Subjective Equilibria under Beliefs of Exogenous Uncertainty

We present a subjective equilibrium notion (called "subjective equilibrium under beliefs of exogenous uncertainty (SEBEU)" for stochastic dynamic games in which each player chooses its decisions under the (incorrect) belief that a stochastic environment process driving the system is exogenous whereas in actuality this process is a solution of closed-loop dynamics affected by each individual player. Players observe past realizations of the environment variables and their local information. At equilibrium, if players are given the full distribution of the stochastic environment process as if it were an exogenous process, they would have no incentive to unilaterally deviate from their strategies. This notion thus generalizes what is known as the price-taking equilibrium in prior literature to a stochastic and dynamic setup. We establish existence of SEBEU, study various properties and present explicit solutions. We obtain the $\epsilon$-Nash equilibrium property of SEBEU when there are many players

Optimal Control for LQG Systems on Graphs---Part I: Structural Results

In this two-part paper, we identify a broad class of decentralized output-feedback LQG systems for which the optimal control strategies have a simple intuitive estimation structure and can be computed efficiently. Roughly, we consider the class of systems for which the coupling of dynamics among subsystems and the inter-controller communication is characterized by the same directed graph. Furthermore, this graph is assumed to be a multitree, that is, its transitive reduction can have at most one directed path connecting each pair of nodes. In this first part, we derive sufficient statistics that may be used to aggregate each controller's growing available information. Each controller must estimate the states of the subsystems that it affects (its descendants) as well as the subsystems that it observes (its ancestors). The optimal control action for a controller is a linear function of the estimate it computes as well as the estimates computed by all of its ancestors. Moreover, these state estimates may be updated recursively, much like a Kalman filter

Dynamic Decision Problems with Cooperative and Strategic Agents and Asymmetric Information.

There exist many real world situations involving multiple decision makers with asymmetric information, such as communication systems, social networks, economic markets and many others. Through this dissertation, we attempt to enhance the conceptual understanding of such systems and provide analytical tools to characterize the optimum or equilibrium behavior. Specifically, we study four discrete time, decentralized decision problems in stochastic dynamical systems with cooperative and strategic agents. The first problem we consider is a relay channel where nodes' queue lengths, modeled as conditionally independent Markov chains, are nodes' private information, whereas nodes' actions are publicly observed. This results in non-classical information pattern. Energy-delay tradeoff is studied for this channel through stochastic control techniques for cooperative agents. Extending this model for strategic users, in the second problem we study a general model with $N$ strategic players having conditionally independent, Markovian types and publicly observed actions. This results in a dynamic game with asymmetric information. We present a forward/backward sequential decomposition algorithm to find a class of perfect Bayesian equilibria of the game. Using this methodology, in the third problem, we study a general two player dynamic LQG game with asymmetric information, where players' types evolve as independent, controlled linear Gaussian processes and players incur quadratic instantaneous costs. We show that under certain conditions, players' strategies that are linear in their private types, together with Gaussian beliefs, form a perfect Bayesian equilibrium (PBE) of the game. Finally, we consider two sub problems in decentralized Bayesian learning in dynamic games. In the first part, we consider an ergodic version of a sequential buyers game where strategic users sequentially make a decision to buy or not buy a product. In this problem, we design incentives to align players' individual objectives with the team objective. In the second part, we present a framework to study learning dynamics and especially informational cascades for decentralized dynamic games. We first generalize our methodology to find PBE to the case when players do not perfectly observe their types; rather they make independent, noisy observations. Based on this, we characterize informational cascades for a specific learning model.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133294/1/dvasal_1.pd

Mean Field Equilibrium in Dynamic Games with Complementarities

We study a class of stochastic dynamic games that exhibit strategic complementarities between players; formally, in the games we consider, the payoff of a player has increasing differences between her own state and the empirical distribution of the states of other players. Such games can be used to model a diverse set of applications, including network security models, recommender systems, and dynamic search in markets. Stochastic games are generally difficult to analyze, and these difficulties are only exacerbated when the number of players is large (as might be the case in the preceding examples). We consider an approximation methodology called mean field equilibrium to study these games. In such an equilibrium, each player reacts to only the long run average state of other players. We find necessary conditions for the existence of a mean field equilibrium in such games. Furthermore, as a simple consequence of this existence theorem, we obtain several natural monotonicity properties. We show that there exist a "largest" and a "smallest" equilibrium among all those where the equilibrium strategy used by a player is nondecreasing, and we also show that players converge to each of these equilibria via natural myopic learning dynamics; as we argue, these dynamics are more reasonable than the standard best response dynamics. We also provide sensitivity results, where we quantify how the equilibria of such games move in response to changes in parameters of the game (e.g., the introduction of incentives to players).Comment: 56 pages, 5 figure