Stochastic Choice and Consideration Sets
By Paola Manzini, Marco Mariotti. Social Science Research Network, 2012
324 citations recorded by the source corpus.
We model a boundedly rational agent who suffers from limited attention. The agent considers each feasible alternative with a given (unobservable) probability, the attention parameter, and then chooses the alternative that maximizes a preference relation within the set of considered alternatives. We show that this random choice rule is the only one for which the impact of removing an alternative on the choice probability of any other alternative is asymmetric and menu independent. Both the preference relation and the attention parameters are identified uniquely by stochastic choice data.
Subfields: Economics and Econometrics, General Decision Sciences
View Paper: https://www.semanticscholar.org/paper/654449cecedee7f6a2cc003628ba60c315aa7056
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Citations in the corpus
- Random Choice and Learning (2019, background): “Gul, Natenzon, and Pesendorfer (2014), Manzini and Mariotti (2014), and Lu (2016) characterize behavior nested in the random utility framework.”