**Introduction.**
In this paper we interlink a dynamic programming, a game theory and
a behavioral simulation approach to the same problem of economic
exchange.

The size and complexity of the strategy sets for even a simple infinite
horizon exchange economy are so overwhelmingly large that it is
reasonably clear that individuals do not indulge in exhaustive search over
even a large subset of the potential strategies. Furthermore unless one
restricts the unadorned definition of a noncooperative equilibrium to a
special form such as a perfect noncooperative equilibrium, almost any
outcome can be enforced as an equilibrium by a sufficiently ingenious
selection of strategies. In essence, almost anything goes, unless the
concept of what constitutes a satisfactory solution to the game places
limits on permitted or expected behavior. The latter presumes that the
players follow the same introspective process as the game-theorist. As
these refinements may be hard to justify, it is interesting to complement
this introspective approach with a study of whether interactive market
processes provide enough structure to tie down the set of strategies
played.

Karatzas, Shubik and Sudderth [1992] formulated a simple infinite
horizon economic exchange model involving a continuum of agents as
a set of parallel dynamic programs, and were able to establish the
existence of a stationary noncooperative equilibrium. In order to obtain
an explicit closed form solution for the optimal policy and equilibrium
wealth distribution, it relies on a particular utility function. In order to
match these analytical results with a behavioral approach, we first
develop simulation models of market processes with agents learning
through reinforcement. Second, we consider more general classes of
utility functions.

**J.E.L. classification codes.** C61, C63, C72, C73, D83, D91

**Keywords.** Market game, dynamic programming, Classifier
System, adaptive behavior