Co-learning Segmentation in Marketplaces

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.