2,421 research outputs found
Signaling equilibria for dynamic LQG games with asymmetric information
We consider a finite horizon dynamic game with two players who observe their
types privately and take actions, which are publicly observed. Players' types
evolve as independent, controlled linear Gaussian processes and players incur
quadratic instantaneous costs. This forms a dynamic linear quadratic Gaussian
(LQG) game with asymmetric information. 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.
Furthermore, it is shown that this is a signaling equilibrium due to the fact
that future beliefs on players' types are affected by the equilibrium
strategies. We provide a backward-forward algorithm to find the PBE. Each step
of the backward algorithm reduces to solving an algebraic matrix equation for
every possible realization of the state estimate covariance matrix. The forward
algorithm consists of Kalman filter recursions, where state estimate covariance
matrices depend on equilibrium strategies
Dynamic Models of Appraisal Networks Explaining Collective Learning
This paper proposes models of learning process in teams of individuals who
collectively execute a sequence of tasks and whose actions are determined by
individual skill levels and networks of interpersonal appraisals and influence.
The closely-related proposed models have increasing complexity, starting with a
centralized manager-based assignment and learning model, and finishing with a
social model of interpersonal appraisal, assignments, learning, and influences.
We show how rational optimal behavior arises along the task sequence for each
model, and discuss conditions of suboptimality. Our models are grounded in
replicator dynamics from evolutionary games, influence networks from
mathematical sociology, and transactive memory systems from organization
science.Comment: A preliminary version has been accepted by the 53rd IEEE Conference
on Decision and Control. The journal version has been submitted to IEEE
Transactions on Automatic Contro
Multi-Layer Cyber-Physical Security and Resilience for Smart Grid
The smart grid is a large-scale complex system that integrates communication
technologies with the physical layer operation of the energy systems. Security
and resilience mechanisms by design are important to provide guarantee
operations for the system. This chapter provides a layered perspective of the
smart grid security and discusses game and decision theory as a tool to model
the interactions among system components and the interaction between attackers
and the system. We discuss game-theoretic applications and challenges in the
design of cross-layer robust and resilient controller, secure network routing
protocol at the data communication and networking layers, and the challenges of
the information security at the management layer of the grid. The chapter will
discuss the future directions of using game-theoretic tools in addressing
multi-layer security issues in the smart grid.Comment: 16 page
Mean-Field-Type Games in Engineering
A mean-field-type game is a game in which the instantaneous payoffs and/or
the state dynamics functions involve not only the state and the action profile
but also the joint distributions of state-action pairs. This article presents
some engineering applications of mean-field-type games including road traffic
networks, multi-level building evacuation, millimeter wave wireless
communications, distributed power networks, virus spread over networks, virtual
machine resource management in cloud networks, synchronization of oscillators,
energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201
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 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
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