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Algorithmic Persuasion with Evidence

Authors Martin Hoefer , Pasin Manurangsi , Alexandros Psomas

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Author Details

Martin Hoefer
  • Goethe Universitä Frankfurt, Germany
Pasin Manurangsi
  • Google Research, Mountain View, CA, USA
Alexandros Psomas
  • Purdue University, West Lafayette, IN, USA

Funding

  • Hoefer, Martin: GIF grant I-1419-118.4/2017 and DFG grants Ho 3831/5-1, 6-1, and 7-1.

Acknowledgements

This work was done in part while Martin Hoefer and Alexandros Psomas were visiting the Simons Institute for the Theory of Computing. The authors acknowledge financial support and the invitation by the organizers to join the stimulating work environment. This work was done in part while Alexandros Psomas was visiting Google Research, Mountain View, USA.

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Martin Hoefer, Pasin Manurangsi, and Alexandros Psomas. Algorithmic Persuasion with Evidence. In 12th Innovations in Theoretical Computer Science Conference (ITCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 185, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.ITCS.2021.3

Abstract

We consider a game of persuasion with evidence between a sender and a receiver. The sender has private information. By presenting evidence on the information, the sender wishes to persuade the receiver to take a single action (e.g., hire a job candidate, or convict a defendant). The sender’s utility depends solely on whether or not the receiver takes the action. The receiver’s utility depends on both the action as well as the sender’s private information. We study three natural variations. First, we consider sequential equilibria of the game without commitment power. Second, we consider a persuasion variant, where the sender commits to a signaling scheme and then the receiver, after seeing the evidence, takes the action or not. Third, we study a delegation variant, where the receiver first commits to taking the action if being presented certain evidence, and then the sender presents evidence to maximize the probability the action is taken. We study these variants through the computational lens, and give hardness results, optimal approximation algorithms, as well as polynomial-time algorithms for special cases. Among our results is an approximation algorithm that rounds a semidefinite program that might be of independent interest, since, to the best of our knowledge, it is the first such approximation algorithm for a natural problem in algorithmic economics.

Subject Classification

ACM Subject Classification
  • Theory of computation → Algorithmic game theory and mechanism design
  • Theory of computation → Approximation algorithms analysis
Keywords
  • Bayesian Persuasion
  • Semidefinite Programming
  • Approximation Algorithms

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