Testing probabilistic models of choice using column generation

Bart Smeulders, Clintin Davis-Stober, Michel Regenwetter and Frits Spieksma

No 572504, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

Abstract: In so-called random preference models of probabilistic choice, a decision maker chooses according to an unspecified probability distribution over preference states. The most prominent case arises when preference states are linear orders or weak orders of the choice alternatives. The literature has documented that actually evaluating whether decision makers' observed choices are consistent with such a probabilistic model of choice poses computational difficulties. This severely limits the possible scale of empirical work in behavioral economics and related disciplines. We propose a family of column generation based algorithms for performing such tests. We evaluate our algorithms on various sets of instances. We observe substantial improvements in computation time and conclude that we can efficiently test substantially larger data sets than previously possible.

Date: 2017-02
New Economics Papers: this item is included in nep-dcm and nep-upt
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Published in FEB Research Report KBI_1703

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