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CAREER: Co-Design of Information and Incentives in Societal-Scale Cyber-Physical Systems

职业：社会规模网络物理系统中信息和激励的协同设计

基本信息

批准号：
1844729
负责人：
Lillian Ratliff
金额：
$ 50万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-15 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844729&HistoricalAwards=false
关键词：
CAREER Co Design Information Incentives

项目摘要

The objective of this CAREER project is to develop a theoretical and computational framework for the co-design of information and incentive mechanisms targeted at humans in Societal-Scale Cyber-Physical Systems (SCPS) in order to encourage efficient shared resource consumption while mitigating unintended consequences. The application focus is on intelligent transportation systems, a prototypical SCPS with humans in the loop, rapid technology adoption, and emerging mobility markets. CPS and internet of things (IoT) infrastructure is pervasive in the mobility landscape, allowing operators and intelligently augmented humans to make decisions rapidly as they interact with one another and engage with the system. Market mechanisms that support interactions occur on multiple timescales, are constrained by CPS dynamics, and are exposed to exogenous uncertainties, information asymmetries, and behavioral aspects of human decision-making. Through the design of algorithms with guarantees for learning in and shaping of SCPS, this project will address two fundamental components missing in the state-of-the-art: (i) models that capture the interactions and learning processes of different SCPS stakeholders, and (ii) certifiable algorithms with high-probability guarantees for the co-design of adaptive information and incentive mechanisms that achieve measurable improvement in system-level performance while ensuring individual-level quality of service and avoiding discriminatory policies. The validation approach uses a data-informed experimental platform with simulation and living lab components. The research agenda will aid in revising the design of operational mechanisms for both private and public CPS-enabled mobility platforms to include efficiency and measurable fairness as valued criteria. The proposed agenda includes an integrated research and education plan: (i) course development leveraging the experimental platform; (ii) undergrad research in which students aid in building out the experimental platform, and engage with municipal/industry partners; (iii) development of Girls In Research Labs (GIRLs), a week-long summer program in which middle school girls explore research labs across campus through hands-on projects.Contributions to the science of CPS will be made through the study of intelligent infrastructure, with a special focus on behavior unique to human-in-the-loop CPS, and applications to multi-modal transportation systems. The technical plan is based on fundamental methods in decision sciences (control theory, game theory, behavioral economics, and mechanism design), statistics, and online learning. The research agenda is organized along two key thrusts: (i) algorithms for learning in SCPS and (ii) algorithms for shaping SCPS via incentives and information. The proposed tool set will enable analysis of multi-timescale decision-making of autonomous agents, including humans, coupled with CPS infrastructure in resource constrained environments, and will allow for the certifiable design of algorithms for learning and control (e.g., co-design of slow policy changes and real-time control). The modeling, synthesis, and validation approach will provide a principled, scientific basis for SCPS engineering design and operations, and supports CPS education by providing a platform for future engineers to discover realities associated with real-world implementation (e.g., socio-technical constraints). The unique perspective of co-designing information and incentives will also lead to new tools for modeling risk and uncertainties and thus, expose potentially new approaches to resilience in the engineering of CPS.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.

这一职业项目的目标是开发一个理论和计算框架，共同设计社会规模的网络物理系统(SCPS)中针对人类的信息和激励机制，以鼓励有效地共享资源消耗，同时减少意外后果。应用重点是智能交通系统、环路中人参与的原型SCPS、快速技术采用和新兴的移动市场。CPS和物联网(IoT)基础设施在移动环境中无处不在，使运营商和智能增强的人类能够在彼此交互并与系统互动时快速做出决策。支持互动的市场机制在多个时间尺度上发生，受到CPS动态的约束，并受到外部不确定性、信息不对称和人类决策行为方面的影响。通过设计保证学习和塑造SCP的算法，该项目将解决最新技术中缺失的两个基本组件：(I)捕捉不同SCPS利益相关者交互和学习过程的模型，以及(Ii)具有高概率保证的可证明算法，用于共同设计适应性信息和激励机制，在确保个人级别的服务质量和避免歧视性政策的同时，实现系统级性能的可衡量改进。验证方法使用了一个具有模拟和现场实验组件的数据信息实验平台。研究议程将有助于修改私营和公共CPS支持的移动平台的运营机制设计，将效率和可衡量的公平性作为重要标准。拟议的议程包括一项综合研究和教育计划：(I)利用实验平台的课程开发；(Ii)本科生研究，学生帮助建立实验平台，并与市政/行业合作伙伴参与；(Iii)开发研究实验室中的女孩(Girls In Research Labs)，这是一个为期一周的暑期项目，中学女孩通过动手项目探索校园内的研究实验室。将通过学习智能基础设施，特别关注人在环路中CPS独特的行为，以及在多式联运系统中的应用，为CPS科学做出贡献。技术方案基于决策科学(控制论、博弈论、行为经济学和机制设计)、统计学和在线学习的基本方法。研究议程按照两个关键主线组织：(I)在SCP中学习的算法和(Ii)通过激励和信息塑造SCP的算法。拟议的工具集将能够分析包括人类在内的自治代理在资源受限环境中与CPS基础设施相结合的多时间尺度决策，并将允许用于学习和控制的算法的可认证设计(例如，共同设计缓慢的政策变化和实时控制)。建模、综合和验证方法将为SCPS工程设计和操作提供原则性的科学基础，并通过为未来工程师提供一个平台来发现与现实世界实施相关的现实(例如，社会技术约束)来支持CPS教育。共同设计信息和激励的独特视角也将导致对风险和不确定性进行建模的新工具，从而揭示在CP工程中可能出现的恢复能力的新方法。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。

项目成果

期刊论文数量（21）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Online Learning in Periodic Zero-Sum Games

周期性零和博弈中的在线学习

DOI：
发表时间：
2021
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Fiez, Tanner;Sim, Ryann;Skoulaskis, Stratis;Piliouras, Georgios;Ratliff, Lillian J.
通讯作者：
Ratliff, Lillian J.

A SUPER* Algorithm to Optimize Paper Bidding in Peer Review

DOI：
发表时间：
2020-06
期刊：
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
影响因子：
0
作者：
Tanner Fiez;Nihar B. Shah;L. Ratliff
通讯作者：
Tanner Fiez;Nihar B. Shah;L. Ratliff

Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation