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

这个CAREER项目的目标是开发一个理论和计算框架，用于在社会规模网络物理系统（SCPS）中针对人类的信息和激励机制的共同设计，以鼓励有效的共享资源消耗，同时减轻意外后果。应用重点是智能交通系统，一个原型的SCPS与人类在循环，快速技术采用和新兴的移动市场。CPS和物联网（IoT）基础设施在移动领域中无处不在，允许操作员和智能增强的人类在相互交互并参与系统时快速做出决策。支持互动的市场机制发生在多个时间尺度上，受到CPS动态的约束，并暴露于外部不确定性，信息不对称和人类决策的行为方面。通过设计保证学习和塑造SCPS的算法，该项目将解决最先进技术中缺少的两个基本组件：（i）捕捉不同SCPS利益相关者的互动和学习过程的模型，和（ii）具有高概率保证的可验证算法，用于自适应信息和激励机制的协同设计，以实现系统的可测量改进-在确保个人服务质量和避免歧视性政策的同时，验证方法使用具有模拟和生活实验室组件的数据通知实验平台。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重要标准。拟议的议程包括一个综合研究和教育计划：（一）利用实验平台的课程开发;（二）本科生研究，学生帮助建立实验平台，并与市政/行业合作伙伴接触;（三）发展研究实验室中的女孩，一个为期一周的暑期项目，在这个项目中，中学女生通过双手探索校园内的研究实验室-对CPS科学的贡献将通过对智能基础设施的研究来实现，特别关注人类在循环CPS中独特的行为，以及对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。研究议程是组织沿着两个关键的推力：（一）算法的学习SCPS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，以及资源受限环境中的CPS基础设施，并将允许用于学习和控制的算法的可认证设计（例如，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原则性的科学基础，并通过为未来的工程师提供平台来支持CPS教育，以发现与现实世界实施相关的现实（例如，社会技术限制）。共同设计信息和激励措施的独特视角也将带来新的风险和不确定性建模工具，从而为CPS工程中的弹性提供潜在的新方法。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.