Game Trees for Decision Analysis

Abstract

Game trees (or extensive-form games) were first defined by von Neumann and Morgenstern in 1944. In this paper, we examine the use of game trees for representing Bayesian decision problems. We propose a method for solving game trees using local computation. This method is a special case of a method due to Wilson for computing equilibria in 2-person games. Game trees differ from decision trees in the representations of information constraints and uncertainty. We compare the game tree representation and solution technique with other techniques for decision analysis such as decision trees, influence diagrams, and valuation networks.