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Mean Field Equilibrium in Dynamic Games with Strategic Complementarities

Author

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  • Sachin Adlakha

    (Center for Mathematics of Information, California Institute of Technology, Pasadena, California 91125)

  • Ramesh Johari

    (Department of Management Science and Engineering, Stanford University, Stanford, California 94305)

Abstract

We study a class of stochastic dynamic games that exhibit strategic complementarities between players; formally, in the games we consider, the payoff of a player has increasing differences between her own state and the empirical distribution of the states of other players. Such games can be used to model a diverse set of applications, including network security models, recommender systems, and dynamic search in markets. Stochastic games are generally difficult to analyze, and these difficulties are only exacerbated when the number of players is large (as might be the case in the preceding examples).We consider an approximation methodology called mean field equilibrium to study these games. In such an equilibrium, each player reacts to only the long-run average state of other players. We find necessary conditions for the existence of a mean field equilibrium in such games. Furthermore, as a simple consequence of this existence theorem, we obtain several natural monotonicity properties. We show that there exist a “largest” and a “smallest” equilibrium among all those where the equilibrium strategy used by a player is nondecreasing, and we also show that players converge to each of these equilibria via natural myopic learning dynamics ; as we argue, these dynamics are more reasonable than the standard best-response dynamics. We also provide sensitivity results, where we quantify how the equilibria of such games move in response to changes in parameters of the game (for example, the introduction of incentives to players).

Suggested Citation

  • Sachin Adlakha & Ramesh Johari, 2013. "Mean Field Equilibrium in Dynamic Games with Strategic Complementarities," Operations Research, INFORMS, vol. 61(4), pages 971-989, August.
  • Handle: RePEc:inm:oropre:v:61:y:2013:i:4:p:971-989
    DOI: 10.1287/opre.2013.1192
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    Cited by:

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    2. Light, Bar & Weintraub, Gabriel, 2018. "Mean Field Equilibrium: Uniqueness, Existence, and Comparative Statics," Research Papers 3731, Stanford University, Graduate School of Business.
    3. Dianetti, Jodi & Ferrari, Giorgio & Fischer, Markus & Nendel, Max, 2019. "Submodular Mean Field Games. Existence and Approximation of Solutions," Center for Mathematical Economics Working Papers 621, Center for Mathematical Economics, Bielefeld University.
    4. Daniel Lacker & Kavita Ramanan, 2019. "Rare Nash Equilibria and the Price of Anarchy in Large Static Games," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 400-422, May.
    5. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2021. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Dynamic Games and Applications, Springer, vol. 11(3), pages 463-490, September.
    6. Harsha Honnappa & Rahul Jain, 2015. "Strategic Arrivals into Queueing Networks: The Network Concert Queueing Game," Operations Research, INFORMS, vol. 63(1), pages 247-259, February.
    7. Jian Yang & Yusen Xia & Xiangtong Qi & Yifeng Liu, 2014. "A nonatomic‐game model for timing clearance sales under competition," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(5), pages 365-385, August.
    8. Krishnamurthy Iyer & Ramesh Johari & Mukund Sundararajan, 2014. "Mean Field Equilibria of Dynamic Auctions with Learning," Management Science, INFORMS, vol. 60(12), pages 2949-2970, December.
    9. Manxi Wu & Saurabh Amin & Asuman Ozdaglar, 2021. "Multi-agent Bayesian Learning with Best Response Dynamics: Convergence and Stability," Papers 2109.00719, arXiv.org.
    10. Piotr Więcek, 2017. "Total Reward Semi-Markov Mean-Field Games with Complementarity Properties," Dynamic Games and Applications, Springer, vol. 7(3), pages 507-529, September.
    11. Piotr Więcek & Eitan Altman, 2015. "Stationary Anonymous Sequential Games with Undiscounted Rewards," Journal of Optimization Theory and Applications, Springer, vol. 166(2), pages 686-710, August.
    12. Jian Yang, 2017. "A link between sequential semi-anonymous nonatomic games and their large finite counterparts," International Journal of Game Theory, Springer;Game Theory Society, vol. 46(2), pages 383-433, May.
    13. Dario Bauso & Raffaele Pesenti & Marco Tolotti, 2016. "Opinion Dynamics and Stubbornness Via Multi-Population Mean-Field Games," Journal of Optimization Theory and Applications, Springer, vol. 170(1), pages 266-293, July.
    14. Jian Yang, 2021. "Analysis of Markovian Competitive Situations Using Nonatomic Games," Dynamic Games and Applications, Springer, vol. 11(1), pages 184-216, March.

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