Automated Strategy Searches in an Electronic Goods Market: Learning and Complex Price Schedules

Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 31-40, 1999

Posted: 6 Apr 2007

See all articles by Christopher H. Brooks

Christopher H. Brooks

University of San Francisco

Scott A. Fay

University of Florida, Department of Marketing; University of Florida

Rajarshi Das

IBM Research

Jeffrey K. MacKie-Mason

UC Berkeley; University of Michigan

Jeffrey O. Kephart

IBM Research

Edmund Durfee

University of Michigan at Ann Arbor - Department of Electrical Engineering and Computer Science

Abstract

In an automated market for electronic goods new problems arise that have not been well studied previously. For example, information goods are very flexible. Marginal costs are negligible and nearly limitless bundling and unbundling of these items are possible, in contrast to physical goods. Consequently, producers can offer complex pricing schemes. However, the profit-maximizing design of a complex pricing schedule depends on a producer's knowledge of the distribution of consumer preferences for the available information goods. Preferences are private and can only be gradually uncovered through market experience. In this paper we compare dynamic performance across price schedules of varying complexity. We provide the producer with two machine learning methods producer that is performing a naive, knowledge-free form of leanings (function approximation and hill-climbing) which implement a strategy that balances exploitation to maximize current profits against exploration of the profit landscape to improve future profits. We find that the tradeoff between exploitation and exploration is different depending on the learning algorithms employed, and in particular depending on the complexity of the price schedule that if offered. In general, simpler price schedules are more robust and give up less profit during the learning periods even though in our stationary environment learning eventually is complete and the more complex schedules have high long-run profits. These results hold for both learning methods, even though the relative performance of the methods is quite sensitive to choice of initial conditions and differences in the smoothness of the profit landscape for different price schedules. Our results have implications for automated learning and strategic pricing in non-stationary environments, which arise when the consumer population changes, individuals change their preferences, or competing firms change their strategies.

Suggested Citation

Brooks, Christopher H. and Fay, Scott A. and Fay, Scott A. and Das, Rajarshi and MacKie-Mason, Jeffrey K. and Kephart, Jeffrey O. and Durfee, Edmund, Automated Strategy Searches in an Electronic Goods Market: Learning and Complex Price Schedules. Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 31-40, 1999, Available at SSRN: https://ssrn.com/abstract=978315

Christopher H. Brooks (Contact Author)

University of San Francisco ( email )

2130 Fulton Street
San Francisco, CA 94117
United States

Scott A. Fay

University of Florida, Department of Marketing ( email )

PO Box 117165, 201 Stuzin Hall
Gainesville, FL 32610-0496
United States

University of Florida ( email )

211 Bryan Hall
PO Box 117150
Gainesville, FL 32611
United States
352-392-0161 (Phone)
Not available (Fax)

Rajarshi Das

IBM Research ( email )

T. J. Watson Research Center
1 New Orchard Road
Armonk, NY 10504-1722
United States

Jeffrey K. MacKie-Mason

UC Berkeley ( email )

102 South Hall
Berkeley, CA 94720-4600
United States

HOME PAGE: http://jeff-mason.com

University of Michigan ( email )

Ann Arbor, MI 48109-1092
United States

HOME PAGE: http://http:/jeff-mason.com/

Jeffrey O. Kephart

IBM Research ( email )

T. J. Watson Research Center
1 New Orchard Road
Armonk, NY 10504-1722
United States

Edmund Durfee

University of Michigan at Ann Arbor - Department of Electrical Engineering and Computer Science ( email )

1101 Beal Avenue
Ann Arbor, MI 48109
United States

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