Deliberately Stochastic∗ Simone Cerreia-Vioglio,y David Dillenberger,z Pietro Ortoleva,x Gil Riella{ First version: February 2012 This version: May 5, 2018 Abstract 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 underlying preferences over lotteries. 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). We also characterize the case where preferences follow Cautious Expected Utility. Here, stochastic choice is linked to Certainty Bias. JEL: D80, D81 Keywords: Stochastic Choice, Random Utility, Hedging, Cautious Expected Utility, Convex Prefer- ences, Regularity, Certainty Bias. ∗We thank Eddie Dekel, Kfir Eliaz, Mira Frick, Faruk Gul, Ryota Iijima, Peter Klibanoff, Jay Lu, Efe Ok, Wolfgang Pesendorfer, Marciano Siniscalchi, Tomasz Strzalecki, and the audience at various seminars and conferences for their useful comments. Cerreia-Vioglio gratefully acknowledges the financial support of ERC grant SDDM-TEA, Ortoleva gratefully acknowledges the financial support of NSF grant SES-1559462, and Riella gratefully acknowledges the financial support of CNPq of Brazil, grant no. 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. yDepartment of Decision Sciences, Universit`aBocconi, email:
[email protected]. zDepartment of Economics, University of Pennsylvania, email:
[email protected].