repeated simulations of a dynamicoptimal control model of a firm’s hiring/firing decisions. We show that (i) agents usingheuristics can behave as if they were seeking rationally to maximize their dynamic returns;(ii) the approach requires fewer behavioral assumptions relative to dynamic optimization andthe assumptions made are based on economically intuitive theoretical results linking ruleadoption to uncertainty; (iii) the approach delineates the domain of applicability ofmaximization hypotheses and describes the behavior of agents in situations of economicdisequilibrium.The approach adopted uses concepts from fuzzy control theory. An agent, instead ofoptimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and outputdata, can be estimated and used to approximate any non-linear dynamic process. Empiricalresults indicate that the fuzzy rule-based system performs extremely well in approximatingoptimal dynamic behavior in situations with limited noise. Simulations are also performedunder increasingly noisy.