Pricing Information Bundles in a Dynamic Environment
(Download full paper)Filed under research category: Information content economics
Tagged as: e-commerce information_goods multi-agent_systems online_markets bundling information_pricing machine_learning
Authors
Jeffrey O. Kephart, Rajarshi Das, Christopher H. Brooks, Edmund H. Durfee, Robert S. Gazzale and Jeffrey K. MacKie-Mason
Abstract
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 is free to set an arbitrarily complex price schedule-a function that maps the set of purchased items to a price-but without direct knowledge of consumer demand it cannot compute the optimal schedule. Instead, it must employ a form of optimization based on 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 parameterizations of the price schedule and quantity 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 consumer demand shifts frequently, but that dynamic pricing based on more complex schedules tends to be most profitable when consumer demand shifts very infrequently.
Citation
ACM Electronic Commerce 2001, Tampa.