Information Bundling in a Dynamic Environment

Markets for digital information goods provide the possibility of exploring new and more complex pricing schemes, due to information goods' flexibility and negligible marginal cost. In this paper we compare the dynamic performance of price schedules of varying complexity under two different specifications of consumer demand shifts. A monopolist producer employs a simple direct-search method that seeks to maximize profits using various price schedules. We find that the complexity of the price schedule affects both the amount of exploration necessary and the aggregate profit received by a producer. The size of the bundle offered, the rate of population change, and the number of iterations a producer can expect to interact with a population in total all affect the choice of schedule. If the number of iterations is small, a producer is best off randomly choosing a high-dimensional schedule, particularly when the bundle size is large. As the number of interactions between the producer and a given consumer population increases, then two-parameter schedules begin to perform best, as their learnability allows the producer to find highly optimal prices quickly. Our results have implications for automated learning and strategic pricing in non-stationary environments arising from changes in the consumer population, in individuals' preferences, or in the strategies of competing firms.