Signaling-based Robust Incentive Designs with Randomized Monitoring

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Incentive design problems entail hierarchical decision-making where a leader crafts a strategy to induce a desired response from a follower. Such dynamic games with decentralized information structures have been well-studied under three assumptions-the leader must have access to the follower's observations, actions, and the objective function. Lack of knowledge on any of these can potentially lead to performance loss for the leader. In this paper, we first study a setup where the leader observes the follower's action through a random monitoring channel and learns about the follower's observation through a follower-designed signal. In this setup, we establish the existence of a signaling-based incentive equilibrium strategy for the leader that induces honest reporting and desired control response from the follower. Then, we study a setting, where the follower's costs are parametric, but the parameters are not known to the leader. We construct an incentive strategy that reduces the sensitivity of the leader's performance to uncertainty in the parameter, close to an initial estimate. More generally, for the case when the leader's knowledge about the follower's cost and distributions of cost-relevant random variables is inaccurate, we establish the existence of a robust incentive equilibrium strategy that bounds the performance loss from the inaccuracy in the model.

Original languageEnglish (US)
Title of host publicationIFAC-PapersOnLine
EditorsHideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
PublisherElsevier B.V.
Number of pages6
ISBN (Electronic)9781713872344
StatePublished - Jul 1 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: Jul 9 2023Jul 14 2023

Publication series

ISSN (Electronic)2405-8963


Conference22nd IFAC World Congress


  • Differential and dynamic games
  • data-driven decision making
  • hierarchical multilevel
  • multi-agent systems
  • multilayer control
  • robustness analysis

ASJC Scopus subject areas

  • Control and Systems Engineering


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