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arXiv · 2009.13104

Ordinal Bayesian incentive compatibility in random assignment model

Sulagna Dasgupta·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.

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Sulagna Dasgupta, Debasis Mishra. 2020-09-28. Ordinal Bayesian incentive compatibility in random assignment model. https://doi.org/10.1007/s10058-022-00289-4

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