IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v9y2003i4d10.1023_bcmot.0000029086.73734.4e.html

Introduction: Learning in Dynamic, On-Line Environments

Author

Listed:
  • Faison P. Gibson

    (Michigan Business School)

Abstract

No abstract is available for this item.

Suggested Citation

  • Faison P. Gibson, 2003. "Introduction: Learning in Dynamic, On-Line Environments," Computational and Mathematical Organization Theory, Springer, vol. 9(4), pages 283-285, December.
    • Handle: RePEc:spr:comaot:v:9:y:2003:i:4:d:10.1023_b:cmot.0000029086.73734.4e
    DOI: 10.1023/B:CMOT.0000029086.73734.4e
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/B:CMOT.0000029086.73734.4e
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1023/B:CMOT.0000029086.73734.4e?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

    for a different version of it.

    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. 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.
    3. Sterman, John., 1994. "Learning in and about complex systems," Working papers 3660-94., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    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. Faison P. Gibson, 2007. "Learning and transfer in dynamic decision environments," Computational and Mathematical Organization Theory, Springer, vol. 13(1), pages 39-61, March.
    2. Faison P. Gibson, 2002. "Is It Better to Forget? Stimulus-Response, Prediction, and the Weight of Past Experience in a Fast-Paced Bargaining Task," Computational and Mathematical Organization Theory, Springer, vol. 8(1), pages 31-47, May.
    3. Philippe Jehiel & Aviman Satpathy, 2024. "Coarse Q-learning: Indifference, Indeterminacy, and Instability," Papers 2412.09321, arXiv.org, revised May 2026.
    4. Terry E. Daniel & Eyran J. Gisches & Amnon Rapoport, 2009. "Departure Times in Y-Shaped Traffic Networks with Multiple Bottlenecks," American Economic Review, American Economic Association, vol. 99(5), pages 2149-2176, December.
    5. 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.
    6. 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..
    7. 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.
    8. Ferraro Paul J & Vossler Christian A, 2010. "The Source and Significance of Confusion in Public Goods Experiments," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-42, July.
    9. Jason McKenzie Alexander & Brian Skyrms & Sandy Zabell, 2012. "Inventing New Signals," Dynamic Games and Applications, Springer, vol. 2(1), pages 129-145, March.
    10. Sueyoshi, Toshiyuki, 2010. "An agent-based approach equipped with game theory: Strategic collaboration among learning agents during a dynamic market change in the California electricity crisis," Energy Economics, Elsevier, vol. 32(5), pages 1009-1024, September.
    11. 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.
    12. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo & Möst, Dominik, 2007. "Agent-based simulation of electricity markets: a literature review," Working Papers "Sustainability and Innovation" S5/2007, Fraunhofer Institute for Systems and Innovation Research (ISI).
    13. Karl Schlag & Andriy Zapechelnyuk, 2009. "Decision Making in Uncertain and Changing Environments," Discussion Papers 19, Kyiv School of Economics.
    14. Khan, Mohammad Tariqul Islam & Tan, Siow-Hooi & Chong, Lee-Lee, 2017. "How past perceived portfolio returns affect financial behaviors—The underlying psychological mechanism," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1478-1488.
    15. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2019. "Algorithmic Pricing What Implications for Competition Policy?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 55(1), pages 155-171, August.
    16. 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.
    17. Keiki Takadama & Tetsuro Kawai & Yuhsuke Koyama, 2008. "Micro- and Macro-Level Validation in Agent-Based Simulation: Reproduction of Human-Like Behaviors and Thinking in a Sequential Bargaining Game," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-9.
    18. Francisco Gomes & Michael Haliassos & Tarun Ramadorai, 2021. "Household Finance," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 919-1000, September.
    19. Kun Wang & Lefeng Cheng & Meng Yin & Kuozhen Zhang & Ruikun Wang & Mengya Zhang & Runbao Sun, 2025. "Evolutionary Game Theory in Energy Storage Systems: A Systematic Review of Collaborative Decision-Making, Operational Strategies, and Coordination Mechanisms for Renewable Energy Integration," Sustainability, MDPI, vol. 17(16), pages 1-153, August.
    20. Funai Naoki, 2014. "An Adaptive Learning Model with Foregone Payoff Information," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 14(1), pages 149-176, January.

    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:spr:comaot:v:9:y:2003:i:4:d:10.1023_b:cmot.0000029086.73734.4e. 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.