7,504 research outputs found
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
Team decision theory for linear continuous-time systems
This paper develops a team decision theory for linear-quadratic (LQ) continuous-time systems. First, a counterpart of the well-known result of Radner on quadratic static teams is obtained for two-member continuous-time LQ static team problems when the statistics of the random variables involved are not necessarily Gaussian. An iterative convergent scheme is developed, which in the limit yields the optimal team strategies. For the special case of Gaussian distributions, the team-optimal solution is affine in the information available to each DM, and for the further special case when the team cost function does not penalize the intermediate values of state, the optimal strategies can be obtained by solving a Liapunov type time-invariant matrix equation. This static theory is then extended to LQG continuous-time dynamic teams with sampled observations under the one-step-delay observation sharing pattern. The unique solution is again affine in the information available to each DM, and further, it features a certainty-equivalence property
Structural Results for Decentralized Stochastic Control with a Word-of-Mouth Communication
In this paper, we analyze a network of agents that communicate through the
``word of mouth," in which, every agent communicates only with its neighbors.
We introduce the prescription approach, present some of its properties and show
that it leads to a new information state. We also state preliminary structural
results for optimal control strategies in systems that evolve using
word-of-mouth communication. The proposed approach can be generalized to
analyze several decentralized systems
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
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