Equilibrium Computation in Discrete Network Games

71 Pages Posted: 11 Jul 2019 Last revised: 12 Jul 2019

See all articles by Michael P. Leung

Michael P. Leung

University of Southern California - Department of Economics

Date Written: July 11, 2019

Abstract

Computing game-theoretic equilibria is important in many applications, including counterfactual policy evaluation and model estimation. This paper is concerned with the computation of pure-strategy Nash equilibria in graphical games and pairwise stable and directed Nash stable networks in network formation games. We study a class of models obeying a restriction on the strength of strategic interactions, analogous to the assumption in the widely used linear-in-means model of social interactions that the magnitude of the endogenous peer effect is bounded below one. This is an empirically useful class to study because it has been shown that a sampling theory is possible under large-network asymptotics. We provide new algorithms for computing the equilibrium set and prove they have complexity O_p(n^c), where the randomness is with respect to the data-generating process, n is the number of agents, and c depends on the strength of strategic interactions.

Keywords: multiple equilibria, graphical games, network formation, empirical games

JEL Classification: C31, C57, C63, C73

Suggested Citation

Leung, Michael, Equilibrium Computation in Discrete Network Games (July 11, 2019). Available at SSRN: https://ssrn.com/abstract=3418017 or http://dx.doi.org/10.2139/ssrn.3418017

Michael Leung (Contact Author)

University of Southern California - Department of Economics ( email )

3620 South Vermont Ave.
Kaprielian (KAP) Hall, 310A
Los Angeles, CA 90089
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
25
Abstract Views
260
PlumX Metrics