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