EDBT 2026 Demo / reviewers in the wild / expert
Peter Klibanoff
dblp:10/6467
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0001-6948-5649ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 since 2021Theory of computation · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% | |
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › mechanism design › information design
bayesian persuasion |
0.8 | 1 | 2024 | Persuasion with Ambiguous Communication · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design
information design |
0.8 | 1 | 2024 | Persuasion with Ambiguous Communication · EC 2024 |
Algorithmic game theory and mechanism design
mechanism design |
0.8 | 1 | 2024 | Persuasion with Ambiguous Communication · EC 2024 |
Algorithmic game theory and mechanism design › imperfect information games
strategic communication |
0.8 | 1 | 2024 | Persuasion with Ambiguous Communication · EC 2024 |
Methods — techniques the papers use, named apart from their topics
decision theory · 1.5bayesian persuasion · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Persuasion with Ambiguous CommunicationabstractThis paper considers the problem of a sender who wishes to favorably influence, through strategic communication of information, the action taken by a receiver. As in the large literature on Bayesian persuasion following Kamenica and Gentzkow [2011] (see also Rayo and Segal [2010] and surveys by Bergemann and Morris [2019] and Kamenica [2019]), we model the sender as committing to a communication strategy and the receiver as best responding to that strategy. A communication strategy for the sender is usually described as a statistical experiment, a function mapping from payoff-relevant states to probability distributions over messages. The key departures from most of the literature and the focus of our analysis are that we enlarge the set of the sender's communication strategies to include ambiguous strategies - strategies for which, from the perspective of both sender and receiver, the probability that a given statistical experiment will be used to generate the signal is subjectively uncertain. Furthermore, the receiver (and possibly the sender as well) is assumed to be ambiguity averse, i.e., averse to this subjective uncertainty about these probabilities. In this paper, we explore whether and to what extent using ambiguous communication can be beneficial for the sender compared to standard, unambiguous communication. Peter Klibanoff, Sujoy Mukerji, Ludovic Renou |
EC | 2 |
| 2011 | Dynamically consistent updating of multiple prior beliefs - An algorithmic approach
Eran Hanany, Peter Klibanoff, Erez Marom |
Int. J. Approx. Reason. | 2 |