Learning in Network Contexts: Experimental Results from Simulations

This paper describes the results of simulation experiments performed on a suite of learning algorithms. We focus on games in network contexts. These are contexts in which (1) agents have very limited information about the game; (2) play can be extremely asynchronous. There are many proposed learning algorithms in the literature. We choose a small sampling of such algorithms and use numerical simulation to explore the nature of asymptotic play. In particular, we explore the extent to which the asymptotic play depends on three factors, namely: limited information, asynchronous play, and the degree of responsiveness of the learning algorithm. JEL Subject Classi cation: C63, C72 Send proofs to: Amy Greenwald Computer Science Department Brown University, Box 1910 Providence, RI 02912