Ordinal Bayesian incentive compatibility in random assignment model

Sulagna Dasgupta and Debasis Mishra

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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 a 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 priors in some neighborhood of a given independent 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 (2001): if there are at least four agents, there is no locally robust OBIC and ordinally efficient mechanism satisfying equal treatment of equals.

Date: 2020-09, Revised 2021-05
New Economics Papers: this item is included in nep-des and nep-mic
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Citations: View citations in EconPapers (1)

Published in Review of Economic Design, 2022

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Working Paper: Ordinal Bayesian incentive compatibility in random assignment model (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2009.13104

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