Lillian J. RatliffPh.D. student, EECS at UC Berkeley
Generally, I am interested in game theory and dynamical systems. In particular, I am examining ways in which games can be decomposed from a structural point of view. Further, I am interested in mechanism design for games that arise in engineering problems. I am interested in using tools from economics and game theory to solve engineering problems such as decentralization of control and other problems such as resource allocation in multi-agent networks. Currently, I am working on a couple different projects.Pricing for coordination of noncooperative agents:
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)Differential topology and game theory:
The aim of this project is to utilize tools from differential algebraic topology to study the interaction of competitive agents. In particular, by looking at games on manifolds we may develop useful decompositions in order to study existence of equilibria and their properties in this more abstract setting.Utility learning in games:
It is often that case that one desires to compute an equilibrium given the utility functions of selfish agents. We pose the inverse question. That is we are interested in learning, from observations of the outcome of a series of games, the utility functions of the participating agents. Given a correlated equilibrium of a game and observations of the players actions, we solve the inverse problem of determining the utility function of each of the agents by parameterizing their utility functions and learning the weights. The tools that we have developed thus far are for finite games. We are investigating methods to extend the results to infinite games. (Collaborators: Aaron Bestick)Disaggregation:
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. (Collaborators: Aaron Bestick, Henrik Ohlsson, Roy Dong)
Henrik Ohlsson, Lillian Ratliff, Roy Dong, Shankar Sastry. "Blind Identification of ARX Models with Piecewise Constant Inputs." 2013. Submitted. ( preprint )
Roy Dong, Lillian Ratliff, Henrik Ohlsson, Shankar Sastry. "A Dynamical Systems Approach to Energy Disaggregation." 2013. Submitted. ( preprint )
Daniel Calderone, Lillian J. Ratliff, S. Shankar Sastry. "Pricing Design for Robustness in Linear-Quadratic Dynamic Games." 2013. Submitted.
Aaron Bestick, Lillian Ratliff, Po Yan, Ruzena Bajcsy, S. Shankar Sastry. "An Inverse Correlated Equilibrium Framework for Utility Learning in Multiplayer, Noncooperative Settings." 2013 Conference on High Confidence Networked Systems. To Appear.
Samuel Coogan, Lillian Ratliff, Daniel Calderone, Claire Tomlin, S. Shankar Sastry. "Energy Management via Pricing in LQ Dynamic Games." 2013 American Control Conference. To Appear.
Lillian Ratliff, Samuel Coogan, Daniel Calderone, S. Shankar Sastry. " Pricing in Linear-Quadratic Dynamic Games." in 50th Annual Allerton Conference on Communication, Control, and Computing, 2012. DOI: 10.1109/Allerton.2012.6483440
Lillian Ratliff and Pushkin Kachroo. "Validating numerically consistent macroscopic traffic models using microscopic data," in 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" in ASME Conference Proceedings, 2006, pp:775--780.
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)
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. (As work in Progress)
My research is funded by an NSF graduate research fellowship.
MS thesis topic: Uncertainty Propagation in Dynamical Systems.
Advisor: Pushkin Kachroo