Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games

By Gabriel Y. Weintraub, C. Lanier Benkard, Benjamin Van Roy. 2005

120 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jun Wang
2. Minyi Huang
3. Yaodong Yang
4. Weinan Zhang
5. Tamer Basar
6. M. Zhou
7. Quanyan Zhu
8. H. Tembine

Citations in the corpus

• Mean Field Multi-Agent Reinforcement Learning (2018, background): “MFG studies population behaviors resulting from the aggregations of decisions taken from individuals.”
• Risk-sensitive mean field stochastic differential games (2011, background): “…controls Huang et al. (2003, 2007); Yin et al. (2010), mean-field optimization, mean-field teams Tembine et al. (2009), mean-field stochastic games Weintraub et al. (2005); Adlakha et al. (2008); Tembine et al. (2009); Tembine (2011), mean-field stochastic differential games Lasry and Lions…”
• Large-Population LQG Games Involving a Major Player: The Nash Certainty Equivalence Principle (2009, background): “For models with many firm dynamics, Weintraub, Benkard, and Von Roy considered decentralized strategies and proposed the notion of oblivious equilibria via a mean field approximation [35, 36].”