no code implementations • 30 Jan 2024 • Itai Arieli, Yakov Babichenko, Omer Madmon, Moshe Tennenholtz
We consider a model of third-degree price discrimination, in which the seller has a valuation for the product which is unknown to the market designer, who aims to maximize the buyers' surplus by revealing information regarding the buyer's valuation to the seller.
no code implementations • 15 Jul 2023 • Itai Arieli, Yakov Babichenko, Fedor Sandomirskiy
We consider a model of Bayesian persuasion with one informed sender and several uninformed receivers.
no code implementations • 26 Jan 2023 • Itai Arieli, Yakov Babichenko, Stephan Müller, Farzad Pourbabaee, Omer Tamuz
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.
no code implementations • 8 Mar 2022 • Itai Arieli, Yakov Babichenko, Fedor Sandomirskiy
For one mediator, the characterization has a geometric meaning of constrained concavification of sender's utility, optimal persuasion requires the same number of signals as without mediators, and the presence of the mediator is never profitable for the sender.
no code implementations • 8 Jan 2021 • Itai Arieli, Yakov Babichenko, Manuel Mueller-Frank
We analyze boundedly rational updating from aggregate statistics in a model with binary actions and binary states.
no code implementations • 26 Feb 2020 • Itai Arieli, Yakov Babichenko, Fedor Sandomirskiy, Omer Tamuz
We study the set of possible joint posterior belief distributions of a group of agents who share a common prior regarding a binary state, and who observe some information structure.
no code implementations • 20 Feb 2018 • Yakov Babichenko, Dan Garber
We focus on the question whether the aggregator can learn to aggregate optimally the forecasts of the experts, where the optimal aggregation is the Bayesian aggregation that takes into account all the information (evidence) in the system.
