TY - JOUR
T1 - Testing probabilistic models of choice using column generation
AU - Smeulders, Bart
AU - Davis-Stober, Clintin
AU - Regenwetter, Michel
AU - Spieksma, Frits C.R.
N1 - This paper is based on a PhD thesis chapter of the first author. We thank the referees and the doctoral committee, in particular Prof. Yves Crama, for helpful comments and discussions. This research was supported by the Interuniversity Attraction Poles Programme initiated by the Belgian Science Policy Office , by National Science Foundation grants SES-14-59866 (PI: Davis-Stober) & SES-14-59699 (PI: Regenwetter), by National Institutes of Health grant K25AA024182 (PI: Davis-Stober) and by the partnership between KU Leuven and the University of Illinois . Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the funding agencies or of the authors’ universities.
PY - 2018/7
Y1 - 2018/7
N2 - 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.
AB - 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.
KW - Choice behavior
KW - Column generation
KW - Membership problems
KW - Probabilistic choice
UR - http://www.scopus.com/inward/record.url?scp=85044133705&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044133705&partnerID=8YFLogxK
U2 - 10.1016/j.cor.2018.03.001
DO - 10.1016/j.cor.2018.03.001
M3 - Article
C2 - 30416247
AN - SCOPUS:85044133705
SN - 0305-0548
VL - 95
SP - 32
EP - 43
JO - Computers and Operations Research
JF - Computers and Operations Research
ER -