Rational Inattention to Discrete Choices: A New Foundation for the Multinomial Logit Model
By Filip Matejka, A. McKay. 2015
770 citations recorded by the source corpus.
Subfields: Economics and Econometrics, General Decision Sciences, Safety Research
View Paper: https://www.semanticscholar.org/paper/faaa399a082da2631ccd84ca0275920f9c49577c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- John Leahy
- Andrew Caplin
- Paulo Natenzon
- Mark Dean
- Yeon-Koo Che
- Konrad Mierendorff
- B. Kőszegi
- Anna Lusardi
In General Decision Sciences
- John Leahy
- Andrew Caplin
- Paulo Natenzon
- Mark Dean
- Yeon-Koo Che
- Konrad Mierendorff
- B. Kőszegi
- Matthew Rabin
In Safety Research
- Paulo Natenzon
- Yeon-Koo Che
- Andrew Caplin
- Konrad Mierendorff
- Mark Dean
- B. Kőszegi
- Matthew Rabin
- Juan D. Carrillo
Citations in the corpus
- Random Choice and Learning (2019, background): “Under assumptions i and ii, Matejka and McKay (2015) showed that the optimal information acquisition strategy for the rationally inattentive decision maker generates choice probabilities that follow the multinomial logit model.11 The last column of table 3 (logit) shows the estimated choice…”
- Optimal Dynamic Allocation of Attention (2018, methodology): “Part (b) shows that the cost of mistakes also matters for the relative appeal of the alternative learning strategies: opposite-biased learning becomes more appealing when signals (see Matejka and McKay (2015)).”
- Rational Inattention, Optimal Consideration Sets, and Stochastic Choice (2018, background): “Such actions increase the value to learning, because it means appropriate action can be taken 11A similar example appears in Matejka and McKay [2015].”