European Business Schools Librarian's Group

SSE/EFI Working Paper Series in Economics and Finance,
Stockholm School of Economics

No 721: Optimal Categorization

Erik Mohlin ()
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Erik Mohlin: Dept. of Economics, Stockholm School of Economics, Postal: Stockholm School of Economics, P.O. Box 6501, SE-113 83 Stockholm, Sweden

Abstract: The importance of categorical reasoning in human cognition is well-established in psychology and cognitive science, and it is generally acknowledged that one of the most important functions of categorization is to facilitate prediction. This paper provides a model of optimal categorization. In the beginning of each period a subject observes a two-dimensional object in one dimension and wants to predict the object's value in the other dimension. The subject partitions the space of objects into categories. She has a data base of objects that were observed in both dimensions in the past. The subject determines what category the new object belongs to on the basis of observation of its first dimension. The average value in the second dimension, of objects in this category in the data base, is used as prediction for the object at hand. At the end of each period the second dimension is observed and the observation is stored in the data base. The main result is that the optimal number of categories is determined by a trade-off between (a) decreasing the size of categories in order to enhance category homogeneity, and (b) increasing the size of categories in order to enhance category sample size.

Keywords: Categorization; Priors; Prediction; Similarity-Based Reasoning.

JEL-codes: C72

41 pages, First version: May 31, 2009. Revised: May 30, 2014. Earlier revisions: July 8, 2009, July 7, 2009, July 7, 2009, June 9, 2011, December 12, 2011, February 25, 2013, March 4, 2013, March 6, 2013, May 7, 2013, September 4, 2013, March 13, 2014, March 27, 2014, April 3, 2014.

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