Career: Learning for Strategic Interactions in Societal-Scale Cyber-Physical Systems

In societal-scale cyber-physical systems (SCPS), machine learning algorithms are increasingly becoming the interface between stakeholders---from matching drivers and riders on ride-sharing platforms to the real-time scheduling of energy resources in electric vehicle (EV) charging stations. The fact that the different stakeholders in these systems have different objectives gives rise to strategic interactions which can result in inefficiencies and negative externalities across the SCPS. This NSF CAREER project seeks to develop a foundational understanding of the strategic interactions that arise in SCPS, the impacts they have on social welfare, and how they affect algorithmic decision-making. The goal is to shift how engineers design algorithms for SCPS. Currently, learning algorithms are trained and developed in isolation, and uncertainty and strategic interactions are treated---if at all--- as adversarial or worst-case. In contrast, the proposed research aims to develop algorithms that can consider economic interactions, human behavior, and uncertainty when making decisions. The theory and algorithms developed through this project will be validated on two physical testbeds: 1. an EV charging testbed where drivers routinely mis-report preferences for faster charging, and 2. the Caltech Social Science Experimental Laboratory where controlled experiments will be conducted to understand how people respond to algorithms. The proposal also includes an integrated education and outreach plan, which includes outreach to K-12 students and new undergraduate and graduate courses on the complexities of learning in SCPS. Key goals of this project include developing a unified design methodology for learning in the presence of strategic behaviors in SCPS and the systematic study of the control actions and control authority that individual users and policymakers can wield to achieve societal goals. The fact that strategic manipulations in SCPS are played out through the (mis)-reporting of data or through algorithmic decision-making distinguishes these problems from those classically studied in game theory and economics. Furthermore, in contrast with existing work in computer science and economics that study strategic interactions, this project aims to take a dynamic view of SCPS, which leverages tools and ideas from dynamical systems theory and stochastic processes to complement ideas in machine learning, game theory, and behavioral economics. This perspective will allow for new insights into how repeated interactions affect strategic decision-making in SCPS and which design decisions impact learning in game theoretic settings. This opens the door to new insights and the analysis of previously overlooked control knobs for achieving societal goals in SCPS. 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.