Automated Learning of Pricing and Bundling Strategies in Information Economies

Abstract

competing with other producers for a market of consumers, the producer must consider both the strategy of other producers and the preferences of the consumers. Typically, neither of these are known, but instead must be learned over time. What makes this difficult is that the other producers are also trying to learn about consumer preferences, so producer strategies are not necessarily stationary. One question we are concerned with is the extent to which reasoning about opponent strategies is actually useful; there will be a tradeoff between an increase in the degree of modeling detail and the marginal gain in the value of the information acquired. One factor that makes this problem different from a traditional game theory problem is that we are explicitly interested in the nonequilibrium rewards that a producer receives. That is, it's not enough for a producer to eventually learn an This work was supported in part by an IBM University Partnership Grant and by the National Science