IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/15942.html
   My bibliography  Save this paper

An Algorithm for the Simulation of Bounded Rational Agents

Author

Listed:
  • Schuster, Stephan

Abstract

Non-classical models of economic behaviour, usually summarised under the notion of 'Bounded Rationality' criticise the assumptions of the standard economic model - hyperrationality, perfect and costless information, and unlimited mental processing capabilities. However, alternative approaches have either remained very simple or purely descriptive. Here, a computational approach is presented based on Simon's concept of bounded rationality and satisficing as a compromise between the oversimplification of analytical and the descriptiveness of rich cognitive models.

Suggested Citation

  • Schuster, Stephan, 2009. "An Algorithm for the Simulation of Bounded Rational Agents," MPRA Paper 15942, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:15942
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/15942/1/MPRA_paper_15942.pdf
    File Function: original version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/19683/1/MPRA_paper_19683.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    3. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    4. Herbert Simon, 2000. "Bounded rationality in social science: Today and tomorrow," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 25-39, March.
    5. 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.
    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. Nicolas Marciales Parra, 2013. "A mathematical model for consumers based on aspiration adaptation theory and bounded rationality," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 5(1), pages 136-143, June.

    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. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.
    2. Francisco Gomes & Michael Haliassos & Tarun Ramadorai, 2021. "Household Finance," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 919-1000, September.
    3. Masiliūnas, Aidas, 2023. "Learning in rent-seeking contests with payoff risk and foregone payoff information," Games and Economic Behavior, Elsevier, vol. 140(C), pages 50-72.
    4. Shu-Heng Chen & Bin-Tzong Chie & Ying-Fang Kao & Ragupathy Venkatachalam, 2019. "Agent-Based Modeling of a Non-tâtonnement Process for the Scarf Economy: The Role of Learning," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 305-341, June.
    5. Yoella Bereby-Meyer & Alvin E. Roth, 2006. "The Speed of Learning in Noisy Games: Partial Reinforcement and the Sustainability of Cooperation," American Economic Review, American Economic Association, vol. 96(4), pages 1029-1042, September.
    6. Erev, Ido & Bereby-Meyer, Yoella & Roth, Alvin E., 1999. "The effect of adding a constant to all payoffs: experimental investigation, and implications for reinforcement learning models," Journal of Economic Behavior & Organization, Elsevier, vol. 39(1), pages 111-128, May.
    7. Tian, Ye & Chiu, Yi-Chang & Sun, Jian, 2019. "Understanding behavioral effects of tradable mobility credit scheme: An experimental economics approach," Transport Policy, Elsevier, vol. 81(C), pages 1-11.
    8. Sebastian J. Goerg & Tibor Neugebauer & Abdolkarim Sadrieh, 2016. "Impulse Response Dynamics in Weakest Link Games," German Economic Review, Verein für Socialpolitik, vol. 17(3), pages 284-297, August.
    9. James J. Choi & David Laibson & Brigitte C. Madrian & Andrew Metrick, 2009. "Reinforcement Learning and Savings Behavior," Journal of Finance, American Finance Association, vol. 64(6), pages 2515-2534, December.
    10. Fiore, Annamaria, 2009. "Experimental Economics: Some Methodological Notes," MPRA Paper 12498, University Library of Munich, Germany.
    11. Chmura, Thorsten & Goerg, Sebastian J. & Selten, Reinhard, 2012. "Learning in experimental 2×2 games," Games and Economic Behavior, Elsevier, vol. 76(1), pages 44-73.
    12. V. P. Crawford, 2014. "Boundedly rational versus optimization-based models of strategic thinking and learning in games," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 5.
    13. Laurent Denant-Boemont & Olivier L’Haridon, 2013. "La rationalité à l'épreuve de l'économie comportementale," Revue française d'économie, Presses de Sciences-Po, vol. 0(2), pages 35-89.
    14. James J. Choi & David Laibson & Brigitte C. Madrian & Andrew Metrick, 2009. "Reinforcement Learning and Savings Behavior," Journal of Finance, American Finance Association, vol. 64(6), pages 2515-2534, December.
    15. Albert Burgos, 2002. "Learning to deal with risk: what does reinforcement learning tell us about risk attitudes?," Economics Bulletin, AccessEcon, vol. 4(10), pages 1-13.
    16. Misha Perepelitsa, 2019. "RPS(1) Preferences," Papers 1901.04995, arXiv.org, revised Feb 2019.
    17. Yechiam, Eldad & Busemeyer, Jerome R., 2008. "Evaluating generalizability and parameter consistency in learning models," Games and Economic Behavior, Elsevier, vol. 63(1), pages 370-394, May.
    18. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    19. repec:ebl:ecbull:v:4:y:2002:i:10:p:1-13 is not listed on IDEAS
    20. Misha Perepelitsa, 2019. "A model of discrete choice based on reinforcement learning under short-term memory," Papers 1908.06133, arXiv.org.
    21. Hwang, Joon Ho & Kim, Min-Su, 2015. "Misunderstanding of the binomial distribution, market inefficiency, and learning behavior: Evidence from an exotic sports betting market," European Journal of Operational Research, Elsevier, vol. 243(1), pages 333-344.

    More about this item

    Keywords

    agent based modelling; bounded rationality; reinforcement learning; rule extraction;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    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:pra:mprapa:15942. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.