IDEAS home Printed from https://ideas.repec.org/p/esi/evopap/2004-16.html
   My bibliography  Save this paper

Agent Learning Representation - Advice in Modelling Economic Learning

Author

Listed:
  • Thomas Brenner

Abstract

This paper presents an overview on the existing learning models in the economic literature. Furthermore, it discusses which of these models should be used under what circumstances and how adequate learning models can be chosen in simulation approaches. It gives advice for getting along with the many models existing and picking the right one for the own application.

Suggested Citation

  • Thomas Brenner, 2004. "Agent Learning Representation - Advice in Modelling Economic Learning," Papers on Economics and Evolution 2004-16, Philipps University Marburg, Department of Geography.
    • Handle: RePEc:esi:evopap:2004-16
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    2. Jasmina Arifovic & John Ledyard, 2004. "Scaling Up Learning Models in Public Good Games," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 6(2), pages 203-238, 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. repec:zbw:iamodp:109915 is not listed on IDEAS
    2. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    3. 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.
    4. Graubner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 109915, Institute of Agricultural Development in Transition Economies (IAMO).
    5. Graupner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM) [Das räumliche agenten-basierte Wettbewerbsmodell SpAbCoM]," IAMO Discussion Papers 135, Leibniz Institute of Agricultural Development in Transition Economies (IAMO).
    6. Kimbrough, Erik O., 2011. "Heuristic learning and the discovery of specialization and exchange," Journal of Economic Dynamics and Control, Elsevier, vol. 35(4), pages 491-511, April.
    7. Arifovic, Jasmina & Ledyard, John, 2011. "A behavioral model for mechanism design: Individual evolutionary learning," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 374-395, May.
    8. Shu-Heng Chen & Chung-Ching Tai, 2006. "Republication: On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 313-331, November.
    9. 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.
    10. Shu-Heng Chen & Chung-Ching Tai, 2006. "On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 51-69, August.
    11. Anufriev, Mikhail & Arifovic, Jasmina & Ledyard, John & Panchenko, Valentyn, 2022. "The role of information in a continuous double auction: An experiment and learning model," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    12. Jasmina Arifovic & Michael Maschek, 2006. "Revisiting Individual Evolutionary Learning in the Cobweb Model – An Illustration of the Virtual Spite-Effect," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 333-354, November.
    13. Dietrichson, Jens, 2013. "Coordination Incentives, Performance Measurement and Resource Allocation in Public Sector Organizations," Working Papers 2013:26, Lund University, Department of Economics.
    14. Meissner, Thomas & Rostam-Afschar, Davud, 2017. "Learning Ricardian Equivalence," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 273-288.
    15. Gomes, Orlando, 2009. "Stability under learning: The endogenous growth problem," Economic Modelling, Elsevier, vol. 26(5), pages 807-816, September.
    16. Philippe Aghion & Ernst Fehr & Richard Holden & Tom Wilkening, 2018. "The Role of Bounded Rationality and Imperfect Information in Subgame Perfect Implementation—An Empirical Investigation," Journal of the European Economic Association, European Economic Association, vol. 16(1), pages 232-274.
    17. Bullard, James & Duffy, John, 1999. "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 13(1), pages 41-60, February.
    18. Kellermann, Konrad & Balmann, Alfons, 2006. "How Smart Should Farms Be Modeled? Behavioral Foundation of Bidding Strategies in Agent-Based Land Market Models," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25446, International Association of Agricultural Economists.
    19. Ernst Fehr & Michael Powell & Tom Wilkening, 2014. "Handing Out Guns at a Knife Fight: Behavioral Limitations of Subgame-Perfect Implementation," CESifo Working Paper Series 4948, CESifo.
    20. Arifovic, Jasmina & Yıldızoğlu, Murat, 2019. "Learning the Ramsey outcome in a Kydland & Prescott economy," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 191-208.
    21. Michael Maschek, 2010. "Intelligent Mutation Rate Control in an Economic Application of Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 35(1), pages 25-49, January.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:esi:evopap:2004-16. 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: Christoph Mengs (email available below). General contact details of provider: https://edirc.repec.org/data/vamarde.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.