**Lillian J. Ratliff**

**ratliffl at eecs.berkeley **

My research interests lie at the intersection of non-cooperative games, incentive design and societal scale cyber-physical systems. We now collect very large and complex data sets through new sensing and control technologies that are currently being deployed in critical infrastructure such as healthcare and transportation systems as well as the smart grid. I am interested in developing data-driven models for agent behavior in social cyber-physical systems and utilizing these models to design incentives for behavior modification. Incentives give rise to new vulnerabilities such as privacy and security issues. Currently I am working on designing privacy-preserving and resilient incentive strategies.

The aim of this project is to utilize tools from differential topology and geometry to study the interaction of competitive agents. In particular, by looking at games in which the strategy spaces are non-convex, we may develop useful decompositions in order to study existence of equilibria and their properties in this more abstract setting. Further, we have developed a characterization of local Nash equilibria that is computable. We term these equilibria *differential* Nash equilibria. The goal is to use the analytical and computational advancements to developing novel schemes for decentralized control in engineered systems as well as onâ€“line identification techniques for human agents. (Collaborator: Samuel Burden)

We model the interaction between non--cooperative agents and a social planner as a reversed Stackelberg game where the social planner is the leader and the agents are the followers that play simultaneously. The social planner would like to modify the agents' behavior so that it aligns with some desired behavior which allows the social planner to meet her objective. However, we assume that the social planner does not know the agents' utility functions and hence must learn them in order to design incentives which modify the agents behavior.

The goal of this project is to design pricing mechanisms to coordinate non-cooperative agents. Applications that are considered include efficient energy management in buildings, controlled diffusion in multi-agent networks, and network security. In a non-cooperative game theoretic framework, the goal is to design pricing schemes to close the gap between the decentralized and the centralized cost. In addition, we are interested in designing robust pricing mechanisms. In an effort to do so, we first characterize when feedback Nash strategies are stable and use this characterization to inform the design of pricing mechanisms. (Collaborators: Sam Coogan, Daniel Calderone)

Disaggregation is the process of taking an aggregated signal and decomposing it into its components. The application we are interested in is energy disaggregation where the aggregate signal is the power consumption for a whole building (residential or commercial) and the goal is to decompose the aggregate signal into the power signal for all the contributing devices. By providing the disaggregated data to consumers, we are enabling them to construct methods for energy efficient operation of the building. Disaggregation of consumer energy data evokes privacy concerns. There is an inherent trade-off between privacy and disaggregation. Our goal is to develop disaggregation algorithms that provide useful information to consumers while preserving some degree of privacy. In addition, we are developing techniques for behavior modification using tools from game theory and mechanism design. The mechanisms we seek are to some degree privacy preserving. (Collaborators: Aaron Bestick, Henrik Ohlsson, Roy Dong)

(planned) CDC, LA, Dec. 2014, Organizing and speaking at workshop on 'Big Data Analytics for Societal Scale Cyber-Physical Systems: Energy Systems'

(planned) IFAC, Cape Town, South Africa, August 2014, Incentive Design and Utility Learning via Energy Disaggregation.

ACC, Portland Oregon, 06 June 2014, Genericty and Structural Stability of Non-degenerate Differential Nash Equilibria

Stanford, 30 May 2014, Game Theoretic Tools for Societal Scale Cyber-Physical Systems

PDE Seminar, Math Department Berkeley, 09 May 2014, Convexity in Multidimensional Screening

PDE Seminar, Math Department Berkeley, 25 April 2014, Multidimensional Screening

EECS Seminar, University of Michigan, 25 March 2014, Game Theoretic Tools for Societal Scale CPS

**Lillian J. Ratliff**, Pushkin Kachroo, and S. Shankar Sastry. "Observability and Resilience in Networked Transportation Systems." 2014.

**Lillian J. Ratliff**, Daniel Calderone, Samuel Coogan, S. Shankar Sastry. "Pricing for Coordination in Dynamic Games." 2014.

**Lillian J. Ratliff**, Samuel A. Burden, S. Shankar Sastry. "On the Characterization and Computation of Local Nash Equilibria." 2014.

** Lillian J. Ratliff**, Roy Dong, Henrik Ohlsson, S. Shankar Sastry. "Incentive Design and Utility Learning in Competitive Multi-Agent Systems." 2014.

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 Trans. on Intelligent
Transportation Systems, 2014. *To Appear. *

Roy Dong, Alvaro A. Cardenas, ** Lillian J. Ratliff**, Henrik
Ohlsson, Shankar Sastry. "Quantifying the Utility-Privacy Tradeoff in
the Smart Grid." IEEE Trans. on Smart Grid, 2014. *Submitted.*

Pushkin Kachroo, **Lillian J. Ratliff**, and S. Shankar Sastry.
"A New Static Traffic Assignment Using Density Based Travel Time."
Applied Mathematical Modeling, 2014. * Submitted. *

** Lillian J. Ratliff**, Ming Jin, Ioannis C. Konstantakopoulos,
Costas Spanos, S. Shankar
Sastry. "Social Game for Building Energy Efficiency: Incentive Design."
Allerton, 2014. *To Appear.*

Ioannis C. Konstantakopoulos, ** Lillian J. Ratliff**, Ming Jin, S. Shankar
Sastry, Costas Spanos. "Social Game for Building Energy Efficiency: Utility Learning, Simulation, and Analysis."
2014. *Submitted.*

** Lillian J. Ratliff**, Roy Dong, Henrik Ohlsson, Alvaro A.
Cardenas, Shankar Sastry. "Privacy and Customer Segmentation in the
Smart Grid." CDC, 2014. *To Appear.*

** Lillian J. Ratliff**, Roy Dong, Henrik Ohlsson, S. Shankar Sastry. "Incentive Design and Utility Learning via Energy Disaggregation." IFAC, 2014. *To Appear.*

Daniel J. Calderone, ** Lillian J. Ratliff**, Shankar Sastry. "Pricing for Coordination in Open-Loop Differential Games." IFAC, 2014. *To Appear.*

Henrik Ohlsson, ** Lillian J. Ratliff**, Roy Dong, S. Shankar Sastry. "Blind Identification via Lifting." IFAC, 2014. *To Appear.*

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

** 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

** 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)

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." Conference on High Confidence Networked Systems, 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.

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

** Lillian J. Ratliff**, Roy Dong, Henrik Ohlsson, and S. Shankar Sastry.
Energy Efficiency via Incentive Design and Utility Learning. HiCoNS, 2014. DOI:
10.1145/2566468.2576849
(PDF)

**Lillian J. Ratliff**, Samuel A. Burden, S. Shankar Sastry. "Characterization and Computation of Local Nash Equilibria in Continuous Games." at TRUST Annual Conference, Washington, D.C. April, 2013. (PDF)

**Lillian Ratliff**, Daniel Calderone, Samuel Coogan, S. Shankar Sastry. "Pricing for Coordination in Dynamic Games." at FORCES Kickoff Meeting, Washington, D.C. April, 2013. (PDF)

Daniel Calderone, Samuel Coogan, **Lillian Ratliff**, Anil Aswani, Claire Tomlin, S. Shankar Sastry. "Quadratic incentive design for noncooperative distributed control" *Conference on High Confidence Networked Systems (HiCoNS)* at CPS Week 2012, Beijing, China, May 2012. (PDF)

NSF Graduate Fellowship, 2009

MS thesis topic: Uncertainty Propagation in Dynamical Systems.

Advisor: Pushkin Kachroo