A reference case for mean field games models

By Olivier Guéant. 2009

105 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8cac81fa14df8857722798debd341765cf13b06c

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Citations in the corpus

Mean Field Games Models—A Brief Survey (2013, background): “Various applications and additional models have been worked out in detail in [7, 10, 41, 53, 54, 67, 75, 79, 81, 84, 86, 87] (see also the special edition on Netw.”
Stationary focusing mean-field games (2016, background): “As far as we know, this setting has not yet been expolored systematically; a study in the quadratic and linear-quadratic case has been carried out in [2, 20, 21].”
Time dependent mean-field games in the superquadratic case (2013, methodology): “These include numerical methods [2, 3, 36], applications in economics [32, 41] and environmental policy [36], finite state problems [17, 27, 28], explicit models [33, 45], obstacle-type problems [19], congestion [18, 25], extended mean-field games [24, 29], probabilistic methods [13, 14], long-time ”
Learning Deep Mean Field Games for Modeling Large Population Behavior (2017, methodology): “(Guéant, 2009; Lachapelle et al., 2010; Bauso et al., 2016), we learn a reward function using demonstration trajectories measured from actual population behavior, to ground the MFG representation of population dynamics on real data.”
Mean Field Games and Applications (2011, background): “Numerically, this is very interesting and the eductive methods give very good results, both for finding stationary equilibrium and for generalizing the approach for seeking dynamic equilibrium (see [23]).”
Optimal stopping in mean field games, an obstacle problem approach (2017, background): “In the past decade, MFG have been broadly studied since their introduction by Lasry and Lions in their series of paper [17,18,19] and it has been shown they have lots of applications [14,15].”
Existence of a solution to an equation arising from the theory of Mean Field Games (2015, methodology): “This manuscript version is made available under the Elsevier user license http://www.elsevier.com/open-access/userlicense/1.0/”
Mean Field Analysis of Controlled Cucker-Smale Type Flocking: Linear Analysis and Perturbation Equations (2011, methodology): “, linearized) equations of the nonlinear MF system of equations around the Gaussian solution of the linear cost coupling case (based on the approach developed in (Guéant, 2009; Nourian et al., 2011b)).”
Time-Dependent Mean-Field Games in the Subquadratic Case (2013, background): “Time-Dependent Mean-Field Games 41 [32, 44] and environmental policy [38], finite state problems [19, 23, 24], explicit models [33, 48], obstacle-type problems [25], extended mean-field games [26, 30], probabilistic methods [13, 14], long-time behavior [10, 12], weak solutions to meanfield games [11”
On non-uniqueness and uniqueness of solutions in finite-horizon Mean Field Games (2017, unknown): “For stationary MFG with ergodic cost functional, examples of non-uniqueness are known since the pioneering paper of Lasry and Lions [39], and others more explicit appear in [4, 7, 25, 29].”
Linear-Quadratic Mean Field Games (2014, background): “(1) For problems over infinite-time horizon, [12] and [13] studied ergodic MFGs with different quadratic cost functionals and linear dynamics; [14] studied a specific MFG with a quadratic Hamiltonian and showed that the density function for the population is Gaussian; [7,15–17] considered MFGs with ”
Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations (2017, methodology): “In contrast to previous MFG research, most of which impose reward functions that are quadratic in actions and logarithmic in the state distribution (Gu´eant, 2009; Lachapelle et al. , 2010; Bauso et al. , 2016), we learn a reward function using demonstration trajectories measured from actual…”
Mean Field Games (2020, background): “The mean field games theory seem also paticularly adapted to modelize problems in economics: see Guéant [28], [29], Lachapelle [39], Lasry, Lions, Guéant [46], and the references therein.”