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How do people acquire rich, flexible knowledge about their environment from others despite limited cognitive capacity? Humans are often thought to rely on computationally costly mentalizing, such as inferring others' beliefs. In contrast,…

Artificial Intelligence · Computer Science 2026-05-11 Silja Keßler , Miriam Bautista-Salinero , Claudio Tennie , Charley M. Wu

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

While lobbying has been demonstrated to have an important effect on public opinion and policy making, existing models of opinion formation do not specifically include its effect. In this work we introduce a new model of lobbying-driven…

This paper studies the semi-parametric identification and estimation of a rational inattention model with Bayesian persuasion. The identification requires the observation of a cross-section of market-level outcomes. The empirical content of…

Econometrics · Economics 2020-09-18 Moyu Liao

We explore conclusions a person draws from observing society when he allows for the possibility that individuals' outcomes are affected by group-level discrimination. Injecting a single non-classical assumption, that the agent is…

Theoretical Economics · Economics 2019-09-19 Paul Heidhues , Botond Kőszegi , Philipp Strack

One's ability to learn a generative model of the world without supervision depends on the extent to which one can construct abstract knowledge representations that generalize across experiences. To this end, capturing an accurate…

Machine Learning · Computer Science 2021-10-28 Zahra Sheikhbahaee , Dongshu Luo , Blake VanBerlo , S. Alex Yun , Adam Safron , Jesse Hoey

When does society eventually learn the truth, or take the correct action, via observational learning? In a general model of sequential learning over social networks, we identify a simple condition for learning dubbed excludability.…

Theoretical Economics · Economics 2024-04-05 Navin Kartik , SangMok Lee , Tianhao Liu , Daniel Rappoport

The ideal Bayesian agent reasons from a global probability model, but real agents are restricted to simplified models which they know to be adequate only in restricted circumstances. Very little formal theory has been developed to help…

Artificial Intelligence · Computer Science 2013-03-25 Kathryn Blackmond Laskey

We examine information structure design, also called "persuasion" or "signaling", in the presence of a constraint on the amount of communication. We focus on the fundamental setting of bilateral trade, which in its simplest form involves a…

Computer Science and Game Theory · Computer Science 2020-03-09 Shaddin Dughmi , David Kempe , Ruixin Qiang

Integrating information gained by observing others via Social Bayesian Learning can be beneficial for an agent's performance, but can also enable population wide information cascades that perpetuate false beliefs through the agent…

Multiagent Systems · Computer Science 2014-06-05 Christoph Salge , Daniel Polani

In this second article, we show a simple use of the Ignorance as defined in "Jaynes & Shannon's Constrained Ignorance and Surprise". By giving an example about the journey of a person, we believe to show some simple, obvious but…

Artificial Intelligence · Computer Science 2022-06-14 Cailleteau Thomas

We read newspapers and watch TV every day. There are many issues and many controversies. Since media are free, we can hear arguments from every possible side. How do we decide what is wrong or right? The first condition to accept a message…

Physics and Society · Physics 2014-08-27 Krzysztof Malarz , Krzysztof Kułakowski

People infer rich social information from others' actions. These inferences are often constrained by the physical world: what agents can do, what obstacles permit, and how the physical actions of agents causally change an environment and…

Neurons and Cognition · Quantitative Biology 2026-03-31 Lance Ying , Aydan Y. Huang , Aviv Netanyahu , Andrei Barbu , Boris Katz , Joshua B. Tenenbaum , Tianmin Shu

We introduce a generic, compositional and interpretable class of generative world models that supports open-ended learning agents. This is a sparse class of Bayesian networks capable of approximating a broad range of stochastic processes,…

Artificial Intelligence · Computer Science 2024-10-16 Lancelot Da Costa

We study what changes to an agent's decision problem increase her value for information. We prove that information becomes more valuable if and only if the agent's reduced-form payoff in her belief becomes more convex. When the…

Theoretical Economics · Economics 2026-04-17 Mark Whitmeyer

We study a regularized variant of the Bayesian Persuasion problem, where the receiver's decision process includes a divergence-based penalty that accounts for deviations from perfect rationality. This modification smooths the underlying…

Optimization and Control · Mathematics 2025-10-03 Romain Duboscq , Frédéric de Gournay

We study the voting game where agents' preferences are endogenously decided by the information they receive, and they can collaborate in a group. We show that strategic voting behaviors have a positive impact on leading to the ``correct''…

Computer Science and Game Theory · Computer Science 2023-05-23 Qishen Han , Grant Schoenebeck , Biaoshuai Tao , Lirong Xia

We introduce a class of learning problems where the agent is presented with a series of tasks. Intuitively, if there is relation among those tasks, then the information gained during execution of one task has value for the execution of…

Machine Learning · Computer Science 2012-09-06 Christos Dimitrakakis

Potential buyers of a product or service, before making their decisions, tend to read reviews written by previous consumers. We consider Bayesian consumers with heterogeneous preferences, who sequentially decide whether to buy an item of…

Optimization and Control · Mathematics 2022-02-24 Etienne Boursier , Vianney Perchet , Marco Scarsini

Humans and animals explore their environment and acquire useful skills even in the absence of clear goals, exhibiting intrinsic motivation. The study of intrinsic motivation in artificial agents is concerned with the following question:…

Machine Learning · Computer Science 2021-12-08 Nicholas Rhinehart , Jenny Wang , Glen Berseth , John D. Co-Reyes , Danijar Hafner , Chelsea Finn , Sergey Levine