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

CAREER项目的目标是为社会规模的信息物理系统（SCPS）中针对人类的信息和激励机制的协同设计开发一个理论和计算框架，以鼓励有效的共享资源消耗，同时减轻意想不到的后果。应用的重点是智能交通系统，一个有人类参与的SCPS原型，快速的技术采用，以及新兴的移动市场。CPS和物联网（IoT）基础设施在移动领域无处不在，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。支持相互作用的市场机制发生在多个时间尺度上，受CPS动力学的约束，并暴露于外生不确定性、信息不对称和人类决策的行为方面。通过算法的设计，保证SCPS的学习和形成，该项目将解决最先进的两个基本组成部分：(i)捕获不同SCPS利益相关者的交互和学习过程的模型；（ii）具有高概率保证的可认证算法，用于自适应信息和激励机制的协同设计，在确保个人层面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。验证方法使用具有模拟和生活实验室组件的数据知情实验平台。研究议程将有助于修改私人和公共cps移动平台的运营机制设计，将效率和可衡量的公平性作为价值标准。拟议的议程包括一项综合研究和教育计划：(i)利用实验平台开发课程；（ii）本科生研究，学生协助建立实验平台，并与市政/行业合作伙伴合作；（三）女生研究实验室（Girls In Research Labs）的发展，这是一个为期一周的暑期项目，中学生通过动手项目探索校园内的研究实验室。对CPS科学的贡献将通过智能基础设施的研究，特别关注人在环CPS的独特行为，以及多式联运系统的应用。该技术计划基于决策科学（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。研究议程主要有两个重点：(i) SCPS的学习算法和（ii）通过激励和信息塑造SCPS的算法。所提出的工具集将能够分析包括人类在内的自主代理的多时间尺度决策，并与资源受限环境中的CPS基础设施相结合，并将允许学习和控制算法的可验证设计（例如，缓慢策略变化和实时控制的协同设计）。建模、综合和验证方法将为SCPS工程设计和操作提供一个有原则的、科学的基础，并通过为未来的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，社会技术限制），从而支持CPS教育。共同设计信息和激励的独特视角也将导致风险和不确定性建模的新工具，从而暴露出CPS工程中恢复能力的潜在新方法。该奖项反映了美国国家科学基金会的法定使命，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。

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.