IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v17y2001i2-3p253-63.html
   My bibliography  Save this article

The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma

Author

Listed:
  • van Bragt, David
  • van Kemenade, Cees
  • la Poutre, Han

Abstract

Many economic and social systems are essentially large multi-agent systems. By means of computational modeling, the complicated behavior of such systems can be investigated. Modeling a multi-agent system as an evolutionary agent system, several important choices have to be made for evolutionary operators. Especially, it is to be expected that evolutionary dynamics substantially depend on the selection scheme. We therefore investigate the influence of evolutionary selection mechanisms on a fundamental problem: the iterated prisoner's dilemma (IPD), an elegant model for the emergence of cooperation in a multi-agent system. We observe various types of behavior, cooperation level, and stability, depending on the selection mechanism and the selection intensity. Hence, our results are important for (1) the proper choice and application of selection schemes when modeling real economic situations and(2) assessing the validity of the conclusions drawn from computer experiments with these models. We also conclude that the role of selection in the evolution of multi-agent systems should be investigated further, for instance using more detailed and complex agent interaction models. Copyright 2001 by Kluwer Academic Publishers

Suggested Citation

  • van Bragt, David & van Kemenade, Cees & la Poutre, Han, 2001. "The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma," Computational Economics, Springer;Society for Computational Economics, vol. 17(2-3), pages 253-263, June.
  • Handle: RePEc:kap:compec:v:17:y:2001:i:2-3:p:253-63
    as

    Download full text from publisher

    File URL: http://journals.kluweronline.com/issn/0927-7099/contents
    Download Restriction: Access to the full text of the articles in this series is restricted.
    ---><---

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ken Binmore & Nir Vulkan, 1999. "Applying game theory to automated negotiation," Netnomics, Springer, vol. 1(1), pages 1-9, October.
    2. Miller, John H., 1996. "The coevolution of automata in the repeated Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 29(1), pages 87-112, January.
    3. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    4. Ken Binmore & Nir Vulkan, "undated". "Applying Game Theory to Automated Negotiation," ELSE working papers 004, ESRC Centre on Economics Learning and Social Evolution.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2011. "Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 737-756, December.
    2. D.D.B. Bragt, van & J. A. La Poutr & E. H. Gerding, 2000. "Equilibrium Selection In Evolutionary Bargaining Models," Computing in Economics and Finance 2000 323, Society for Computational Economics.
    3. D.D.B. van Bragt & J.A. La Poutré, 2003. "Why Agents for Automated Negotiations Should Be Adaptive," Netnomics, Springer, vol. 5(2), pages 101-118, November.
    4. Waltman, L. & van Eck, N.J.P., 2009. "A Mathematical Analysis of the Long-run Behavior of Genetic Algorithms for Social Modeling," ERIM Report Series Research in Management ERS-2009-011-LIS, 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.
    5. E. J. Anderson & T. D. H. Cau, 2009. "Modeling Implicit Collusion Using Coevolution," Operations Research, INFORMS, vol. 57(2), pages 439-455, April.
    6. Alan G. Isaac, 2008. "Simulating Evolutionary Games: A Python-Based Introduction," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(3), pages 1-8.

    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. Enrico Gerding & David van Bragt & Han La Poutré, 2003. "Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions," Computational Economics, Springer;Society for Computational Economics, vol. 22(1), pages 39-63, August.
    2. David van Bragt & Han La Poutré, 2001. "Evolving Automata Play the Alternating-Offers Game," CeNDEF Workshop Papers, January 2001 2B.3, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    3. D.D.B. Bragt, van & J. A. La Poutr & E. H. Gerding, 2000. "Equilibrium Selection In Evolutionary Bargaining Models," Computing in Economics and Finance 2000 323, Society for Computational Economics.
    4. V. Kumar & Ashutosh Dixit & Rajshekar (Raj) G. Javalgi & Mayukh Dass, 2016. "Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 24-45, January.
    5. Fabian Lang & Andreas Fink, 2015. "Learning from the Metaheuristics: Protocols for Automated Negotiations," Group Decision and Negotiation, Springer, vol. 24(2), pages 299-332, March.
    6. Stan van Hoesel & Rudolf Müller, 2001. "Optimization in electronic markets: examples in combinatorial auctions," Netnomics, Springer, vol. 3(1), pages 23-33, June.
    7. repec:dgr:rugsom:99b41 is not listed on IDEAS
    8. Tomas Klos, 1999. "Governance and Matching," Computing in Economics and Finance 1999 341, Society for Computational Economics.
    9. Ivan Marsa-Maestre & Miguel A. Lopez-Carmona & Juan A. Carral & Guillermo Ibanez, 2013. "A Recursive Protocol for Negotiating Contracts Under Non-monotonic Preference Structures," Group Decision and Negotiation, Springer, vol. 22(1), pages 1-43, January.
    10. Usha Kiruthika & Thamarai Selvi Somasundaram & S. Kanaga Suba Raja, 2020. "Lifecycle Model of a Negotiation Agent: A Survey of Automated Negotiation Techniques," Group Decision and Negotiation, Springer, vol. 29(6), pages 1239-1262, December.
    11. Maria Minniti & William Bygrave, 2001. "A Dynamic Model of Entrepreneurial Learning," Entrepreneurship Theory and Practice, , vol. 25(3), pages 5-16, April.
    12. Rudolf Vetschera, 2003. "Experimentation and Learning in Repeated Cooperation," Computational and Mathematical Organization Theory, Springer, vol. 9(1), pages 37-60, May.
    13. Olivier Compte & Andrew Postlewaite, 2007. "Effecting Cooperation," PIER Working Paper Archive 09-019, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 29 May 2009.
    14. Barr, Jason & Saraceno, Francesco, 2009. "Organization, learning and cooperation," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 39-53, May.
    15. Marco Casari, 2002. "Can genetic algorithms explain experimental anomalies? An application to common property resources," UFAE and IAE Working Papers 542.02, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    16. Safarzynska, Karolina & van den Bergh, Jeroen C.J.M., 2011. "Beyond replicator dynamics: Innovation-selection dynamics and optimal diversity," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 229-245, May.
    17. Casari, Marco, 2008. "Markets in equilibrium with firms out of equilibrium: A simulation study," Journal of Economic Behavior & Organization, Elsevier, vol. 65(2), pages 261-276, February.
    18. Thomas Riechman, 2000. "A Model Of Boundedly Rational Consumer Choice," Computing in Economics and Finance 2000 321, Society for Computational Economics.
    19. Nobuyuki Hanaki, 2007. "Individual and Social Learning," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 421-421, May.
    20. Xavier Vilà, 2009. "A Model-to-Model Analysis of the Repeated Prisoners’ Dilemma: Genetic Algorithms vs. Evolutionary Dynamics," Lecture Notes in Economics and Mathematical Systems, in: Cesáreo Hernández & Marta Posada & Adolfo López-Paredes (ed.), Artificial Economics, chapter 0, pages 237-244, Springer.
    21. Tesfatsion, Leigh, 1998. "Teaching Agent-Based Computational Economics To Graduate Students," Economic Reports 18193, Iowa State University, Department of Economics.

    More about this item

    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:kap:compec:v:17:y:2001:i:2-3:p:253-63. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.