Network Models for Game Theory and Economics
Michael Kearns, Computer and Information Science, University of Pennsylvania
Feb 27, 2004

Abstract

Over the last several years, a number of authors have developed
graph-theoretic or network models for large-population game theory
and economics. In such models, each player or organization is represented
by a vertex in a graph, and payoffs and transactions are restricted to
obey the topology of the graph. This allows the detailed specification
of rich structure (social, technological, organizational, political,
regulatory) in strategic and economic systems.

In this talk, I will survey these models and the attendant algorithms
for certain basic computations, including Nash, correlated, and
Arrow-Debreu equilibria. Connections to related topics, such as
Bayesian and Markov networks for probabilistic modeling and inference,
will be discussed. Time permitting, I will briefly discuss some
recent experimental work marrying this general line of thought with
topics in social network theory.