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Bounded incentives in manipulating the Probabilistic Serial rule

Bounded incentives in manipulating the Probabilistic Serial rule
Bounded incentives in manipulating the Probabilistic Serial rule
The Probabilistic Serial mechanism is well-known for its desirable fairness and efficiency properties. It is one of the most prominent protocols for the random assignment problem. However, Probabilistic Serial is not incentive-compatible, thereby these desirable properties only hold for the agents' declared preferences, rather than their genuine preferences. A substantial utility gain through strategic behaviors would trigger self-interested agents to manipulate the mechanism and would subvert the very foundation of adopting the mechanism in practice. In this paper, we characterize the extent to which an individual agent can increase its utility by strategic manipulation. We show that the incentive ratio of the mechanism is 3/2. That is, no agent can misreport its preferences such that its utility becomes more than 1.5 times of what it is when reports truthfully. This ratio is a worst-case guarantee by allowing an agent to have complete information about other agents' reports and to figure out the best response strategy even if it is computationally intractable in general. To complement this worst-case study, we further evaluate an agent's utility gain on average by experiments. The experiments show that an agent' incentive in manipulating the rule is very limited. These results shed some light on the robustness of Probabilistic Serial against strategic manipulation, which is one step further than knowing that it is not incentive-compatible.
Wang, Zihe
e4c314c2-e0e0-48d7-9859-a0dab56c74b1
Wei, Zhide
3f5e0b63-53b2-4cfd-b200-d4357941ac80
Zhang, Jie
6bad4e75-40e0-4ea3-866d-58c8018b225a
Wang, Zihe
e4c314c2-e0e0-48d7-9859-a0dab56c74b1
Wei, Zhide
3f5e0b63-53b2-4cfd-b200-d4357941ac80
Zhang, Jie
6bad4e75-40e0-4ea3-866d-58c8018b225a

Wang, Zihe, Wei, Zhide and Zhang, Jie (2020) Bounded incentives in manipulating the Probabilistic Serial rule. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020. (doi:10.1609/aaai.v34i02.5605).

Record type: Conference or Workshop Item (Paper)

Abstract

The Probabilistic Serial mechanism is well-known for its desirable fairness and efficiency properties. It is one of the most prominent protocols for the random assignment problem. However, Probabilistic Serial is not incentive-compatible, thereby these desirable properties only hold for the agents' declared preferences, rather than their genuine preferences. A substantial utility gain through strategic behaviors would trigger self-interested agents to manipulate the mechanism and would subvert the very foundation of adopting the mechanism in practice. In this paper, we characterize the extent to which an individual agent can increase its utility by strategic manipulation. We show that the incentive ratio of the mechanism is 3/2. That is, no agent can misreport its preferences such that its utility becomes more than 1.5 times of what it is when reports truthfully. This ratio is a worst-case guarantee by allowing an agent to have complete information about other agents' reports and to figure out the best response strategy even if it is computationally intractable in general. To complement this worst-case study, we further evaluate an agent's utility gain on average by experiments. The experiments show that an agent' incentive in manipulating the rule is very limited. These results shed some light on the robustness of Probabilistic Serial against strategic manipulation, which is one step further than knowing that it is not incentive-compatible.

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Published date: 3 April 2020

Identifiers

Local EPrints ID: 443706
URI: http://eprints.soton.ac.uk/id/eprint/443706
PURE UUID: 52413fce-0975-4a3d-b682-7ca9d2d8d4e8
ORCID for Jie Zhang: ORCID iD orcid.org/0000-0002-5348-7671

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Date deposited: 09 Sep 2020 16:32
Last modified: 10 Sep 2020 01:45

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Contributors

Author: Zihe Wang
Author: Zhide Wei
Author: Jie Zhang ORCID iD

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