NSF-BSF: AF: Small: Algorithmic Persuasion: Re-creating the Success of Mechanism Design

NSF-BSF：AF：小：算法说服：重新创造机制设计的成功

• 批准号: 2303372
• 负责人: Haifeng Xu
• 金额: $ 45.34万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303372&HistoricalAwards=false
• 关键词: NSF; BSF; AF; Small; Algorithmic

In today’s increasingly connected world, particularly on the Internet, interactions among people and algorithms lead to important social and economic outcomes. Such interactions involve massive exchange of information, often by self-interested parties, on the basis of which individuals make decisions and choose their actions. An emerging research area termed "Bayesian persuasion" studies the optimal design of information mechanisms for such strategic communications, also known as signaling schemes. This project will promote this area of research through the computational lens, and aims at bringing current stylized models closer to practice and thus uncovering new structure that will help make progress on longstanding problems. It will combine algorithmic and game-theoretic tools to achieve better designs of information mechanisms, towards enhanced social welfare and economic surplus. Since one of the main characteristics of today’s digital economy is the collection of information and its dissemination among many self-interested parties, developing a modern algorithmic theory of persuasion is of imminent importance. As part of this project, the PIs will organize education activities (tutorials, workshops and surveys) to propel forward the relatively nascent research area of algorithmic persuasion to the research community, and will integrate research findings into courses to provide the next generation of computer scientists the ability of reasoning about the strategic role of information in complex environments. Like mechanism design, persuasion is inherently an optimization task. On a technical level, the main focus of this project is to identify and expand multiple new research frontiers driven by key applications of persuasion in today’s digital economy, with the ultimate goal of obtaining a mature algorithmic theory of persuasion. This includes the following. (1) Going beyond the basic models of persuasion studied algorithmically thus far, by taking into account additional structure present in important applications of persuasion, e.g., online advertising auctions. Utilizing structure is crucial in overcoming the hardness and impossibility results with which the general persuasion models are so rife. (2) Going beyond a flat model of persuasion to more realistic communication on networks. For example, how would information transmit over a social network when each agent is both an information sender and receiver? (3) Designing optimal or approximately-optimal persuasion schemes under realistic constraints: privacy-preservation, robustness, and communication restrictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在当今日益互联的世界中，特别是在互联网上，人与算法之间的交互导致了重要的社会和经济成果，这种交互涉及大量的信息交换，通常是由利己方进行的，个人在此基础上做出决策和选择。一个名为“贝叶斯说服”的新兴研究领域研究了此类战略通信的信息机制的优化设计，也称为信号方案，该项目将通过计算镜头推动这一领域的研究，旨在引入当前的程式化。模型更接近实践，从而揭示新的结构将有助于在长期存在的问题上取得进展，它将结合算法和博弈论工具来实现更好的信息机制设计，从而提高社会福利和经济盈余，因为当今数字经济的主要特征之一是集合。作为该项目的一部分，PI 将组织教育活动（教程、研讨会和调查）以推动相对新兴的研究。说服研究领域的算法，并将研究成果融入课程中，为下一代计算机科学家提供推理信息在复杂环境中的战略作用的能力，就像机制设计一样，说服本质上是一项优化任务。在技​​术层面上，该项目的主要重点是确定和扩展当今数字经济中说服的关键应用驱动的多个新的研究前沿，最终目标是获得成熟的说服算法理论。 (1) 超越迄今为止在算法上研究的说服的基本模型，考虑说服的重要应用中存在的附加结构，例如在线广告拍卖，利用结构对于克服一般说服的困难和不可能结果至关重要。 (2) 超越平面说服模型，转向更现实的网络通信。例如，当每个代理既是信息发送者又是信息发送者时，信息将如何通过社交网络传输。 (3) 设计最佳或近似最佳的说服方案反映了现实的限制：隐私保护、稳健性和通信限制。该奖项是 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持。审查标准。

项目成果

Algorithmic Information Design in Multi-Player Games: Possibilities and Limits in Singleton Congestion

多人游戏中的算法信息设计：单例拥塞的可能性和限制

• DOI: 10.1145/3490486.3538238
• 发表时间: 2022-07
• 期刊: Proc. 23th ACM Conference on Economics and Computation
• 作者: Zhou, Chenghan;Nguyen, Thanh H.;Xu, Haifeng
• 通讯作者: Xu, Haifeng

Regret-minimizing Bayesian persuasion

遗憾最小化贝叶斯说服

• DOI: 10.1016/j.geb.2022.09.001
• 发表时间: 2022-11
• 期刊: Games and Economic Behavior
• 影响因子: 1.1
• 作者: Babichenko, Yakov;Talgam;Xu, Haifeng;Zabarnyi, Konstantin
• 通讯作者: Zabarnyi, Konstantin

The Strange Role of Information Asymmetry in Auctions—Does More Accurate Value Estimation Benefit a Bidder?

信息不对称在拍卖中的奇怪作用——更准确的价值估算对投标人有利吗？

• DOI: 10.1609/aaai.v36i5.20459
• 发表时间: 2022-06
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Xu, Haifeng;Cavallo, Ruggiero
• 通讯作者: Cavallo, Ruggiero

Robust Stackelberg Equilibria

鲁棒Stackelberg均衡

• DOI: 10.1145/3580507.3597680
• 发表时间: 2023-04-28
• 期刊: Proceedings of the 24th ACM Conference on Economics and Computation
• 作者: Jiarui Gan;Minbiao Han;Jibang Wu;Haifeng Xu
• 通讯作者: Haifeng Xu

Multi-Channel Bayesian Persuasion

多渠道贝叶斯说服

• 发表时间: 2022-11
• 期刊: Proceedings of the 13th Innovations in Theoretical Computer Science Conference (ITCS 2022
• 作者: Babichenko, Yakov;Talgam;Xu, Haifeng;Zabarnyi, Konstantin
• 通讯作者: Zabarnyi, Konstantin