Pricing information bundles in a dynamic environment

We explore a scenario in which a monopolist producer of information goods seeks to maximize its profits in a market where consumer demand shifts frequently and unpredictably. The producer may set an arbitrarily complex price schedule-a function that maps the set of purchased items to a price. However, lacking direct knowledge of consumer demand, it cannot compute the optimal schedule. Instead, it attempts to optimize profits via trial and error. By means of a simple model of consumer demand and a modified version of a simple nonlinear optimization routine, we study a variety of parametrizations of the price schedule and quantify some of the relationships among learnability, complexity, and profitability. In particular, we show that fixed pricing or simple two-parameter dynamic pricing schedules are preferred when demand shifts frequently, but that dynamic pricing based on more complex schedules tends to be most profitable when demand shifts very infrequently.