TY - JOUR
T1 - Deliberately stochastic
AU - Cerreia-Vioglio, Simone
AU - Dillenberger, David
AU - Ortoleva, Pietro
AU - Riella, Gil
N1 - Funding Information:
Jeffrey Ely was the coeditor for this article. We thank Eddie Dekel, Kfir Eliaz, Mira Frick, Faruk Gul, Ryota Iijima, Peter Klibanoff, Jay Lu, Efe Ok, Wolfgang Pesendorfer, Marciano Siniscalchi, and Tomasz Strzalecki for their useful comments. The coeditor and four anonymous referees provided valuable comments that improved the paper significantly. Cerreia-Vioglio gratefully acknowledges the financial support of ERC grant SDDM-TEA, Ortoleva gratefully acknowledges the financial support of NSF grant SES-1763326, and Riella gratefully acknowledges the financial support of CNPq of Brazil, grant 304560/2015-4.
Funding Information:
* Cerreia-Vioglio: Department of Decision Sciences, Università Bocconi, Via Sarfatti, 25 Milano, Italy, and IGIER (email: simone.cerreia@unibocconi.it); Dillenberger: Department of Economics, University of Pennsylvania, Philadelphia, PA 19104 (email: ddill@sas.upenn.edu); Ortoleva: Department of Economics and Woodrow Wilson School, Princeton University, Princeton, NJ 08544 (email: pietro.ortoleva@princeton.edu); Riella: Escola de Políticas Públicas e Governo and Brazilian School of Public and Business Administration Getulio Vargas Foundation, SGAN, Quadra 602, Brasilia/DF/Brazil, 70830-051 (email: gil.riella@fgv.br). Jeffrey Ely was the coeditor for this article. We thank Eddie Dekel, Kfir Eliaz, Mira Frick, Faruk Gul, Ryota Iijima, Peter Klibanoff, Jay Lu, Efe Ok, Wolfgang Pesendorfer, Marciano Siniscalchi, and Tomasz Strzalecki for their useful comments. The coeditor and four anonymous referees provided valuable comments that improved the paper significantly. Cerreia-Vioglio gratefully acknowledges the financial support of ERC grant SDDM-TEA, Ortoleva gratefully acknowledges the financial support of NSF grant SES-1763326, and Riella gratefully acknowledges the financial support of CNPq of Brazil, grant 304560/2015-4. Part of this work was done while Dillenberger was visiting the economics department at Princeton University. He is most grateful to this institution for its hospitality. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.
Publisher Copyright:
© 2019 American Economic Association. All rights reserved.
PY - 2019/7
Y1 - 2019/7
N2 - We study stochastic choice as the outcome of deliberate randomization. We derive a general representation of a stochastic choice function where stochasticity allows the agent to achieve from any set the maximal element according to her underlying preferences over lotteries. We show that in this model stochasticity in choice captures complementarity between elements in the set, and thus necessarily implies violations of Regularity/Monotonicity, one of the most common properties of stochastic choice. This feature separates our approach from other models, e.g., Random Utility.
AB - We study stochastic choice as the outcome of deliberate randomization. We derive a general representation of a stochastic choice function where stochasticity allows the agent to achieve from any set the maximal element according to her underlying preferences over lotteries. We show that in this model stochasticity in choice captures complementarity between elements in the set, and thus necessarily implies violations of Regularity/Monotonicity, one of the most common properties of stochastic choice. This feature separates our approach from other models, e.g., Random Utility.
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U2 - 10.1257/aer.20180688
DO - 10.1257/aer.20180688
M3 - Article
AN - SCOPUS:85068324915
SN - 0002-8282
VL - 109
SP - 2425
EP - 2445
JO - American Economic Review
JF - American Economic Review
IS - 7
ER -