activity
20152025
most citedBayesian Persuasion with Mediators

9 citations · 16 across the 25 of their papers we have counts for

collaborators
Showing 2023Show all

7 papers · 1 filter

cs.GT2023

The Value of Mediation in Long Cheap Talk

Itai Arieli, Ivan Geffner, Moshe Tennenholtz

In this paper, we study an extension of the classic long cheap talk equilibrium introduced by Aumann and Hart~\citeN{aumann-hart-03}, and ask how much can the players benefit from…

econ.TH2023★ 1 cited

Resilient Information Aggregation

Itai Arieli, Ivan Geffner, Moshe Tennenholtz

In an information aggregation game, a set of senders interact with a receiver through a mediator. Each sender observes the state of the world and communicates a message to the medi…

econ.TH2023

Feasible Conditional Belief Distributions

Itai Arieli, Yakov Babichenko, Fedor Sandomirskiy

Agents receive private signals about an unknown state. The resulting joint belief distributions are complex and lack a simple characterization. Our key insight is that, when condit…

cs.GT2023

Reputation-based Persuasion Platforms

Itai Arieli, Omer Madmon, Moshe Tennenholtz

In this paper, we introduce a two-stage Bayesian persuasion model in which a third-party platform controls the information available to the sender about users' preferences. We aim…

cs.GT2023

A Random Dictator Is All You Need

Itai Arieli, Yakov Babichenko, Inbal Talgam-Cohen +1

We study information aggregation with a decision maker aggregating binary recommendations from symmetric agents. Each agent's recommendation depends on her private information abou…

econ.TH2023

Informationally Robust Cheap-Talk

Itai Arieli, Ronen Gradwohl, Rann Smorodinsky

We study the robustness of cheap-talk equilibria to infinitesimal private information of the receiver in a model with a binary state-space and state-independent sender-preferences.…

econ.TH2023★ 1 cited

The Hazards and Benefits of Condescension in Social Learning

Itai Arieli, Yakov Babichenko, Stephan Müller +2

In a misspecified social learning setting, agents are condescending if they perceive their peers as having private information that is of lower quality than it is in reality. Apply…