Optimization of Individual and Regulatory Market Strategies with Genetic Algorithms

Abstract

An optimal policy problem is formulated for evolutionary market settings and analyzed in two applications at the micro- and macrolevels. First, individual portfolio policy is studied in case of a fully computerized, multiagent market system. We clarify the conditions under which static approaches—such as constraint optimization with stochastic rates or stochastic programming—apply in coevolutionary markets with strictly maximal players under scaled genetic algorithms. Convergence to global optimum is discussed for (a) coevolution of buying and selling strategies and for (b) coevolution of portfolio strategies and asset distributions over market players. Because only a finite population size in our setting suffices for the asymptotic convergence, the design criteria for genetic algorithm given (explicit cooling scheme for mutation and crossover, exponentiation schedule for fitness-selection) are of practical importance. Second, system optimization policy is studied for a model economy of Kareken and Wallace (1982) type. The income redistribution, monetary and market regulation policies are subjected to a supergenetic algorithm with various objective functions. In particular, the fitness function of a policy (i.e., a supercreature) is computed by means of a conventional genetic algorithm which is applied to the market players (creatures) in a fixed evaluation period. Here, the underlying genetic algorithm drives the infinite market dynamics and the supergenetic algorithm solves the optimal policy problem. Coevolution of consumption and foreign currency saving policies is discussed. Finally, a Java model of a stationary market was developed and made available for use and download.