Summary of Bayesian Persuasion with Externalities: Exploiting Agent Types, by Jonathan Shaki et al.
Bayesian Persuasion with Externalities: Exploiting Agent Types
by Jonathan Shaki, Jiarui Gan, Sarit Kraus
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a Bayesian persuasion problem with externalities, where a principal sends signals to inform multiple agents about the state of the world while also considering correlations between agents’ actions due to externalities in their utilities. The model assumes agents are categorized into types with identical utility functions and equitable treatment by both other agents and the principal. The paper analyzes optimal signaling strategies for the principal under three channel types: public, private, and semi-private. Key findings include revelation-principle-style characterizations of optimal strategies, linear programming formulations, and analysis of computational complexity. Notably, when the maximum number of deviating agents is bounded, polynomial-time solvability is achieved through LP-based formulations; otherwise, the problems are NP-hard. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how a person in charge can persuade many people to do something by sending secret messages. This is tricky because what one person does affects others, and everyone’s motivations are different. The researchers looked at three ways the person could send these messages: publicly, privately, or somewhere in between. They found some surprising patterns about what works best under each scenario. For example, if there’s a limit to how many people can be swayed, they can quickly figure out the right approach; otherwise, it becomes much harder. |