Learning in Network Contexts: Experimental Results from Simulations
Amy Greenwald, Eric Friedman, and Scott Shenker
Abstract
This paper describes the results of simulation experiments performed
on a suite of learning algorithms. We focus on games in {\em network
contexts}. These are contexts in which (1) agents have very limited
information about the game; users do not know their own (or any other
agent's) payoff function, they merely observe the outcome of their
play. (2) Play can be extremely asynchronous; players update their
strategies at very different rates.
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.