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RESEARCH PRODUCT
Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension
Dario BausoThulasi MylvaganamAlessandro Astolfisubject
0209 industrial biotechnologyStochastic stabilityMathematical optimizationCollective behaviorTechnologyComputer sciencePopulationcontrol designcrowd-averse robust mean-field games state space extension dynamic agents linear stochastic differential equation Brownian motion adversarial disturbance cost functional cross-coupling mean-field term collective behavior stock market application production engineering example dynamic demand management problem robust mean-field game approximation error stochastic stability microscopic dynamics macroscopic dynamicscontrol engineering02 engineering and technology01 natural sciencesStochastic differential equationoptimal control020901 industrial engineering & automationQuadratic equationAutomation & Control SystemsEngineeringClosed loop systemsSettore ING-INF/04 - AutomaticaApproximation errorRobustness (computer science)Control theory0102 Applied MathematicsState space0101 mathematicsElectrical and Electronic EngineeringeducationBrownian motioneducation.field_of_studyScience & TechnologyStochastic process010102 general mathematicsRelaxation (iterative method)Engineering Electrical & ElectronicOptimal controlComputer Science Applications0906 Electrical and Electronic EngineeringIndustrial Engineering & AutomationMean field theoryControl and Systems EngineeringSettore MAT/09 - Ricerca Operativa0913 Mechanical Engineeringdescription
We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a cross-coupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term “crowd-averse.” Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For the problem in its abstract formulation, we illustrate the paradigm of robust mean-field games. Main contributions involve first the formulation of the problem as a robust mean-field game; second, the development of a new approximate solution approach based on the extension of the state space; third, a relaxation method to minimize the approximation error. Further results are provided for the scalar case, for which we establish performance bounds, and analyze stochastic stability of both the microscopic and the macroscopic dynamics.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |