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

在当今日益互联的世界中，特别是在互联网上，人与人之间的互动和算法会带来重要的社会和经济成果。这种互动涉及到大规模的信息交换，通常是由自利的各方进行的，个人在此基础上做出决定并选择他们的行动。一个新兴的研究领域称为“贝叶斯说服”研究的信息机制，这种战略沟通，也被称为信号方案的最佳设计。该项目将通过计算透镜促进这一领域的研究，旨在使目前的程式化模型更接近实践，从而揭示新的结构，这将有助于在长期存在的问题上取得进展。它将结合联合收割机和博弈论工具，以实现更好的信息机制设计，以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信息的收集及其在许多自利方之间的传播，因此开发现代说服算法理论迫在眉睫。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），以推动相对新生的算法说服研究领域向研究界发展，并将研究成果整合到课程中，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。 与机制设计一样，说服本质上也是一项优化任务。在技术层面上，该项目的主要重点是识别和扩展当今数字经济中由说服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)超越迄今为止在算法上研究的说服的基本模型，通过考虑说服的重要应用中存在的额外结构，例如，在线广告拍卖。利用结构是克服困难和不可能的结果，与一般的说服模型是如此普遍的关键。(2)超越平面的说服模式，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，信息将如何在社交网络上传输？(3)在现实约束条件下设计最佳或近似最佳的说服方案：隐私保护，鲁棒性和通信限制。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.