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.