2018
DOI: 10.1016/j.jet.2018.05.015
|View full text |Cite|
|
Sign up to set email alerts
|

Dual random utility maximisation

Abstract: Dual Random Utility Maximisation (dRUM) is Random Utility Maximisation when utility depends on only two states. dRUM has many relevant behavioural interpretations and practical applications. We show that it is (generically) the only stochastic choice rule that satisfies Regularity and two new simple properties: Constant Expansion (if the choice probability of an alternative is the same across two menus, then it is the same in the combined menu), and Negative Expansion (if the choice probability of an alternati… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
9
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
7

Relationship

1
6

Authors

Journals

citations
Cited by 18 publications
(9 citation statements)
references
References 36 publications
0
9
0
Order By: Relevance
“…As with Manzini and Mariotti [2014], our work provides a link between the study of consideration sets and the recent literature aimed at understanding stochastic choice data (e.g. Agranov and Ortoleva [2015], Manzini and Mariotti [2016], Apesteguia and Ballester [2016]). An earlier literature in marketing discussed models of endogenous consideration set formation (e.g.…”
Section: Introductionmentioning
confidence: 75%
“…As with Manzini and Mariotti [2014], our work provides a link between the study of consideration sets and the recent literature aimed at understanding stochastic choice data (e.g. Agranov and Ortoleva [2015], Manzini and Mariotti [2016], Apesteguia and Ballester [2016]). An earlier literature in marketing discussed models of endogenous consideration set formation (e.g.…”
Section: Introductionmentioning
confidence: 75%
“…64 Lu (forthcoming) studied an analogous model with state-dependent utilities in an objective state-space setting. 65 Other recent contributions by Apesteguia, Ballester, and Lu (2017) and Manzini and Mariotti (2018) respectively studied random utility models with linearly ordered choice options and binary support. 66 On more limited domains, Gul, Natenzon, and Pesendorfer (2014) studied an agent who receives an outcome only once at the end of a decision tree and characterized a generalization of the Luce model.…”
Section: Related Literaturementioning
confidence: 99%
“… Other recent contributions by Apesteguia, Ballester, and Lu () and Manzini and Mariotti () respectively studied random utility models with linearly ordered choice options and binary support. …”
mentioning
confidence: 99%
“…For the claim to hold an additional axiom is required. We correct the mistake in the proof in Manzini and Mariotti [3].…”
mentioning
confidence: 91%
“…
Lemma 2 (and hence the statement of Theorem 1) in Manzini and Mariotti [3] is incorrect as stated. For the claim to hold an additional axiom is required.
…”
mentioning
confidence: 99%