mlsanityData to 7 Oct 2026

Commitment with Signaling under Double-sided Information Asymmetry

arXiv 2212.11446 ↗First posted 22 Dec 2022cs.GT cs.LGcited by 1

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Abstract (arXiv)

Information asymmetry in games enables players with the information advantage to manipulate others' beliefs by strategically revealing information to other players. This work considers a double-sided information asymmetry in a Bayesian Stackelberg game, where the leader's realized action, sampled from the mixed strategy commitment, is hidden from the follower. In contrast, the follower holds private information about his payoff. Given asymmetric information on both sides, an important question arises: Does the leader's information advantage outweigh the follower's? We answer this question affirmatively in this work, where we demonstrate that by adequately designing a signaling device that reveals partial information regarding the leader's realized action to the follower, the leader can achieve a higher expected utility than that without signaling. Moreover, unlike previous works on the Bayesian Stackelberg game where mathematical programming tools are utilized, we interpret the leader's commitment as a probability measure over the belief space. Such a probabilistic language greatly simplifies the analysis and allows an indirect signaling scheme, leading to a geometric characterization of the equilibrium under the proposed game model.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 33 references that are in the map. First posted on arXiv 22 Dec 2022.

Builds on

9 of 33 references.

References · 9

  1. 2021The Confluence of Networks, Games and Learningcited by 26
  2. 2013Approachability, Regret and Calibration; implications and equivalencescited by 18
  3. 2020Blackwell Online Learning for Markov Decision Processescited by 12
  4. 2020Optimally Deceiving a Learning Leader in Stackelberg Gamescited by 11
  5. 2017On Stackelberg Mixed Strategiescited by 10
  6. 2019Imitative Follower Deception in Stackelberg Gamescited by 9
  7. 2019Manipulating a Learning Defender and Ways to Counteractcited by 8
  8. 2022On the Resilience of Traffic Networks under Non-Equilibrium Learningcited by 6
  9. 2020Distributed Machine Learning with Strategic Network Design: A Game-Theoretic Perspectivecited by 1

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