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Lillian J. Ratliff

ratliffl at eecs.berkeley

Postdoctoral Researcher, EECS at UC Berkeley

( current research . publications . past research )


Current Research

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The next generation urban ecosystem, increasingly empowered by the Internet of Things, has at its core a shared economy where resources, both physical and information-based, are easily aggregated and exchanged via novel sharing mechanisms. As a result, a new market place is emerging in which these resources are a valuable commodity. However, the evolution of this market brings with it many challenges including 1) a large number of self-interested entities operating in the environment, 2) a rapidly growing number of sharing mechanisms, 3) a wide variety of resource constraints, and 4) new classes of vulnerabilities.

My research uses game theory to model the incentive structure (players and their motivations) of the emerging data market. A primary aim of this research is to develop learning and optimization schemes that will address the inefficiencies that arise in this market due to asymmetric information between the market participants, e.g. regulators, service providers (traditional and third-party), and end-users. In these schemes, each of these market participants is considered both as strategic data resources and resource seekers.

It is possible to quantify the value of the information content of the data acquired from tightly coupled infrastructure systems, thereby exposing tradeoffs between the data's utility and vulnerability. To balance this tradeoff, I derive user decision-making and resource consumption models that are incorporated into the design of market mechanisms such as contracts or incentives aimed at improving operational efficiency and resilience. One thrust of my work focuses on constructing adaptive incentive mechanisms to address adverse selection in smart, connected infrastructure systems. Further, I am recently interested in develop new notions for bounded/non-rational equilibria for modeling strategic decision-making using game theory and dynamical systems theory.

Smart Urban Mobility

In collaboration with Baosen Zhang and the Seattle Department of Transportation (SDOT) we are investigating various modes of urban mobility. As a start, we are currently looking at is parking and its utilization in Seattle's downtown. We are coupling smart meter data with exogenous data sources such as land use information and other economic indicators to build predictive models of occupancy, design pricing and information dissemination mechanisms, and ultimately create a living lab to test and validates these tools. We aim to create novel algorithms for prediction and control that leverage the richness of the spatially and temporally varying data streams. To support these efforts, we have a team of undergraduate students who are building a web map and smart device application for allowing users to view blockfaces near their destination and select a desirable parking location based on price, personal preferences, and historical and predicted occupancy. This tool will be used to conduct studies for increasing awareness of parking price and other strategies aimed at closing the loop around the end-user.

some relevant papers:

L. J. Ratliff, C. Dowling, E. Mazumdar, B. Zhang. To Observe or Not to Observe: Queuing Game Framework for Urban Parking. submitted to IEEE CDC, 2016. ( PDF)

D. Calderone, E. Mazumdar, L. J. Ratliff, S. S. Sastry. Understanding the Impact of Parking on Urban Mobility via Routing Games on Queue–Flow Networks. submitted to IEEE CDC, 2016. ( PDF)

Energy Efficiency via Gamification

In collaboration with UC Berkeley researchers in CREST, we have instrumented a collaboratory space on the Berkeley campus to allow occupants to vote according to their preferences for such things as lighting dim level, temperature setpoint, etc. Exploiting the actuation capabilities of the smart building, we implement the average of the occupants' votes and give points to each occupant based on how energy efficient their vote was in comparison to others. A lottery mechanism awards an occupant a monetary prize each week. We are simultaneously learning the decision-making process of agents via utility learning schemes that we developed and adaptively designing economic mechanisms to both elicit informative responses from agents and incentivize desirable outcomes. We are working towards implementing a larger scale version of this social game in Singapore.

some relevant papers:

I. C. Konstantakopoulos, L. J. Ratliff, M. Jin, C. Spanos, S. S. Sastry. Smart Building Energy Efficiency via Social Game: A Robust Utility Learning Framework for Closing–the–Loop. SCOPE [ACM/IEEE CPSWeek], 2016. ( PDF)

L J. Ratliff, M. Jin, I. C. Konstantakopoulos, C. Spanos, S. S. Sastry. Social Game for Building Energy Efficiency: Incentive Design. Allerton, 2014. (PDF)

M. Jin, L. J. Ratliff, I. C. Konstantakopoulos, C. Spanos, S. S. Sastry. REST: A Reliable Estimation and Stopping Time Algorithm for Social Game Experiments. ACM/IEEE ICCPS, 2015. (PDF)

Inverse Modeling Under Bounded Rationality Constraints

On the more theoretical side of my work, I develop new equilibrium concepts in game theory that are amenable to computation and are such that they can be leveraged in the design of incentive mechanisms. My work aims to go beyond the standard utility maximization modeling paradigm by folding in non-expected utility models that allow for varying degrees of rationality including new models that allow for distortion in the perception of the likelihood of events and their consequences. Moreover, I develop algorithms that learn correlations between multiple users decision making processes thereby allowing for discovery of coalitions and how they evolve over time. Using data from experiments as well as data collected in situ, I validate the equilibrium concepts and develop learning mechanisms for better estimation and prediction of decision-making processes.

some relevant papers:

L. J. Ratliff, S. A. Burden, S. S. Sastry. On the Characterization of Local Nash Equilibria in Continuous Games. IEEE TAC, 2014. (arXiv:1411.2168)

I. C. Konstantakopoulos, L. J. Ratliff, M. Jin, C. Spanos, S. S. Sastry. Inverse Modeling of Non-Cooperative Agents via Mixture of Utilities. submitted to IEEE CDC, 2016.( PDF)

L. J. Ratliff, S. A. Burden, S. S. Sastry. Genericity and Structural Stability of Non-degenerate Differential Nash Equilibria. ACC, 2014. (PDF)

Publications, &c.

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In Preparation

Lillian J. Ratliff, Roy Dong, Walid Krichene, and S. Shankar Sastry. "Utility Learning and Incentive Design via Adaptive Control," 2016.

Ioannis Konstantakopoulos, Lillian J. Ratliff, Ming Jin, Costas Spanos, and S. Shankar Sastry. "Utility Learning and Incentive Design for Sustainability in Building Energy Management," 2016.

Lillian J. Ratliff, Ioannis Konstantakopoulos, Ming Jin, Costas Spanos, and S. Shankar Sastry. "Inverse Modeling of Non-Cooperative Agents via Mixture of Utilities," 2016.

Published, Under Review, &c.

Lillian Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang. To Observe or Not to Observe: Queuing Game Framework for Urban Parking. IEEE Conference on Decision and Control, 2016. (submitted) ( PDF)

Ioannis C. Konstantakopoulos, Lillian Ratliff, Ming Jin, Costas Spanos, S. Shankar Sastry. Inverse Modeling of Non-Cooperative Agents via Mixture of Utilities. IEEE Conference on Decision and Control, 2016. (submitted) ( PDF)

Daniel Calderone, Eric Mazumdar, Lillian Ratliff, S. Shankar Sastry. Understanding the Impact of Parking on Urban Mobility via Routing Games on Queue–Flow Networks. IEEE Conference on Decision and Control, 2016. (submitted) ( PDF)

Ioannis C. Konstantakopoulos, Lillian Ratliff, Ming Jin, Costas Spanos, S. Shankar Sastry. Smart Building Energy Efficiency via Social Game: A Robust Utility Learning Framework for Closing–the–Loop. 1st International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE) [ACM/IEEE CPSWeek], 2016. (to appear) ( PDF)

Lillian J. Ratliff, Samuel A. Burden, S. Shankar Sastry. On the Characterization of Local Nash Equilibria in Continuous Games. IEEE Transactions on Automatic Control, 2014. (To Appear) (arXiv:1411.2168)

Lillian J. Ratliff. Incentivizing Efficiency in Societal-Scale Cyber-Physical Systems. Ph.D. Thesis, UC Berkeley, 2015. (PDF)

Lillian J. Ratliff, Carlos Barreto, Roy Dong, Henrik Ohlsson, Alvaro A. Cárdenas, and S. Shankar Sastry. Effects of Risk on Privacy Contracts for Demand-Side Management. (arXiv:1409.7926v3)

Pushkin Kachroo, Lillian J. Ratliff, and S. Shankar Sastry. Analysis of the Godunov Based Hybrid Model for Ramp Metering and Robust Feedback Control Design. IEEE Transactions on Intelligent Transportation Systems, Vol. 15, Issue 5, 2132-2142, Oct. 2014. ( PDF)

Roy Dong, Alvaro A. Cárdenas, Lillian J. Ratliff, Henrik Ohlsson, Shankar Sastry. Quantifying the Utility-Privacy Tradeoff in the Smart Grid. IEEE Transactions on Smart Grid, 2014. Under Review, 2nd Round (Apr. 2015). ( arXiv:1406.2568v1)

Dexter Scobee, Lillian Ratliff, Roy Dong, Henrik Ohlsson, Michel Verhaegen and S. Shankar Sastry. Nuclear Norm Minimization for Blind Subspace Identification (N2BSID). IEEE Conference on Decision and Control, 2015.

Daniel Calderone, Lillian Ratliff, S. Shankar Sastry. Lane Pricing via Decision–Theoretic Lane Changing Model of Driver Behavior. IEEE Conference on Decision and Control, 2015.

Ming Jin, Lillian J. Ratliff, Ioannis C. Konstantakopoulos, Costas Spanos, S. Shankar Sastry. REST: A Reliable Estimation and Stopping Time Algorithm for Social Game Experiments. ACM/IEEE International Conference on Cyber-Physical Systems, 2015. (PDF)

Lillian J. Ratliff, Ming Jin, Ioannis C. Konstantakopoulos, Costas Spanos, S. Shankar Sastry. Social Game for Building Energy Efficiency: Incentive Design. 52nd Annual Allerton Conference on Communication, Control, and Computing, 2014. (PDF, Slides)

Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, Alvaro A. Cardenas, Shankar Sastry. Privacy and Customer Segmentation in the Smart Grid. IEEE Conference on Decision and Control, 2014. (PDF)

Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, S. Shankar Sastry. Incentive Design and Utility Learning via Energy Disaggregation. 19th World Congress of the International Federation of Automatic Control, 2014. (PDF)

Daniel J. Calderone, Lillian J. Ratliff, Shankar Sastry. Pricing for Coordination in Open-Loop Differential Games. 19th World Congress of the International Federation of Automatic Control (IFAC), 2014. (PDF)

Henrik Ohlsson, Lillian J. Ratliff, Roy Dong, S. Shankar Sastry. Blind Identification via Lifting. 19th World Congress of the International Federation of Automatic Control (IFAC), 2014. (PDF)

Roy Dong, Lillian J. Ratliff, Henrik Ohlsson, Shankar Sastry. Fundamental Limits of Non-Intrusive Load Monitoring. ACM International Conference on High Confidence Networked Systems (HiCoNS), 2014. DOI: 10.1145/2566468.2566471 (arXiv)

Lillian J. Ratliff, Roy Dong, Henrik Ohlsson, and S. Shankar Sastry. Energy Efficiency via Incentive Design and Utility Learning. ACM International Conference on High Confidence Networked Systems (HiCoNS), 2014. DOI: 10.1145/2566468.2576849 (PDF)

Lillian J. Ratliff, Samuel A. Burden, Shankar Sastry. Genericity and Structural Stability of Non-degenerate Differential Nash Equilibria. American Control Conference, 2014. DOI: 10.1109/ACC.2014.6858848 (PDF)

Lillian J. Ratliff, Samuel A. Burden, Shankar Sastry. Characterization and Computation of Local Nash Equilibria in Continuous Games. 51st Annual Allerton Conference on Communication, Control, and Computing, 2013. DOI: 10.1109/Allerton.2013.6736623 (PDF, Slides)

Henrik Ohlsson, Lillian Ratliff, Roy Dong, Shankar Sastry. Blind Identification of ARX Models with Piecewise Constant Inputs. arXiv:1303.6719, 2013. (PDF)

Roy Dong, Lillian J. Ratliff, Henrik Ohlsson, S. Shankar Sastry. Energy Disaggregation via Adaptive Filtering. 51st Annual Allerton Conference on Communication, Control, and Computing, 2013. DOI: 10.1109/Allerton.2013.6736521 (arXiv)

Roy Dong, Lillian J. Ratliff, Henrik Ohlsson, Shankar Sastry. A Dynamical Systems Approach to Energy Disaggregation. IEEE Conference on Decision and Control, 2013. pg. 6335-6340. DOI: 10.1109/CDC.2013.6760891 (arXiv, PDF)

Daniel Calderone, Lillian J. Ratliff, S. Shankar Sastry. Pricing Design for Robustness in Linear-Quadratic Dynamic Games. IEEE Conference on Decision and Control, 2013. DOI: 10.1109/CDC.2013.6760558

Aaron Bestick, Lillian J. Ratliff, Po Yan, Ruzena Bajcsy, S. Shankar Sastry. An Inverse Correlated Equilibrium Framework for Utility Learning in Multiplayer, Noncooperative Settings. ACM International Conference on High Confidence Networked Systems (HiCoNS), 2013. DOI: 10.1145/2461446.2461449 (PDF)

Samuel Coogan, Lillian J. Ratliff, Daniel Calderone, Claire Tomlin, S. Shankar Sastry. Energy Management via Pricing in LQ Dynamic Games. American Control Conference, 2013. DOI: 10.1109/ACC.2013.6579877 (PDF)

Lillian J. Ratliff, Samuel Coogan, Daniel Calderone, S. Shankar Sastry. Pricing in Linear-Quadratic Dynamic Games. 50th Annual Allerton Conference on Communication, Control, and Computing, 2012. DOI: 10.1109/Allerton.2012.6483440 (PDF)

Lillian J. Ratliff and Pushkin Kachroo. Validating numerically consistent macroscopic traffic models using microscopic data. Transportation Research Board 89th Annual Meeting, 2010.

Daniel P. Cook, Yitung Chen, Lillian J. Ratliff, Huajun Chen, and Jian Ma. Numerical Modeling of EM Pump Efficiency. ASME Conference Proceedings, 2006.

Awards

NSF Graduate Fellowship, 2009