Ordinal Bayesian incentive compatibility in random assignment model

Sulagna Dasgupta and Debasis Mishra
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Sulagna Dasgupta: University of Chicago

Discussion Papers from Indian Statistical Institute, Delhi

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 ecient 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)
Pages: 22 pages
Date: 2020-09
New Economics Papers: this item is included in nep-des, nep-mic and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Ordinal Bayesian incentive compatibility in random assignment model (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:alo:isipdp:20-06

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