Abstract
We investigate the extent to which agents can learn to
coordinate on stationary perfect-foresight cycles in a
general-equilibrium environment. Depending on the value
of a preference parameter, the limiting backward
(direction of time reversed) perfect-foresight dynamics
are characterized by steady-state, periodic, or chaotic
trajectories for real money balances. We relax the
perfect-foresight assumption and examine how a
population of artificial, heterogeneous adaptive agents
might learn in such an environment. These artificial
agents optimize given their forecasts of future prices,
and they use forecast rules that are consistent with
steady-state or periodic trajectories for prices. The
agents' forecast rules are updated by a genetic
algorithm. We find that the population of artificial
adaptive agents is able eventually to coordinate on
steady state and low-order cycles, but not on the
higher-order periodic equilibria that exist under the
perfect-foresight assumption.
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