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

在当今联系日益紧密的世界中，特别是在互联网上，人与算法之间的互动会产生重要的社会和经济成果。这种互动涉及大量的信息交换，通常是由自利的各方进行的，个人根据这些信息做出决定并选择自己的行动。一个名为“贝叶斯说服”的新兴研究领域研究此类战略沟通的信息机制的优化设计，也称为信令方案。该项目将通过计算镜头促进这一领域的研究，旨在使当前的风格化模型更接近实践，从而揭示有助于在长期存在的问题上取得进展的新结构。它将结合算法和博弈论工具，实现更好的信息机制设计，从而提高社会福利和经济盈余。由于当今数字经济的主要特征之一是收集信息并在许多自利的各方之间传播信息，因此开发一种现代说服算法理论迫在眉睫。作为该项目的一部分，私人投资促进机构将组织教育活动(教程、讲习班和调查)，将算法说服这一相对较新的研究领域推向研究界，并将研究成果纳入课程，为下一代计算机科学家提供关于信息在复杂环境中的战略作用的推理能力。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识别和扩展由说服在当今数字经济中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。这包括以下内容。(1)超越到目前为止通过算法研究的说服的基本模型，考虑到说服的重要应用中存在的额外结构，例如在线广告拍卖。在克服一般说服模型普遍存在的困难和不可能结果方面，利用结构是至关重要的。(2)超越平坦的说服模式，在网络上进行更现实的沟通。例如，当每个代理既是信息发送者又是信息接收者时，如何在社交网络上传输信息？(3)在现实约束下设计最优或接近最优的说服方案：隐私保护、健壮性和通信限制。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.