Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
What the 2007 TAC Market Design Game tells us about effective auction mechanisms
Autonomous Agents and Multi-Agent Systems
Market niching in multi-attribute computational resource allocation systems
Proceedings of the 13th International Conference on Electronic Commerce
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We present the problem of automatic co-niching in which potential suppliers of some product or service need to determine which offers to make to the marketplace at the same time as potential buyers need to determine which offers (if any) to purchase. Because both groups typically face incomplete or uncertain information needed for these decisions, participants in repeated market interactions engage in a learning process, making tentative decisions and adjusting these in the light of experiences they gain. Perhaps surprisingly, real markets typically then exhibit a form of parallel clustering: buyers cluster into segments of similar preferences and buyers into segments of similar offers. For computer scientists, the interesting question is whether such co-niching behaviours can be automated. We report on the first simulation experiments showing automated co-niching is possible using reinforcement learning in a multi-attribute product model. The work is of relevance to designers of online marketplaces, of computational resource allocation systems, and of automated software trading agents.