IDEAS home Printed from https://ideas.repec.org/a/bpj/bejtec/vcontributions.6y2006i1n14.html
   My bibliography  Save this article

Finite Memory Distributed Systems

Author

Listed:
  • Dorofeenko Victor

    (Institute for Advanced Studies, Vienna)

  • Shorish Jamsheed

    (Institute for Advanced Studies, Vienna)

Abstract

A distributed system model is studied, where individual agents play repeatedly against each other and change their strategies based upon previous play. It is shown how to model this environment in terms of continuous population densities of agent types. A complication arises because the population densities of different strategies depend upon each other not only through game payoffs, but also through the strategy distributions themselves. In spite of this, it is shown that when an agent imitates the strategy of his previous opponent at a sufficiently high rate, the system of equations which governs the dynamical evolution of agent populations can be reduced to one equation for the total population. In a sense, the dynamics 'collapse' to the dynamics of the entire system taken as a whole, which describes the behavior of all types of agents. We explore the implications of this model, and present both analytical and simulation results.

Suggested Citation

  • Dorofeenko Victor & Shorish Jamsheed, 2006. "Finite Memory Distributed Systems," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 6(1), pages 1-29, December.
    • Handle: RePEc:bpj:bejtec:v:contributions.6:y:2006:i:1:n:14
    DOI: 10.2202/1534-5971.1315
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1534-5971.1315
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1534-5971.1315?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sandholm, William H., 2003. "Evolution and equilibrium under inexact information," Games and Economic Behavior, Elsevier, vol. 44(2), pages 343-378, August.
    2. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    3. Jorgen W. Weibull, 1997. "Evolutionary Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262731215, December.
    4. William A. Brock & Steven N. Durlauf, 2002. "A Multinomial-Choice Model of Neighborhood Effects," American Economic Review, American Economic Association, vol. 92(2), pages 298-303, May.
    5. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    6. Dorofeenko, Victor & Shorish, Jamsheed, 2005. "Partial differential equation modelling for stochastic fixed strategy distributed systems," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 335-367, January.
    7. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, 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. John Sherwood & Anthony Ditta & Becky Haney & Loren Haarsma & Michael Carbajales-Dale, 2017. "Resource Criticality in Modern Economies: Agent-Based Model Demonstrates Vulnerabilities from Technological Interdependence," Biophysical Economics and Resource Quality, Springer, vol. 2(3), pages 1-22, September.
    2. Lucio Biggiero & Enrico Sevi, 2009. "Opportunism by cheating and its effects on industry profitability. The CIOPS model," Computational and Mathematical Organization Theory, Springer, vol. 15(3), pages 191-236, September.
    3. Zhangqi Zhong & Lingyun He, 2022. "Macro-Regional Economic Structural Change Driven by Micro-founded Technological Innovation Diffusion: An Agent-Based Computational Economic Modeling Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 471-525, February.
    4. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2015. "An agent based decentralized matching macroeconomic model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 305-332, October.
    5. Givanni Bonfani & Marco Villani, 2013. "Exaptation in innovation processes: theory and models," Chapters, in: Anna Grandori (ed.), Handbook of Economic Organization, chapter 10, Edward Elgar Publishing.
    6. Dirk Helbing & Thomas U. Grund, 2013. "Editorial: Agent-Based Modeling And Techno-Social Systems," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 16(04n05), pages 1-3.
    7. Matus Halas, 2018. "Balancing Against Threats In Interactions Determined By Distance And Overall Gains," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1-22, August.
    8. Cincotti, Silvano & Raberto, Marco & Teglio, Andrea, 2010. "Credit money and macroeconomic instability in the agent-based model and simulator Eurace," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-32.
    9. Waters, George A., 2009. "Chaos in the cobweb model with a new learning dynamic," Journal of Economic Dynamics and Control, Elsevier, vol. 33(6), pages 1201-1216, June.
    10. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    11. Filatova, Tatiana & Parker, Dawn C. & van der Veen, Anne, 2011. "The Implications of Skewed Risk Perception for a Dutch Coastal Land Market: Insights from an Agent-Based Computational Economics Model," Agricultural and Resource Economics Review, Cambridge University Press, vol. 40(3), pages 405-423, December.
    12. Witte, Björn-Christopher, 2012. "Fund managers - Why the best might be the worst: On the evolutionary vigor of risk-seeking behavior," Economics Discussion Papers 2012-20, Kiel Institute for the World Economy (IfW Kiel).
    13. Moulet, Sonia & Rouchier, Juliette, 2008. "The influence of seller learning and time constraints on sequential bargaining in an artificial perishable goods market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2322-2348, July.
    14. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    15. Scott Moss, 2007. "Alternative Approaches to the Empirical Validation of Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(1), pages 1-5.
    16. Gräbner, Claudius, 2016. "From realism to instrumentalism - and back? Methodological implications of changes in the epistemology of economics," MPRA Paper 71933, University Library of Munich, Germany.
    17. Witte, Björn-Christopher, 2011. "Fund managers - why the best might be the worst: On the evolutionary vigor of risk-seeking behavior," BERG Working Paper Series 81, Bamberg University, Bamberg Economic Research Group.
    18. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    19. Tatiana Filatova & Dawn C. Parker & Anne van der Veen, 2009. "Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-3.
    20. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

    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:bpj:bejtec:v:contributions.6:y:2006:i:1:n:14. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    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.