78 citations · 333 across the 57 of their papers we have counts for
21 papers · 1 filter
Consistent Conjectural Variations Equilibrium: Characterization & Stability for a Class of Continuous Games
Daniel J. Calderone, Benjamin J. Chasnov, Samuel A. Burden +1
Leveraging tools from the study of linear fractional transformations and algebraic Riccati equations, a local characterization of consistent conjectural variations equilibrium is g…
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games
Arnab Maiti, Kevin Jamieson, Lillian J. Ratliff
We study the sample complexity of identifying an approximate equilibrium for two-player zero-sum matrix games. That is, in a sequence of repeated game plays, how many r…
Fast Convergence of Optimistic Gradient Ascent in Network Zero-Sum Extensive Form Games
Georgios Piliouras, Lillian Ratliff, Ryann Sim +1
The study of learning in games has thus far focused primarily on normal form games. In contrast, our understanding of learning in extensive form games (EFGs) and particularly in EF…
Multiplayer Performative Prediction: Learning in Decision-Dependent Games
Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy +2
Learning problems commonly exhibit an interesting feedback mechanism wherein the population data reacts to competing decision makers' actions. This paper formulates a new game theo…
Online Learning in Periodic Zero-Sum Games
Tanner Fiez, Ryann Sim, Stratis Skoulakis +2
A seminal result in game theory is von Neumann's minmax theorem, which states that zero-sum games admit an essentially unique equilibrium solution. Classical learning results build…
Improved Rates for Derivative Free Gradient Play in Strongly Monotone Games
Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J Ratliff
The influential work of Bravo et al. 2018 shows that derivative free play in strongly monotone games has complexity , where is the target accura…
Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games
Stratis Skoulakis, Tanner Fiez, Ryann Sim +2
The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interac…
Stability of Gradient Learning Dynamics in Continuous Games: Scalar Action Spaces
Benjamin J. Chasnov, Daniel Calderone, Behçet Açıkmeşe +2
Learning processes in games explain how players grapple with one another in seeking an equilibrium. We study a natural model of learning based on individual gradients in two-player…
Stability of Gradient Learning Dynamics in Continuous Games: Vector Action Spaces
Benjamin J. Chasnov, Daniel Calderone, Behçet Açıkmeşe +2
Towards characterizing the optimization landscape of games, this paper analyzes the stability of gradient-based dynamics near fixed points of two-player continuous games. We introd…
Disturbance Decoupling for Gradient-based Multi-Agent Learning with Quadratic Costs
Sarah H. Q. Li, Lillian Ratliff, Behçet Açıkmeşe
Motivated by applications of multi-agent learning in noisy environments, this paper studies the robustness of gradient-based learning dynamics with respect to disturbances. While d…
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in `Almost All' Zero-Sum Continuous Games
Eric Mazumdar, Lillian Ratliff
We prove that differential Nash equilibria are generic amongst local Nash equilibria in continuous zero-sum games. That is, there exists an open-dense subset of zero-sum games for…
Sensitivity Analysis for Markov Decision Process Congestion Games
Sarah H. Q. Li, Daniel Calderone, Lillian Ratliff +1
We consider a non-atomic congestion game where each decision maker performs selfish optimization over states of a common MDP. The decision makers optimize for their own expected co…
Adaptive Constraint Satisfaction for Markov Decision Process Congestion Games: Application to Transportation Networks
Sarah H. Q. Li, Yue Yu, Nicolas Miguel +3
Under the Markov decision process (MDP) congestion game framework, we study the problem of enforcing population distribution constraints on a population of players with stochastic…
Convergence of Learning Dynamics in Stackelberg Games
Tanner Fiez, Benjamin Chasnov, Lillian J. Ratliff
This paper investigates the convergence of learning dynamics in Stackelberg games. In the class of games we consider, there is a hierarchical game being played between a leader and…
Competitive Statistical Estimation with Strategic Data Sources
Tyler Westenbroek, Roy Dong, Lillian J. Ratliff +1
In recent years, data has played an increasingly important role in the economy as a good in its own right. In many settings, data aggregators cannot directly verify the quality of…
Tolling for Constraint Satisfaction in Markov Decision Process Congestion Games
Sarah H. Q. Li, Yue Yu, Daniel Calderone +2
Markov decision process (MDP) congestion game is an extension of classic congestion games, where a continuous population of selfish agents solves Markov decision processes with con…
Adaptive Incentive Design
Lillian J. Ratliff, Tanner Fiez
We apply control theoretic and optimization techniques to adaptively design incentives. In particular, we consider the problem of a planner with an objective that depends on data f…
Towards a Socially Optimal Multi-Modal Routing Platform
Chinmaya Samal, Liyuan Zheng, Fangzhou Sun +2
The increasing rate of urbanization has added pressure on the already constrained transportation networks in our communities. Ride-sharing platforms such as Uber and Lyft are becom…
Uncertainty in Multi-Commodity Routing Networks: When does it help?
Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff +1
We study the equilibrium behavior in a multi-commodity selfish routing game with many types of uncertain users where each user over- or under-estimates their congestion costs by a…
A Robust Utility Learning Framework via Inverse Optimization
Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin +2
In many smart infrastructure applications flexibility in achieving sustainability goals can be gained by engaging end-users. However, these users often have heterogeneous preferenc…
Statistical Estimation with Strategic Data Sources in Competitive Settings
Tyler Westenbroek, Roy Dong, Lillian J. Ratliff +1
In this paper, we introduce a preliminary model for interactions in the data market. Recent research has shown ways in which a data aggregator can design mechanisms for users to en…