Empirical Game-Theoretic Methods for Strategy Design and Analysis in Complex Games

Abstract

The goal of my thesis work is to develop game analysis tech-niques capable of informing strategy design in large, com-plex games. Broadly defined, games are situations where multiple players make interacting decisions; each players’ choice depends on the choices of the other players. The for-mal study of games has origins in economics, but in recent years game theory has drawn increasing interest in com-puter science. Computer science offers a rich set of tools for advancing the art of game analysis, including simulation methods that can be used to explore strategic interactions in games. Game theory has achieved significant success and pop-ularity, but practitioners have had mixed success applying the theory to real strategy design problems (Roth 2002). An acute challenge in game theory applications is the size and complexity of real games, which often require players to choose among very large sets of distinct courses of action. A second (related) challenge is that players and analysts typi-cally face substantial uncertainty about the outcomes due to computational and observational limitations. Large games exacerbate these uncertainties to the extent that they arise from resource limitations. This form of uncertainty is partic-ularly difficult to characterize due to the intricacies of gath-ering evidence about game outcomes. Consider, for example, the Trading Agent Competition Supply Chain Management game (TAC SCM) (Arunacha-