IDEAS home Printed from https://ideas.repec.org/p/pit/wpaper/276.html
   My bibliography  Save this paper

Self-Organized Criticality in a Dynamic Game

Author

Listed:
  • John Duffy
  • Andreas Blume
  • Ted Temzelides

Abstract

We investigate conditions under which self-organized criticality (SOC) arises in a version of a dynamic entry game. In the simplest version of the game, there is a single location -- a pool -- and one agent is exogenously dropped into the pool every period. Payoffs to entrants are positive as long as the number of agents in the pool is below a critical level. If an agent chooses to exit, he cannot re-enter, resulting in a future payoff of zero. Agents in the pool decide simultaneously each period whether to stay in or not. We characterize the symmetric mixed strategy equilibrium of the resulting dynamic game. We then introduce local interactions between agents that occupy neighboring pools and demonstrate that, under our payoff structure, local interaction effects are necessary and sufficient for SOC and for an associated power law to emerge. Thus, we provide an explicit game-theoretic model of the mechanism through which SOC can arise in a social context with forward looking agents.

Suggested Citation

  • John Duffy & Andreas Blume & Ted Temzelides, 2006. "Self-Organized Criticality in a Dynamic Game," Working Paper 276, Department of Economics, University of Pittsburgh, revised Dec 2009.
  • Handle: RePEc:pit:wpaper:276
    as

    Download full text from publisher

    File URL: http://www.econ.pitt.edu/papers/John_JEDC5795.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arenas, Alex & Diaz-Guilera, Albert & Perez, Conrad J. & Vega-Redondo, Fernando, 2002. "Self-organized criticality in evolutionary systems with local interaction," Journal of Economic Dynamics and Control, Elsevier, vol. 26(12), pages 2115-2142, October.
    2. Eitan Altman & Nahum Shimkin, 1998. "Individual Equilibrium and Learning in Processor Sharing Systems," Operations Research, INFORMS, vol. 46(6), pages 776-784, December.
    3. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    4. Bak, Per & Chen, Kan & Scheinkman, Jose & Woodford, Michael, 1993. "Aggregate fluctuations from independent sectoral shocks: self-organized criticality in a model of production and inventory dynamics," Ricerche Economiche, Elsevier, vol. 47(1), pages 3-30, March.
    5. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-411, May.
    6. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    7. W. Brian Arthur, 1994. "Inductive Reasoning, Bounded Rationality and the Bar Problem," Working Papers 94-03-014, Santa Fe Institute.
    8. Andergassen, Rainer & Nardini, Franco & Ricottilli, Massimo, 2006. "Innovation waves, self-organized criticality and technological convergence," Journal of Economic Behavior & Organization, Elsevier, vol. 61(4), pages 710-728, December.
    9. Scheinkman, Jose A & Woodford, Michael, 1994. "Self-Organized Criticality and Economic Fluctuations," American Economic Review, American Economic Association, vol. 84(2), pages 417-421, May.
    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. Blume, Andreas & Duffy, John & Temzelides, Ted, 2010. "Self-organized criticality in a dynamic game," Journal of Economic Dynamics and Control, Elsevier, vol. 34(8), pages 1380-1391, August.
    2. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, October.
    3. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    4. Zhao, Zhijun & Zhang, Xiaoqi, 2022. "A continuous heterogeneous-agent model for the co-evolution of asset price and wealth distribution in financial market," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    5. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    6. Scott C. Linn & Nicholas S. P. Tay, 2007. "Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning," Management Science, INFORMS, vol. 53(7), pages 1165-1180, July.
    7. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
    8. Furtado, Bernardo Alves & Eberhardt, Isaque Daniel Rocha, 2015. "Modelo espacial simples da economia: uma proposta teórico-metodológica [A simple spatial economic model: a proposal]," MPRA Paper 67005, University Library of Munich, Germany.
    9. Bernardo Alves Furtado, 2022. "PolicySpace2: Modeling Markets and Endogenous Public Policies," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 25(1), pages 1-8.
    10. Andergassen, Rainer & Nardini, Franco & Ricottilli, Massimo, 2009. "Innovation and growth through local and global interaction," Journal of Economic Dynamics and Control, Elsevier, vol. 33(10), pages 1779-1795, October.
    11. Kristoufek, Ladislav & Vošvrda, Miloslav S., 2016. "Herding, minority game, market clearing and efficient markets in a simple spin model framework," FinMaP-Working Papers 68, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    12. Lucas Fievet & Didier Sornette, 2018. "Calibrating emergent phenomena in stock markets with agent based models," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    13. Rosser Jr., J. Barkley, 2007. "The rise and fall of catastrophe theory applications in economics: Was the baby thrown out with the bathwater?," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3255-3280, October.
    14. Fraiman, Daniel, 2022. "A self-organized criticality participative pricing mechanism for selling zero-marginal cost products," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    15. Matros, Alexander, 2012. "Altruistic versus egoistic behavior in a Public Good game," Journal of Economic Dynamics and Control, Elsevier, vol. 36(4), pages 642-656.
    16. Michael S. Harr'e, 2018. "Multi-agent Economics and the Emergence of Critical Markets," Papers 1809.01332, arXiv.org.
    17. Maria Minniti & William Bygrave, 2001. "A Dynamic Model of Entrepreneurial Learning," Entrepreneurship Theory and Practice, , vol. 25(3), pages 5-16, April.
    18. Reitz, Stefan & Rülke, Jan & Stadtmann, Georg, 2012. "Nonlinear Expectations in Speculative Markets," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62045, Verein für Socialpolitik / German Economic Association.
    19. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets – Evidence from the ECB survey of professional forecasters," Journal of Economic Dynamics and Control, Elsevier, vol. 36(9), pages 1349-1363.
    20. Westerhoff Frank H., 2008. "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 195-227, April.

    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:pit:wpaper:276. 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: Department of Economics, University of Pittsburgh (email available below). General contact details of provider: https://edirc.repec.org/data/depghus.html .

    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.