Information Bundling in a Dynamic Environment

Digital information goods potentially provide information producers with a new set of strategies, or price schedules, for offering these goods to a consumer population. If consumer preferences are known, then a producer can choose from the available schedules according to the profits they are able to extract. However, if the population is not known, then a producer must weigh a schedule’s complexity against its potential profitability. In addition, the producer must consider the learnability of the environment; is the consumer population changing, and how much information does it have about the nature of this change? In this work, we present a model of a monopolist producer learning the parameters of a number of price schedules when the consumer population is nonstationary. We adapt amoeba, an optimization algorithm, to effectively handle nonstationary profit landscapes and compare the performance of different price schedules as the rate of consumer change and knowledge about consumer preferences is varied. We find that two-part tariff, a two-parameter price schedule, performs well, due to its low complexity and its robustness to change in the consumer population.