Summary of Multi-sender Persuasion: a Computational Perspective, by Safwan Hossain et al.
Multi-Sender Persuasion: A Computational Perspective
by Safwan Hossain, Tonghan Wang, Tao Lin, Yiling Chen, David C. Parkes, Haifeng Xu
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the “multi-sender persuasion problem,” where multiple players with informational advantages try to convince a single self-interested actor to take certain actions. This generalizes the Bayesian Persuasion framework, which is crucial in computational economics, multi-agent learning, and multi-objective machine learning. The core concept is finding Nash equilibria of senders’ signaling policies. Theoretically, it’s proven that finding an equilibrium is PPAD-Hard and even computing a sender’s best response is NP-Hard. To overcome these difficulties, the paper proposes a novel differentiable neural network to approximate the game’s non-linear utilities. This is complemented by the extra-gradient algorithm, which discovers local equilibria that Pareto dominate full-revelation equilibria and those found by existing neural networks. The paper’s theoretical and empirical contributions have implications for a wide range of economic problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how people with more information try to convince someone else to do something. This is important in economics, learning, and making decisions. The main idea is to find a balance where everyone is happy with the outcome. It’s hard to do this exactly, but it’s possible to get close. The authors use a special kind of computer program called a neural network to help figure out what will happen. They also found a way to make the program work better by adding some extra steps. This research can be used to understand how people make decisions and how we can make better choices. |
Keywords
» Artificial intelligence » Machine learning » Neural network
