Learning to Persuade on the Fly: Robustness Against Ignorance

You Zu (), Krishnamurthy Iyer () and Haifeng Xu ()
Additional contact information
You Zu: Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota 55455
Krishnamurthy Iyer: Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota 55455
Haifeng Xu: Department of Computer Science, University of Chicago, Chicago, Illinois 60637

Operations Research, 2025, vol. 73, issue 1, 194-208

Abstract: Motivated by information sharing in online platforms, we study repeated persuasion between a sender and a stream of receivers, where, at each time, the sender observes a payoff-relevant state drawn independently and identically from an unknown distribution and shares state information with the receivers, who each choose an action. The sender seeks to persuade the receivers into taking actions aligned with the sender’s preference by selectively sharing state information. However, in contrast to the standard models, neither the sender nor the receivers know the distribution, and the sender has to persuade while learning the distribution on the fly. We study the sender’s learning problem of making persuasive action recommendations to achieve low regret against the optimal persuasion mechanism with the knowledge of the distribution. To do this, we first propose and motivate a persuasiveness criterion for the unknown distribution setting that centers robustness as a requirement in the face of uncertainty. Our main result is an algorithm that, with high probability, is robustly persuasive and achieves O ( T log T ) regret, where T is the horizon length. Intuitively, at each time, our algorithm maintains a set of candidate distribution and chooses a signaling mechanism that is simultaneously persuasive for all of them. Core to our proof is a tight analysis about the cost of robust persuasion, which may be of independent interest. We further prove that this regret order is optimal (up to logarithmic terms) by showing that no algorithm can achieve regret better than Ω ( T ) .

Keywords: Market Analytics and Revenue Management; no-regret learning; robustness; persuasion; prior-independence (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2021.0529 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:73:y:2025:i:1:p:194-208

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().