Categorization

33 Pages Posted: 21 Feb 2006

See all articles by Marcin Pęski

Marcin Pęski

University of Toronto - Department of Economics

Date Written: January 2006

Abstract

We present a model of induction, where the decision maker (DM) infers the unknown properties of an object from information about other objects. We show that categorization is an optimal solution to the prediction problem: The DM classifies objects into groups and her predictions of the object's properties are based on typical properties in the group. The DM prefers to use relatively fewer and larger categories to avoid the problem of overfitting. Since the DM is fully rational, it is an argument that bounded rationality is not necessary for categorization, and categorization does not need to lead to a persistent bias in information processing. In a technical sense, our model studies exchangeability in a multi-dimensional setting. This leads to a generalization of methods related to de Finetti's theorem.

Keywords: Learning, Statistical Decision Theory

Suggested Citation

Pęski, Marcin, Categorization (January 2006). Available at SSRN: https://ssrn.com/abstract=884232 or http://dx.doi.org/10.2139/ssrn.884232

Marcin Pęski (Contact Author)

University of Toronto - Department of Economics ( email )

150 St. George Street
Toronto, Ontario M5S3G7
Canada

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