Automated Learning of Pricing and Bundling Strategies in Information Economies

The emergence of the Internet and the potential of software agents for conducting electronic commerce presents a new set of challenges for producers of information goods. We discuss how a producer of information goods can learn a price schedule to charge and a set of goods to offer while contending with an unknown and changing consumer population and competition from other producers. We take a decision- theoretic approach, emphasizing the cost of learning and the need for a producer to quickly find an acceptable solution, due to the dynamics of the problem and the small number of potential interactions with a consumer population. We also discuss the use of taxonomic information as a guide when selecting which goods to offer.

Authors

• Christopher H. Brooks and Edmund H. Durfee University of Michigan

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