Learning in network contexts: Experimental results from simulations
成果类型：
Article
署名作者：
Greenwald, A; Friedman, EJ; Shenker, S
署名单位：
Brown University; Rutgers University System; Rutgers University New Brunswick
刊物名称：
GAMES AND ECONOMIC BEHAVIOR
ISSN/ISSBN：
0899-8256
DOI：
10.1006/game.2000.0835
发表日期：
2001
页码：
80-123
关键词：
摘要：
This paper describes the results of simulation experiments performed on a suits of learning algorithms. We focus on games in network contexts. These are contexts in which (1) agents have very limited information about the game and (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: limited information, asynchronous play, and the degree of responsiveness of the learning algorithm. (C) 2001 Academic Press.