"Incentivizing Efficiency in Societal-Scale Cyber-Physical Systems"

Lillian Ratliff, Faculty Candidate, UC Berkeley

March 11th (Wednesday), 10:00am
Harold Frank Hall (HFH), Rm 4164

In the modernization of infrastructure systems spanning energy, transportation, and health we are seeing the convergence of big data analytics, cyber-physical systems, and the internet of things. The resulting societal-scale cyber-physical systems (S-CPS) provide new opportunities for efficiency yet expose novel vulnerabilities. In the energy systems, for example, the availability of streaming data from smart metering enables monetization of energy savings. These savings can be realized by employing novel variants of machine learning algorithms to generate energy analytics that allow customization of offerings to consumers and by creating the economic incentives necessary for investment in the instrumentation of physical infrastructure. On the other hand, these emerging service models depend on the underlying CPS infrastructure and thus, reveal new vulnerabilities due to ubiquitous sensing, real-time constraints, and ‘closing-the-loop’ attributes. This efficiency-vulnerability tradeoff is a fundamental challenge facing S-CPS, wherein scarce resources must be allocated amongst competitive agents with misaligned goals. To manage this tradeoff, a coordinator can provide incentives to align these goals, for instance, by ensuring the equilibrium behavior optimizes a societal cost.

In this talk, I present an algorithm for synthesizing incentive strategies that lead to efficient behavior when the preferences of the underlying agents are unknown to the coordinator and must be learned. In support of the incentive design and learning steps in the algorithm, I present an intrinsic characterization of Nash equilibria that is amenable to computation. This learning and mechanism design procedure aims to bridge the gap between the non-cooperative Nash equilibrium and a more efficient, perhaps socially optimal, solution. A coordinator can leverage the underlying CPS infrastructure to enhance system efficiency at the expense of revealing vulnerabilities. By focusing on the demand-side of the power grid, I provide tools for analysis of consumer privacy and the design of economic mechanisms for balancing the efficiency-vulnerability tradeoff. The combination of data-driven models and game-theoretic tools I present provides the foundation for a systems theory of S-CPS.

About Lillian Ratliff:

photo of lillian ratliff Lillian Ratliff is a Ph.D. candidate in Electrical Engineering and Computer Sciences at UC Berkeley and expects to graduate in May of 2015. Her research interests lie at the intersection of the study of game theory, dynamical systems, statistical learning and societal-scale cyber-physical systems (S-CPS). She is interested in utilizing the complex datasets captured through new sensing and control technologies being deployed in critical infrastructure such as intelligent energy, transportation and healthcare systems in order to develop data-driven models and analytics for both system and agent behavior. Further, she strives to synergistically combine data-driven modeling and analytics with game-theoretic tools that capture complex socioeconomic interactions in support of analysis of vulnerabilities and synthesis of economic and physical control for balancing the efficiency-vulnerability tradeoff inherent to S-CPS. She is the recipient of a National Science Foundation Graduate Research Fellowship.

Hosted by: Professor Andrew Teel