Sequential Decision Making With Information Asymmetry (Invited Talk)

Authors Jiarui Gan , Rupak Majumdar , Goran Radanovic , Adish Singla



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Author Details

Jiarui Gan
  • University of Oxford, UK
Rupak Majumdar
  • Max Planck Institute for Software Systems (MPI-SWS), Kaiserslautern, Germany
Goran Radanovic
  • Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken, Germany
Adish Singla
  • Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken, Germany

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Jiarui Gan, Rupak Majumdar, Goran Radanovic, and Adish Singla. Sequential Decision Making With Information Asymmetry (Invited Talk). In 33rd International Conference on Concurrency Theory (CONCUR 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 243, pp. 4:1-4:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.CONCUR.2022.4

Abstract

We survey some recent results in sequential decision making under uncertainty, where there is an information asymmetry among the decision-makers. We consider two versions of the problem: persuasion and mechanism design. In persuasion, a more-informed principal influences the actions of a less-informed agent by signaling information. In mechanism design, a less-informed principal incentivizes a more-informed agent to reveal information by committing to a mechanism, so that the principal can make more informed decisions. We define Markov persuasion processes and Markov mechanism processes that model persuasion and mechanism design into dynamic models. Then we survey results on optimal persuasion and optimal mechanism design on myopic and far-sighted agents. These problems are solvable in polynomial time for myopic agents but hard for far-sighted agents.

Subject Classification

ACM Subject Classification
  • Theory of computation → Models of computation
Keywords
  • Bayesian persuasion
  • Automated mechanism design
  • Markov persuasion processes
  • Markov mechanism processes
  • Myopic agents

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