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Random Choice as Behavioral Optimization †

By Faruk Gul, Paulo Natenzon and Wolfgang Pesendorfer


We study random choice to capture violations of the weak axiom of revealed preference. Using comparisons of choice probabilities, we introduce the notion of a stochastic preference. We show that the Luce model is the unique rule that has a context-independent stochastic preference. To address well-known difficulties of the Luce model in situations where choice objects have overlapping attributes, we introduce a new random choice model, the weighted attributes rule. We show that it is identified by a context-independent stochastic preference over attributes

Year: 2010
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