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Related papers: Bayesian Elicitation

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I study a model of information acquisition and transmission in which the sender's ability to misreport her findings is limited. The sender learns covertly, so a key observation is that in equilibrium she must be deterred from undetectably…

Theoretical Economics · Economics 2025-10-28 Matteo Escudé

Bayesian optimization (BO) is an effective approach to optimize expensive black-box functions, that seeks to trade-off between exploitation (selecting parameters where the maximum is likely) and exploration (selecting parameters where we…

Machine Learning · Statistics 2021-10-19 Tristan Fauvel , Matthew Chalk

Overcoming the impact of selfish behavior of rational players in multiagent systems is a fundamental problem in game theory. Without any intervention from a central agent, strategic users take actions in order to maximize their personal…

Computer Science and Game Theory · Computer Science 2024-09-06 Maria-Florina Balcan , Matteo Pozzi , Dravyansh Sharma

How should an agent (the sender) observing multi-dimensional data (the state vector) persuade another agent to take the desired action? We show that it is always optimal for the sender to perform a (non-linear) dimension reduction by…

Machine Learning · Statistics 2022-10-05 Semyon Malamud , Andreas Schrimpf

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classical notion of Value of…

Artificial Intelligence · Computer Science 2013-01-30 Richard Dearden , Nir Friedman , David Andre

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with…

Machine Learning · Statistics 2011-12-30 Neil Houlsby , Ferenc Huszár , Zoubin Ghahramani , Máté Lengyel

Information designers, such as online platforms, often do not know the beliefs of their receivers. We design learning algorithms so that the information designer can learn the receivers' prior belief from their actions through repeated…

Computer Science and Game Theory · Computer Science 2025-10-01 Ce Li , Tao Lin

In the Bayesian persuasion model, a sender can convince a receiver to choose an alternative action to the one originally preferred by the receiver. A crucial assumption in this model is the sender's commitment to a predetermined information…

Computer Science and Game Theory · Computer Science 2024-12-04 Jiahao Zhang , Shuran Zheng , Renato Paes Leme , Zhiwei Steven Wu

In the classical Bayesian persuasion model an informed player and an uninformed one engage in a static interaction. The informed player, the sender, knows the state of nature, while the uninformed one, the receiver, does not. The informed…

Theoretical Economics · Economics 2021-11-30 Ehud Lehrer , Dimitry Shaiderman

In a game of persuasion with evidence, a sender has private information. By presenting evidence on the information, the sender wishes to persuade a receiver to take a single action (e.g., hire a job candidate, or convict a defendant). The…

Computer Science and Game Theory · Computer Science 2024-09-10 Martin Hoefer , Pasin Manurangsi , Alexandros Psomas

Persuasion studies how an informed principal may influence the behavior of agents by the strategic provision of payoff-relevant information. We focus on the fundamental multi-receiver model by Arieli and Babichenko (2019), in which there…

Computer Science and Game Theory · Computer Science 2020-04-01 Matteo Castiglioni , Andrea Celli , Nicola Gatti

We study decision-making with rational inattention in settings where agents have perception constraints. In such settings, inaccurate prior beliefs or models of others may lead to inattention blindness, where an agent is unaware of its…

Computer Science and Game Theory · Computer Science 2025-10-06 Mustafa O. Karabag , Jesse Milzman , Ufuk Topcu

Strategic classification studies the interaction between a classification rule and the strategic agents it governs. Under the assumption that the classifier is known, rational agents respond to it by manipulating their features. However, in…

Machine Learning · Computer Science 2021-06-15 Ganesh Ghalme , Vineet Nair , Itay Eilat , Inbal Talgam-Cohen , Nir Rosenfeld

Individual decision-makers consume information revealed by the previous decision makers, and produce information that may help in future decisions. This phenomenon is common in a wide range of scenarios in the Internet economy, as well as…

Computer Science and Game Theory · Computer Science 2019-05-06 Yishay Mansour , Aleksandrs Slivkins , Vasilis Syrgkanis

Designing cyber defense systems to account for cognitive biases in human decision making has demonstrated significant success in improving performance against human attackers. However, much of the attention in this area has focused on…

Artificial Intelligence · Computer Science 2025-05-16 Tailia Malloy , Cleotilde Gonzalez

We study preference learning through recommendations in multi-agent game settings, where a moderator repeatedly interacts with agents whose utility functions are unknown. In each round, the moderator issues action recommendations and…

Computer Science and Game Theory · Computer Science 2026-03-06 Arwa Alanqary , Zakaria Baba , Manxi Wu , Alexandre M. Bayen

How does information regarding an adversary's intentions affect optimal system design? This paper addresses this question in the context of graphical coordination games where an adversary can indirectly influence the behavior of agents by…

Computer Science and Game Theory · Computer Science 2020-03-18 Brandon C. Collins , Philip N. Brown

Deception is a technique to mislead human or computer systems by manipulating beliefs and information. Successful deception is characterized by the information-asymmetric, dynamic, and strategic behaviors of the deceiver and the deceivee.…

Cryptography and Security · Computer Science 2018-10-02 Tao Zhang , Quanyan zhu

We consider an infinite collection of agents who make decisions, sequentially, about an unknown underlying binary state of the world. Each agent, prior to making a decision, receives an independent private signal whose distribution depends…

Computer Science and Game Theory · Computer Science 2012-09-07 Kimon Drakopoulos , Asuman Ozdaglar , John Tsitsiklis

We study an information design problem with two informed senders and a receiver in which, in contrast to traditional Bayesian persuasion settings, senders do not have commitment power. In our setting, a trusted mediator/platform gathers…

Computer Science and Game Theory · Computer Science 2022-11-30 Itai Arieli , Ivan Geffner , Moshe Tennenholtz