IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1910.13534.html
   My bibliography  Save this paper

Microscopic Derivation of Mean Field Game Models

Author

Listed:
  • Martin Frank
  • Michael Herty
  • Torsten Trimborn

Abstract

Mean field game theory studies the behavior of a large number of interacting individuals in a game theoretic setting and has received a lot of attention in the past decade (Lasry and Lions, Japanese journal of mathematics, 2007). In this work, we derive mean field game partial differential equation systems from deterministic microscopic agent dynamics. The dynamics are given by a particular class of ordinary differential equations, for which an optimal strategy can be computed (Bressan, Milan Journal of Mathematics, 2011). We use the concept of Nash equilibria and apply the dynamic programming principle to derive the mean field limit equations and we study the scaling behavior of the system as the number of agents tends to infinity and find several mean field game limits. Especially we avoid in our derivation the notion of measure derivatives. Novel scales are motivated by an example of an agent-based financial market model.

Suggested Citation

  • Martin Frank & Michael Herty & Torsten Trimborn, 2019. "Microscopic Derivation of Mean Field Game Models," Papers 1910.13534, arXiv.org.
  • Handle: RePEc:arx:papers:1910.13534
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1910.13534
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jovanovic, Boyan & Rosenthal, Robert W., 1988. "Anonymous sequential games," Journal of Mathematical Economics, Elsevier, vol. 17(1), pages 77-87, February.
    2. Olivier Guéant & Pierre Louis Lions & Jean-Michel Lasry, 2011. "Mean Field Games and Applications," Post-Print hal-01393103, HAL.
    3. Levy, Haim & Levy, Moshe & Solomon, Sorin, 2000. "Microscopic Simulation of Financial Markets," Elsevier Monographs, Elsevier, edition 1, number 9780124458901.
    4. Torsten Trimborn & Lorenzo Pareschi & Martin Frank, 2017. "Portfolio Optimization and Model Predictive Control: A Kinetic Approach," Papers 1711.03291, arXiv.org, revised Feb 2019.
    5. P. Cardaliaguet, 2013. "Long Time Average of First Order Mean Field Games and Weak KAM Theory," Dynamic Games and Applications, Springer, vol. 3(4), pages 473-488, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Doruk Cetemen & Felix Zhiyu Feng & Can Urgun, 2019. "Contracting with Non-Exponential Discounting: Moral Hazard and Dynamic Inconsistency," Working Papers 2019-17, Princeton University. Economics Department..
    2. Trimborn, Torsten & Frank, Martin & Martin, Stephan, 2018. "Mean field limit of a behavioral financial market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 613-631.
    3. Minyi Huang, 2013. "A Mean Field Capital Accumulation Game with HARA Utility," Dynamic Games and Applications, Springer, vol. 3(4), pages 446-472, December.
    4. Piotr Więcek, 2020. "Discrete-Time Ergodic Mean-Field Games with Average Reward on Compact Spaces," Dynamic Games and Applications, Springer, vol. 10(1), pages 222-256, March.
    5. Olivier Gallay & Fariba Hashemi & Max-Olivier Hongler, 2019. "Imitation, Proximity, And Growth — A Collective Swarm Dynamics Approach," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-43, August.
    6. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.
    8. Light, Bar & Weintraub, Gabriel, 2018. "Mean Field Equilibrium: Uniqueness, Existence, and Comparative Statics," Research Papers 3731, Stanford University, Graduate School of Business.
    9. Lacker, Daniel, 2015. "Mean field games via controlled martingale problems: Existence of Markovian equilibria," Stochastic Processes and their Applications, Elsevier, vol. 125(7), pages 2856-2894.
    10. Tanya Araújo & Miguel St. Aubyn, 2008. "Education, Neighborhood Effects And Growth: An Agent-Based Model Approach," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 99-117.
    11. Camacho, Carmen & Kamihigashi, Takashi & Sağlam, Çağrı, 2018. "Robust comparative statics for non-monotone shocks in large aggregative games," Journal of Economic Theory, Elsevier, vol. 174(C), pages 288-299.
    12. Moshe Levy & Haim Levy, 2013. "Prospect Theory: Much Ado About Nothing?," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 7, pages 129-144, World Scientific Publishing Co. Pte. Ltd..
    13. A. Corcos & J-P Eckmann & A. Malaspinas & Y. Malevergne & D. Sornette, 2002. "Imitation and contrarian behaviour: hyperbolic bubbles, crashes and chaos," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 264-281.
    14. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    15. Mikhail Anufriev & Giulio Bottazzi, 2005. "Price and Wealth Dynamics in a Speculative Market with an Arbitrary Number of Generic Technical Traders," LEM Papers Series 2005/06, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    16. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    17. Maximilian Beikirch & Torsten Trimborn, 2020. "Novel Insights in the Levy-Levy-Solomon Agent-Based Economic Market Model," Papers 2002.10222, arXiv.org.
    18. Flavio Toxvaerd & Chryssi Giannitsarou, 2004. "Recursive global games," Money Macro and Finance (MMF) Research Group Conference 2003 104, Money Macro and Finance Research Group.
    19. Peter Richmond & Sorin Solomon, 2000. "Power Laws are Boltzmann Laws in Disguise," Papers cond-mat/0010222, arXiv.org.
    20. Anufriev, Mikhail & Bottazzi, Giulio & Marsili, Matteo & Pin, Paolo, 2012. "Excess covariance and dynamic instability in a multi-asset model," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1142-1161.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1910.13534. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.