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Collaborative Research: Distributed Mechanism Design with Learning Guarantees: Resource Allocation Among Networked Strategic Agents

协作研究：具有学习保证的分布式机制设计：网络化战略代理之间的资源分配

基本信息

批准号：
2014816
负责人：
Jianghai Hu
金额：
$ 22.46万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014816&HistoricalAwards=false
关键词：
Collaborative Research Distributed Mechanism Design

项目摘要

During the last decades there has been significant research in understanding how strategic agents in communication, transportation, energy, economic, societal networks make decisions in the presence of uncertainty about other agents' preferences. This research is motivated by a multitude of applications in our ever-connected society and economy, and the realization that assumptions such as fully-informed or fully-compliant agents are untenable in vastly decentralized networks. The design of incentives for resource allocation problems in the presence of strategic agents falls under the research area of mechanism design (MD). There are two issues with the current state of the art in MD. First, it assumes the existence of a central entity accepting bids and being capable of communicating with each agent. Second, it does not adequately address the question of how agents converge to the designed equilibria. Intellectual Merit: Our overarching objective in this proposal is to create a new subfield of research that addresses these issues in a unified framework. We utilize this framework to design mechanisms that are distributed and have learning (i.e., convergence) guarantees for a sufficiently broad range of agents' behaviors. To achieve our objective, we plan to proceed along a path that blends in fundamental research with targeted applications. In particular, we first investigate the design of distributed mechanisms. We consider two illustrative applications, namely, rate allocation in unicast/multicast-multirate networks and demand management of energy communities. We then investigate distributed mechanisms that incorporate learning guarantees, by which the community reaches an equilibrium. Finally, we study the above two problems in the context of non-Bayesian agents with ``no-regret'' type limited rationality.Broader Impacts: Besides the two illustrative applications, research to be carried in this project will benefit a broad range of practical fields of large societal impact. Examples include smart energy and infrastructure systems, communication systems, cyber physical and human systems, social and economical systems, to name a few. The research outcomes will be utilized for curriculum development, for training undergraduate and graduate students, and for various outreach activities at the PIs’ institutions.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.

在过去的几十年里，有大量的研究来理解在通信、交通、能源、经济、社会网络中的战略代理人如何在其他代理人偏好不确定的情况下做出决策。这项研究的动机是在我们不断连接的社会和经济中的大量应用，以及认识到诸如完全知情或完全顺从的代理人等假设在非常分散的网络中是站不住脚的。存在战略代理人的资源配置问题的激励设计福尔斯机制设计（MD）的研究领域。MD的当前技术水平存在两个问题。首先，它假设存在一个接受投标并能够与每个代理人通信的中央实体。其次，它没有充分解决代理人如何收敛到设计的均衡的问题。智力优势：我们在这项提案中的总体目标是创建一个新的研究子领域，在一个统一的框架中解决这些问题。我们利用这个框架来设计分布式和学习的机制（即，收敛）保证了足够广泛的代理行为。为了实现我们的目标，我们计划沿着一条将基础研究与目标应用相结合的道路前进。特别是，我们首先研究分布式机制的设计。我们考虑两个说明性的应用程序，即，在单播/组播多速率网络的速率分配和能源社区的需求管理。然后，我们调查分布式机制，将学习保证，社区达到一个平衡。最后，我们研究了上述两个问题的背景下，非贝叶斯代理与“无遗憾”型有限rationality.Broader影响：除了这两个说明性的应用，在这个项目中进行的研究将有利于广泛的实际领域的大社会影响。示例包括智能能源和基础设施系统、通信系统、网络物理和人类系统、社会和经济系统等。研究成果将用于课程开发、本科生和研究生的培训以及PI机构的各种外展活动。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。