IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v13y2007i4d10.1007_s10588-007-9016-9.html
   My bibliography  Save this article

Simulating complex social behaviour with the genetic action tree kernel

Author

Listed:
  • Thorsten Chmura

    (Shanghai Jiao Tong University)

  • Johannes Kaiser

    (University of Bonn)

  • Thomas Pitz

    (Shanghai Jiao Tong University)

Abstract

The concept of genetic action trees combines action trees with genetic algorithms. In this paper, we create a multi-agent simulation on the base of this concept and provide the interested reader with a software package to apply genetic action trees in a multi-agent simulation to simulate complex social behaviour. An example model is introduced to conduct a feasibility study with the described method. We find that our library can be used to simulate the behaviour of agents in a complex setting and observe a convergence to a global optimum in spite of the absence of stable states.

Suggested Citation

  • Thorsten Chmura & Johannes Kaiser & Thomas Pitz, 2007. "Simulating complex social behaviour with the genetic action tree kernel," Computational and Mathematical Organization Theory, Springer, vol. 13(4), pages 355-377, December.
    • Handle: RePEc:spr:comaot:v:13:y:2007:i:4:d:10.1007_s10588-007-9016-9
    DOI: 10.1007/s10588-007-9016-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-007-9016-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-007-9016-9?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    2. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    3. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-411, May.
    4. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    5. Schelling, Thomas C, 1969. "Models of Segregation," American Economic Review, American Economic Association, vol. 59(2), pages 488-493, 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. Willemien Kets, 2007. "The minority game: An economics perspective," Papers 0706.4432, arXiv.org.
    2. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    3. Kets, W., 2008. "Networks and learning in game theory," Other publications TiSEM 7713fce1-3131-498c-8c6f-3, Tilburg University, School of Economics and Management.
    4. Ianni, A., 2002. "Reinforcement learning and the power law of practice: some analytical results," Discussion Paper Series In Economics And Econometrics 203, Economics Division, School of Social Sciences, University of Southampton.
    5. Sergiu Hart & Andreu Mas-Colell, 2013. "A Simple Adaptive Procedure Leading To Correlated Equilibrium," World Scientific Book Chapters, in: Simple Adaptive Strategies From Regret-Matching to Uncoupled Dynamics, chapter 2, pages 17-46, World Scientific Publishing Co. Pte. Ltd..
    6. Marco LiCalzi & Roland Mühlenbernd, 2022. "Feature-weighted categorized play across symmetric games," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 1052-1078, June.
    7. Osili, Una Okonkwo & Paulson, Anna, 2014. "Crises and confidence: Systemic banking crises and depositor behavior," Journal of Financial Economics, Elsevier, vol. 111(3), pages 646-660.
    8. Rottenstreich, Yuval & Kivetz, Ran, 2006. "On decision making without likelihood judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(1), pages 74-88, September.
    9. Ed Hopkins, 2002. "Two Competing Models of How People Learn in Games," Econometrica, Econometric Society, vol. 70(6), pages 2141-2166, November.
    10. Francisco Gomes & Michael Haliassos & Tarun Ramadorai, 2021. "Household Finance," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 919-1000, September.
    11. Simon P. Anderson & Jacob K. Goeree & Charles A. Holt, 2002. "The Logit Equilibrium: A Perspective on Intuitive Behavioral Anomalies," Southern Economic Journal, John Wiley & Sons, vol. 69(1), pages 21-47, July.
    12. Franke, Reiner, 2003. "Reinforcement learning in the El Farol model," Journal of Economic Behavior & Organization, Elsevier, vol. 51(3), pages 367-388, July.
    13. Arifovic, Jasmina & Karaivanov, Alexander, 2010. "Learning by doing vs. learning from others in a principal-agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1967-1992, October.
    14. David Goldbaum, 2009. "Follow the Leader: Steady State Analysis of a Dynamic Social Network," Working Paper Series 158, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    15. Dalton, Michael & Landry, Peter, 2020. "‘Overattention’ to first-hand experience in hiring decisions: Evidence from professional basketball," Journal of Economic Behavior & Organization, Elsevier, vol. 175(C), pages 98-113.
    16. Funai, Naoki, 2022. "Reinforcement learning with foregone payoff information in normal form games," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 638-660.
    17. C. Monica Capra & Jacob K Goeree & Rosario Gomez & Charles A Holt, 2002. "Learning and Noisy Equilibrium Behavior in an Experimental Study of Imperfect Price Competition," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(3), pages 613-636, August.
    18. Carlin, Bruce I. & Robinson, David T., 2009. "Fear and loathing in Las Vegas: Evidence from blackjack tables," Judgment and Decision Making, Cambridge University Press, vol. 4(5), pages 385-396, August.
    19. Schlag, Karl H. & Zapechelnyuk, Andriy, 2017. "Dynamic benchmark targeting," Journal of Economic Theory, Elsevier, vol. 169(C), pages 145-169.
    20. Kosfeld, Michael & Droste, Edward & Voorneveld, Mark, 2002. "A myopic adjustment process leading to best-reply matching," Games and Economic Behavior, Elsevier, vol. 40(2), pages 270-298, August.

    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:spr:comaot:v:13:y:2007:i:4:d:10.1007_s10588-007-9016-9. 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.