DEPARTMENT OF COMPUTER SCIENCE
DOCTORAL DISSERTATION DEFENSE


Candidate: Amy Greenwald
Advisor: B. Mishra

LEARNING TO PLAY NETWORK GAMES

Tuesday, April 20, 1998, 10:30 a.m.
room 513, Warren Weaver Hall
251 Mercer Street

Abstract

This talk concerns the strategic behavior of automated agents in the framework of network game theory, with particular focus on the collective behavior that arises via learning. In particular, ideas are conveyed on both the theory and simulation of learning in network games, in terms of two sample applications. The first application is network control, presented via an abstraction known as the Santa Fe bar problem, for which it is proven that rational learning does *not* converge to Nash equilibrium, the classic game-theoretic solution concept. On the other hand, it is observed via simulations, that low-rationality learning, where agents trade-off between exploration and exploitation, typically converges to mixed strategy Nash equilibria in this game. The second application is the economics of shopbots - agents that automatically search the Internet for price and product information - in which learning yields behaviors ranging from price wars to tacit collusion, with sophisticated low-rationality learning algorithms converging to Nash equilibria. This work forms part of a larger research program that advocates learning and game theory as a framework in which to model the interactions of computational agents in network domains.