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Imitative follower deception in Stackelberg games

Imitative follower deception in Stackelberg games
Imitative follower deception in Stackelberg games

Information uncertainty is one of the major challenges facing applications of game theory. In the context of Stackelberg games, various approaches have been proposed to deal with the leader's incomplete knowledge about the follower's payoffs, typically by gathering information from the leader's interaction with the follower. Unfortunately, these approaches rely crucially on the assumption that the follower will not strategically exploit this information asymmetry, i.e., the follower behaves truthfully during the interaction according to their actual payoffs. As we show in this paper, the follower may have strong incentives to deceitfully imitate the behavior of a different follower type and, in doing this, benefit significantly from inducing the leader into choosing a highly suboptimal strategy. This raises a fundamental question: how to design a leader strategy in the presence of a deceitful follower? To answer this question, we put forward a basic model of Stackelberg games with (imitative) follower deception and show that the leader is indeed able to reduce the loss due to follower deception with carefully designed policies. We then provide a systematic study of the problem of computing the optimal leader policy and draw a relatively complete picture of the complexity landscape; essentially matching positive and negative complexity results are provided for natural variants of the model. Our intractability results are in sharp contrast to the situation with no deception, where the leader's optimal strategy can be computed in polynomial time, and thus illustrate the intrinsic difficulty of handling follower deception. Through simulations we also examine the benefit of considering follower deception in randomly generated games.

Equilibrium computation, Imitative follower deception, Learning to commit, Stackelberg game
639-657
ACM
Gan, Jiarui
eaa9f4a0-ced7-48e9-b03e-ee10f21b76dc
Xu, Haifeng
f82aa998-282f-4d50-be6d-8edf15a1f0a9
Guo, Qingyu
9922ab2c-9e8f-484f-ae29-0455d5edc6b3
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Rabinovich, Zinovi
d1b689c6-504b-4e12-a077-6337e84796b0
Wooldridge, Michael
94674704-0392-4b93-83db-18198c2cfa3b
Gan, Jiarui
eaa9f4a0-ced7-48e9-b03e-ee10f21b76dc
Xu, Haifeng
f82aa998-282f-4d50-be6d-8edf15a1f0a9
Guo, Qingyu
9922ab2c-9e8f-484f-ae29-0455d5edc6b3
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Rabinovich, Zinovi
d1b689c6-504b-4e12-a077-6337e84796b0
Wooldridge, Michael
94674704-0392-4b93-83db-18198c2cfa3b

Gan, Jiarui, Xu, Haifeng, Guo, Qingyu, Tran-Thanh, Long, Rabinovich, Zinovi and Wooldridge, Michael (2019) Imitative follower deception in Stackelberg games. In ACM EC 2019 - Proceedings of the 2019 ACM Conference on Economics and Computation. ACM. pp. 639-657 . (doi:10.1145/3328526.3329629).

Record type: Conference or Workshop Item (Paper)

Abstract

Information uncertainty is one of the major challenges facing applications of game theory. In the context of Stackelberg games, various approaches have been proposed to deal with the leader's incomplete knowledge about the follower's payoffs, typically by gathering information from the leader's interaction with the follower. Unfortunately, these approaches rely crucially on the assumption that the follower will not strategically exploit this information asymmetry, i.e., the follower behaves truthfully during the interaction according to their actual payoffs. As we show in this paper, the follower may have strong incentives to deceitfully imitate the behavior of a different follower type and, in doing this, benefit significantly from inducing the leader into choosing a highly suboptimal strategy. This raises a fundamental question: how to design a leader strategy in the presence of a deceitful follower? To answer this question, we put forward a basic model of Stackelberg games with (imitative) follower deception and show that the leader is indeed able to reduce the loss due to follower deception with carefully designed policies. We then provide a systematic study of the problem of computing the optimal leader policy and draw a relatively complete picture of the complexity landscape; essentially matching positive and negative complexity results are provided for natural variants of the model. Our intractability results are in sharp contrast to the situation with no deception, where the leader's optimal strategy can be computed in polynomial time, and thus illustrate the intrinsic difficulty of handling follower deception. Through simulations we also examine the benefit of considering follower deception in randomly generated games.

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More information

Published date: 17 June 2019
Venue - Dates: 20th ACM Conference on Economics and Computation, EC 2019, Phoenix, United States, 2019-06-24 - 2019-06-28
Keywords: Equilibrium computation, Imitative follower deception, Learning to commit, Stackelberg game

Identifiers

Local EPrints ID: 432835
URI: https://eprints.soton.ac.uk/id/eprint/432835
PURE UUID: 2c21fd0b-587d-48bc-9054-0dc74b3b9636
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316

Catalogue record

Date deposited: 29 Jul 2019 16:30
Last modified: 30 Nov 2019 01:34

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Contributors

Author: Jiarui Gan
Author: Haifeng Xu
Author: Qingyu Guo
Author: Long Tran-Thanh ORCID iD
Author: Zinovi Rabinovich
Author: Michael Wooldridge

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