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Testing Probabilistic Models of Choice using Column Generation
Computers & Operations Research ( IF 4.6 ) Pub Date : 2018-07-01 , DOI: 10.1016/j.cor.2018.03.001
Bart Smeulders 1 , Clintin Davis-Stober 2 , Michel Regenwetter 3 , Frits C R Spieksma 4
Affiliation  

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.

中文翻译:

使用列生成测试选择的概率模型

在所谓的概率选择的随机偏好模型中,决策者根据偏好状态的未指定概率分布进行选择。最突出的情况出现在偏好状态是选择选项的线性顺序或弱顺序时。文献证明,实际评估决策者观察到的选择是否与这种概率选择模型一致会带来计算困难。这严重限制了行为经济学和相关学科中实证工作的可能规模。我们提出了一系列基于列生成的算法来执行此类测试。我们在各种实例集上评估我们的算法。
更新日期:2018-07-01
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