Equilibrium of E-markets populated with reputation oriented learning agents

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 focusses 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. Thomas Tran, Ph.D.: He is an Assistant Professor in the School of Information Technology and Engineering at the University of Ottawa. He received his Ph.D. from the University of Waterloo in 2004. His current research work is on Multi-Agent Systems, Intelligent Agents, Reinforcement Learning, Trust and Reputation Modelling, Agent Negotiation, Mechanism Design and Applications of AI to E-Commerce. Robin Cohen, Ph.D.: She is a Professor in the School of Computer Science at the University of Waterloo. She received her Ph.D. from the University of Toronto in 1983. Her current research work is on User Modeling, Intelligent Interaction, Multi-Agent Systems, Adjustable Autonomy and Mixed-Initiative Systems and Dialogue, including Applications to E-Commerce.