Testing probabilistic models of choice using column generation

Bart Smeulders, Clintin Davis-Stober, Michel Regenwetter, Frits C.R. Spieksma

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)32-43
Number of pages12
JournalComputers and Operations Research
StatePublished - Jul 2018


  • Choice behavior
  • Column generation
  • Membership problems
  • Probabilistic choice

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research


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