New Generation Computing, 23(2005)33-41
Ohmsha, Ltd. and Springer

Equilibrium of E-Markets Populated with Reputation Oriented Learning Agents

Thomas TRAN and Robin COHEN
School of Computer Science
University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
{tt5tran,rcohen}@uwaterloo.ca

Received 29 November 2002

Abstract

In this paper, we propose reputation oriented reinforcement learning algorithms for buying and selling agents in electronic marketplaces. We consider the fact that the quality of a good offered by multiple selling agents may not be the same, and a selling agent may alter the quality of its goods. In our approach, buying agents learn to avoid the risk of purchasing low quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable and disreputable sellers. Selling agents learn to maximize their expected profits by adjusting product prices and optionally altering the quality of their goods. This paper focuses on presenting results from experiments investigating the behaviour of an e-market populated with our buying and selling agents. Our results show that such a market can reach an equilibrium state where the agent population remains stable, and this equilibrium is optimal for the participant agents.

Keywords:Multi-Agent Systems, Learning Agents, Electronic Marketplaces, Reinforcement Learning, Reputation Modelling.

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