On the convergence of genetic learning in a double auction market

We study the learning behavior of a population of buyers and a population o f sellers whose members are repeatedly randomly matched to engage in a seal ed bid double auction. The agents are assumed to be boundedly rational and choose their strategies by imitating successful behavior and adding innovat ions triggered by random errors or communication with other agents. This pr ocess is modelled by a two-population genetic algorithm. A general characte rization of the equilibria in mixed population distributions is given and i t is shown analytically that only one price equilibria are attractive for t he GA dynamics. Simulation results confirm these findings and imply that in cases with random initialization with high probability the gain of trade i s equally split between buyers and sellers. (C) 1999 Elsevier Science B.V. All rights reserved.