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A Machine Learning Method for Stackelberg Mean Field Games
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References

  • 1987Aggregation and Linearity in the Provision of Intertemporal Incentives2,870 cited
  • 2006Large population stochastic dynamic games: closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle1,629 cited
  • 2007Large-Population Cost-Coupled LQG Problems With Nonuniform Agents: Individual-Mass Behavior and Decentralized $\varepsilon$-Nash Equilibria1,092 cited
  • 2006Jeux à champ moyen. I – Le cas stationnaire983 cited
  • 2006Jeux à champ moyen. II – Horizon fini et contrôle optimal845 cited
  • 2013Mean Field Games and Mean Field Type Control Theory801 cited
  • 2008A Continuous-Time Version of the Principal–Agent Problem719 cited
  • 2018Probabilistic Theory of Mean Field Games with Applications II683 cited
  • 2018Probabilistic Theory of Mean Field Games with Applications I559 cited
  • 2010Mean Field Games: Numerical Methods359 cited
  • 2010Large-Population LQG Games Involving a Major Player: The Nash Certainty Equivalence Principle326 cited
  • 2012Mean Field Games: Numerical Methods for the Planning Problem206 cited
  • 2013$\epsilon$-Nash Mean Field Game Theory for Nonlinear Stochastic Dynamical Systems with Major and Minor Agents188 cited
  • 2015The Maximum Principle for Global Solutions of Stochastic Stackelberg Differential Games144 cited

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  • 2018Linear quadratic mean field Stackelberg differential games133 cited
  • 2017Dynamic programming approach to principal–agent problems126 cited
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