Optimal categorization

Erik Mohlin ()

Journal of Economic Theory, 2014, vol. 152, issue C, 356-381

Abstract: This paper studies categorizations that are optimal for the purpose of making predictions. A subject encounters an object (x,y). She observes the first component, x, and has to predict the second component, y. The space of objects is partitioned into categories. The subject determines what category the new object belongs to on the basis of x, and predicts that its y-value will be equal to the average y-value among the past observations in that category. The optimal categorization minimizes the expected prediction error. The main results are driven by a bias-variance trade-off: The optimal size of a category around x, is increasing in the variance of y conditional on x, decreasing in the variance of the conditional mean, decreasing in the size of the data base, and decreasing in the marginal density over x.

Keywords: Categorization; Priors; Coarse reasoning; Similarity-based reasoning; Case-based reasoning; Regression trees (search for similar items in EconPapers)
JEL-codes: C72 D83 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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Working Paper: Optimal Categorization (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:152:y:2014:i:c:p:356-381

DOI: 10.1016/j.jet.2014.03.007

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