Improving Learning Performance by Applying Economic Knowledge

Christopher H. Brooks, Jeffrey K. MacKie Mason, Robert S. Gazzale and Edmund H. Durfee
Additional contact information
Christopher H. Brooks: University of San Francisco
Jeffrey K. MacKie Mason: University of Michigan
Robert S. Gazzale: Williams College
Edmund H. Durfee: University of Michigan

No 2004-01, Department of Economics Working Papers from Department of Economics, Williams College

Abstract: Digital information economies require information goods producers to learn how to position themselves within a potentially vast product space. Further, the topography of this space is often nonstationary, due to the interactive dynamics of multiple producers changing their position as they try to learn the distribution of consumer preferences and other features of the problem's economic structure. This presents a producer or its agent with a difficult learning problem: how to locate profitable niches in a very large space. In this paper, we present a model of an information goods duopoly and show that, under complete information, producers would prefer not to compete, instead acting as local monopolists and targeting separate niches in the consumer population. However, when producers have no information about the problem they are solving, it can be quite difficult for them to converge on this solution. We show how a modest amount of economic knowledge about the problem can make it much easier, either by reducing the search space, starting in a useful area of the space, or introducing a gradient. These experiments support the hypothesis that a producer using some knowledge of a problem's (economic) structure can outperform a producer that is performing a naive, knowledge-free form of learning.

Pages: 8 pages
Date: 2004-01
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Published in Peyman Faratin, David C. Parkes, Juan A. Rogríquez- Aguilar & William E. Walsh, eds., Agent-Mediated Electronic Commerce V: Designing Mechanisms and Systems. Springer Verlag, 2004.

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