IDEAS home Printed from https://ideas.repec.org/h/eee/hecchp/2-27.html
   My bibliography  Save this book chapter

Market Design Using Agent-Based Models

In: Handbook of Computational Economics

Author

Listed:
  • Marks, Robert

Abstract

This chapter explores the state of the emerging practice of designing markets by the use of agent-based modeling, with special reference to electricity markets and computerized (on-line) markets, perhaps including real-life electronic agents as well as human traders. The paper first reviews the use of evolutionary and agent-based techniques of analyzing market behaviors and market mechanisms, and economic models of learning, comparing genetic algorithms with reinforcement learning. Ideal design would be direct optimization of an objective function, but in practice the complexity of markets and traders' behavior prevents this, except in special circumstances. Instead, iterative analysis, subject to design criteria trade-offs, using autonomous self-interested agents, mimics the bottom-up evolution of historical market mechanisms by trial and error. The chapter highlights ten papers that exemplify recent progress in agent-based evolutionary analysis and design of markets in silico, using electricity markets and on-line double auctions as illustrations. A monopoly sealed-bid auction is examined in the tenth paper, and a new auction mechanism is evolved and analyzed. The chapter concludes that, as modeling the learning and behavior of traders improves, and as the software and hardware available for modeling and analysis improves, the techniques will provide ever greater insights into improving the designs of existing markets, and facilitating the design of new markets.

Suggested Citation

  • Marks, Robert, 2006. "Market Design Using Agent-Based Models," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 27, pages 1339-1380, Elsevier.
  • Handle: RePEc:eee:hecchp:2-27
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/B7P5C-4JR414P-J/2/58fb3f93605ffbb99d958140cc845589
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    Citations

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


    Cited by:

    1. John J. Nay & Yevgeniy Vorobeychik, 2016. "Predicting Human Cooperation," Papers 1601.07792, arXiv.org, revised Apr 2016.
    2. Rich, Karl M. & Ross, R. Brent & Baker, A. Derek & Negassa, Asfaw, 2011. "Quantifying value chain analysis in the context of livestock systems in developing countries," Food Policy, Elsevier, vol. 36(2), pages 214-222, April.
    3. Filatova, Tatiana & Parker, Dawn C. & van der Veen, Anne, 2011. "The Implications of Skewed Risk Perception for a Dutch Coastal Land Market: Insights from an Agent-Based Computational Economics Model," Agricultural and Resource Economics Review, Cambridge University Press, vol. 40(3), pages 405-423, December.
    4. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Springer;Society for Computational Economics, vol. 33(1), pages 47-78, February.
    5. Robert Marks, 2007. "Validating Simulation Models: A General Framework and Four Applied Examples," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 265-290, October.
    6. Albert Banal-Estañol & Augusto Rupérez-Micola, 2010. "Are agent-based simulations robust? The wholesale electricity trading case," Economics Working Papers 1214, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Banal-Estañol, Albert & Rupérez Micola, Augusto, 2011. "Behavioural simulations in spot electricity markets," European Journal of Operational Research, Elsevier, vol. 214(1), pages 147-159, October.
    8. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.
    9. Block, C. & Collins, J. & Ketter, W. & Weinhardt, C., 2009. "A Multi-Agent Energy Trading Competition," ERIM Report Series Research in Management ERS-2009-054-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.
    10. John J Nay & Yevgeniy Vorobeychik, 2016. "Predicting Human Cooperation," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
    11. Lin-Ju Chen & Lei Zhu & Ying Fan & Sheng-Hua Cai, 2013. "Long-Term Impacts of Carbon Tax and Feed-in Tariff Policies on China's Generating Portfolio and Carbon Emissions: A Multi-Agent-Based Analysis," Energy & Environment, , vol. 24(7-8), pages 1271-1293, December.
    12. Herbert Dawid & Joern Dermietzel, 2006. "How Robust is the Equal Split Norm? Responsive Strategies, Selection Mechanisms and the Need for Economic Interpretation of Simulation Parameters," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 371-397, November.
    13. Crow, Daniel J.G. & Giarola, Sara & Hawkes, Adam D., 2018. "A dynamic model of global natural gas supply," Applied Energy, Elsevier, vol. 218(C), pages 452-469.
    14. Rich, Karl M. & Baker, Derek & Negassa, Asfaw & Ross, R. Brent, 2009. "Concepts, applications, and extensions of value chain analysis to livestock systems in developing countries," 2009 Conference, August 16-22, 2009, Beijing, China 51922, International Association of Agricultural Economists.
    15. Adrien Querbes, 2018. "Banned from the sharing economy: an agent-based model of a peer-to-peer marketplace for consumer goods and services," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 633-665, August.

    More about this item

    JEL classification:

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

    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:eee:hecchp:2-27. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description .

    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.