IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb373/200096.html
   My bibliography  Save this paper

Dumb software agents on an experimental asset market

Author

Listed:
  • Großklags, Jens
  • Schmidt, Carsten
  • Siegel, Jonathan

Abstract

We have analyzed the impact of agents and their trading strategies on an experimental electronic market. Therefore, we added an XML-interface to an existing electronic market and implemented artificial agents which acted as elements of disturbance in the trading process. These artificial traders applied simple and constant strategies which may sometimes appear to be rational or random to the eyes of other traders. We then stepped back and recorded the reaction of the electronic market.

Suggested Citation

  • Großklags, Jens & Schmidt, Carsten & Siegel, Jonathan, 2000. "Dumb software agents on an experimental asset market," SFB 373 Discussion Papers 2000,96, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:200096
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/62229/1/723877459.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sunder, S., 1992. "Lower Bounds for Efficiency of Surplus Extraction in Double Auctions," GSIA Working Papers 1992-17, Carnegie Mellon University, Tepper School of Business.
    2. Ken Binmore & Nir Vulkan, 1999. "Applying game theory to automated negotiation," Netnomics, Springer, vol. 1(1), pages 1-9, October.
    3. Smith, Vernon L, 1976. "Experimental Economics: Induced Value Theory," American Economic Review, American Economic Association, vol. 66(2), pages 274-279, May.
    4. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    5. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    6. Rust, John & Miller, John H. & Palmer, Richard, 1994. "Characterizing effective trading strategies : Insights from a computerized double auction tournament," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 61-96, January.
    7. Reinhard Selten & Michael Mitzkewitz & Gerald R. Uhlich, 1997. "Duopoly Strategies Programmed by Experienced Players," Econometrica, Econometric Society, vol. 65(3), pages 517-556, May.
    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. Carsten Schmidt & Jens Grossklags, 2004. "Interaction of Human and Artificial Agents on Double Auction Markets - Simulations and Laboratory Experiments," Papers on Strategic Interaction 2003-22, Max Planck Institute of Economics, Strategic Interaction Group.
    2. Sergiy Gerasymchuk, 2008. "Asset return and wealth dynamics with reference dependent preferences and heterogeneous beliefs," Working Papers 160, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    3. Valentyn Panchenko & Sergiy Gerasymchuk & Oleg V. Pavlov, 2007. "Asset price dynamics with small world interactions under hetereogeneous beliefs," Working Papers 149, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    4. Jens Grossklags & Carsten Schmidt, 2002. "Artificial Software Agents on Thin Double Auction Markets - A Human Trader Experiment," Papers on Strategic Interaction 2002-45, Max Planck Institute of Economics, Strategic Interaction Group.

    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. Jens Grossklags & Carsten Schmidt, 2002. "Artificial Software Agents on Thin Double Auction Markets - A Human Trader Experiment," Papers on Strategic Interaction 2002-45, Max Planck Institute of Economics, Strategic Interaction Group.
    2. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    3. 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.
    4. Carsten Schmidt & Jens Grossklags, 2004. "Interaction of Human and Artificial Agents on Double Auction Markets - Simulations and Laboratory Experiments," Papers on Strategic Interaction 2003-22, Max Planck Institute of Economics, Strategic Interaction Group.
    5. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    6. Itzhak Rasooly, 2022. "Competitive equilibrium and the double auction," Economics Series Working Papers 974, University of Oxford, Department of Economics.
    7. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    8. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    9. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    10. Arthur T. Denzau & Douglass C. North, 1994. "Shared Mental Models: Ideologies and Institutions," Kyklos, Wiley Blackwell, vol. 47(1), pages 3-31, February.
    11. James Nicolaisen & Valentin Petrov & Leigh Tesfatsion, 2000. "Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing," Computational Economics 0004005, University Library of Munich, Germany.
    12. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    13. Barbara Ikica & Simon Jantschgi & Heinrich H. Nax & Diego G. Nuñez Duran & Bary S. R. Pradelski, 2023. "Competitive Market Behavior: Convergence And Asymmetry In The Experimental Double Auction," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1087-1126, August.
    14. Tai, Chung-Ching & Chen, Shu-Heng & Yang, Lee-Xieng, 2018. "Cognitive ability and earnings performance: Evidence from double auction market experiments," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 409-440.
    15. David Rushing Dewhurst & Michael Vincent Arnold & Colin Michael Van Oort, 2018. "Selection mechanisms affect volatility in evolving markets," Papers 1812.05657, arXiv.org, revised Apr 2019.
    16. Söllner, Matthias, 2008. "Menschliches Verhalten in elektronischen Märkten," Bayreuth Reports on Information Systems Management 34, University of Bayreuth, Chair of Information Systems Management.
    17. Arvid Oskar Ivar Hoffmann & Wander Jager & J. H. Von Eije, 2007. "Social Simulation of Stock Markets: Taking It to the Next Level," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-7.
    18. List John A. & Millimet Daniel L, 2008. "The Market: Catalyst for Rationality and Filter of Irrationality," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 8(1), pages 1-55, November.
    19. Concetta Sorropago, 2014. "Behavioral Finance and Agent Based Model: the new evolving discipline of quantitative behavioral finance ?," DIAG Technical Reports 2014-13, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    20. Jason Shachat & Zhenxuan Zhang, 2017. "The Hayek Hypothesis and Long‐run Competitive Equilibrium: An Experimental Investigation," Economic Journal, Royal Economic Society, vol. 127(599), pages 199-228, February.

    More about this item

    Keywords

    XML; double auction; negotiation;
    All these keywords.

    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:zbw:sfb373:200096. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/sfhubde.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.