Large-Population Cost-Coupled LQG Problems With Nonuniform Agents: Individual-Mass Behavior and Decentralized $\varepsilon$-Nash Equilibria

By Minyi Huang, Peter E. Caines, Roland P. Malhamé. 2007

828 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Public Health, Environmental and Occupational Health

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Citations in the corpus

Mean Field Analysis of Controlled Cucker-Smale Type Flocking: Linear Analysis and Perturbation Equations (2011, methodology): “Based on the MF approach developed in (Huang et al., 2007), we derived an individual based MF equation system of the dynamic game stochastic consensus problem and explicitly computed its unique solution in (Nourian et al.”
Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games (2019, methodology): “An alternative approach to studying large N -player game is mean-field game theory, introduced by Lasry and Lions (2006a,b, 2007) and by Huang et al. (2007, 2006) around the same time.”
Distributed Algorithms for Aggregative Games on Graphs (2016, background): “More recently, game-theoretic models have assumed relevance in the control of a large collection of coupled nonlinear systems, instances of which arise in production planning [15], synchronization of coupled oscillators [60], amongst others.”
Decentralized charging control for large populations of plug-in electric vehicles (2010, background): “[6], [11], [7] in the context of large scale dynamic games for sets of weakly coupled stochastic control systems.”
Efficiency of the price formation process in presence of high frequency participants: a mean field game analysis (2013, background): “Similar ideas have been introduced from an engineering viewpoint by [Huang et al., 2007] and [Adlakha et al., 2013].”
On non-uniqueness and uniqueness of solutions in finite-horizon Mean Field Games (2017, neutral): “The theory of MFGs started with the independent work of Lasry and Lions [39] and Huang, Caines, and Malhame [31, 33, 32].”