Ordinal Bayesian incentive compatibility in random assignment model

Sulagna Dasgupta () and Debasis Mishra ()
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Sulagna Dasgupta: University of Chicago
Debasis Mishra: Indian Statistical Institute, Delhi

Review of Economic Design, 2022, vol. 26, issue 4, No 8, 664 pages

Abstract: Abstract We explore the consequences of weakening the notion of incentive compatibility from strategy-proofness to ordinal Bayesian incentive compatibility (OBIC) in the random assignment model. If the common prior of the agents is the uniform prior, then a large class of random mechanisms are OBIC with respect to this prior—this includes the probabilistic serial mechanism. We then introduce a robust version of OBIC: a mechanism is locally robust OBIC if it is OBIC with respect all independent and identical priors in some neighborhood of a given independent and identical prior. We show that every locally robust OBIC mechanism satisfying a mild property called elementary monotonicity is strategy-proof. This leads to a strengthening of the impossibility result in Bogomolnaia and Moulin (J Econ Theory 100:295–328, 2001): if there are at least four agents, there is no locally robust OBIC and ordinally efficient mechanism satisfying equal treatment of equals.

Keywords: Ordinal Bayesian incentive compatibility; Random assignment; Probabilistic serial mechanism (search for similar items in EconPapers)
JEL-codes: D47 D82 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10058-022-00289-4

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