Model Selection in an Information Economy: Choosing What to Learn

Computational Intelligence, Vol. 18, No. 4, pp. 566-582, November 2002

Posted: 7 Apr 2007

See all articles by Christopher H. Brooks

Christopher H. Brooks

University of San Francisco

Robert S. Gazzale

University of Toronto - Department of Economics; Williams College - Department of Economics

Rajarshi Das

IBM Research

Jeffrey O. Kephart

IBM Research

Jeffrey K. MacKie-Mason

UC Berkeley; University of Michigan

Edmund Durfee

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

Abstract

In an economy in which a producer must learn the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the producer has a limited number of chances to experiment, it must explicitly consider the cost of learning (in terms of foregone profit) against the value of the information acquired. Information goods add an additional dimension to this problem; due to their flexibility, they can be bundled and priced according to a number of different price schedules. An optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule.

In this paper, we demonstrate the tradeoff between complexity and profitability for a number of common price schedules. We begin with a one-shot decision as to which schedule to learn. Schedules with moderate complexity are preferred in the short and medium term, as they are learned quickly, yet extract a significant fraction of the available profit. We then turn to the repeated version of this one-shot decision and show that moderate complexity schedules, in particular two-part tariff, perform well when the producer must adapt to nonstationarity in the consumer population. When a producer can dynamically change schedules as it learns, it can use an explicit decision-theoretic formulation to greedily select the schedule which appears to yield the greatest profit in the next period.

Suggested Citation

Brooks, Christopher H. and Gazzale, Robert S. and Das, Rajarshi and Kephart, Jeffrey O. and MacKie-Mason, Jeffrey K. and Durfee, Edmund, Model Selection in an Information Economy: Choosing What to Learn. Computational Intelligence, Vol. 18, No. 4, pp. 566-582, November 2002, Available at SSRN: https://ssrn.com/abstract=978060

Christopher H. Brooks (Contact Author)

University of San Francisco ( email )

2130 Fulton Street
San Francisco, CA 94117
United States

Robert S. Gazzale

University of Toronto - Department of Economics ( email )

150 St. George Street
Toronto, Ontario M5S 3G7
Canada
416.978.2123 (Phone)

HOME PAGE: http://www.economics.utoronto.ca/gazzale/

Williams College - Department of Economics ( email )

24 Hopkins Hall Drive
Williamstown, MA 01267
United States

Rajarshi Das

IBM Research ( email )

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

Jeffrey O. Kephart

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/

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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