A genetic game of trade, growth and externalities

Abstract

This dissertation introduces a new adaptive search algorithm, Genetic Algorithm (GA), for dynamic game applications. Since GAs require little knowledge of the problem itself, computations based on these algorithms are very attractive for optimizing complex dynamic structures. Part one discusses GA in general, and dynamic game applications in particular. Part two is comprised of three essays on computational economics. In Chapter one, a genetic algorithm is developed to approximate open-loop Nash equilibria in non-linear difference games of fixed duration. Two sample problems are provided to verify the success of the algorithm. Chapter two covers discrete-time dynamic games with more than two conflicting parties. In games with more than two players, there arises the possibility of coalitions among groups of players. A three-country, two-bloc trade model analyzes the impact of coalition formation on optimal policies. Chapter three extends GA further to solve open-loop differential games of infinite duration. In a dynamic North/South trade game with transboundary knowledge spillover and local pollution optimal policies are searched. Cooperative and noncooperative modes of behavior are considered to address the welfare effects of pollution and knowledge externalities.