IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v18y2012i1d10.1007_s10588-011-9098-2.html
   My bibliography  Save this article

Effectively combining experimental economics and multi-agent simulation: suggestions for a procedural integration with an example from prediction markets research

Author

Listed:
  • Frank M. A. Klingert

    (Technische Universität Hamburg-Harburg)

  • Matthias Meyer

    (Technische Universität Hamburg-Harburg)

Abstract

This paper presents several ideas for combining experimental economics (EXP) with multi-agent simulation (MAS) more effectively. It argues that from an epistemological perspective a closer integration of both methods allows for a better use of their complementary advantages and can accelerate scientific progress. To realize this potential, we suggest an iterative, incremental procedural model as a framework for the collaboration between researchers. To further foster the integration, we recommend a higher level of documentation and standardization with respect to model and result description. An example from prediction markets research illustrates our methodological considerations. It can be shown how the suggested model and result documentations align research efforts and facilitate the transfer of results between EXP and MAS and how the procedural research model augments the scientific contributions of both methods.

Suggested Citation

  • Frank M. A. Klingert & Matthias Meyer, 2012. "Effectively combining experimental economics and multi-agent simulation: suggestions for a procedural integration with an example from prediction markets research," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 63-90, March.
    • Handle: RePEc:spr:comaot:v:18:y:2012:i:1:d:10.1007_s10588-011-9098-2
    DOI: 10.1007/s10588-011-9098-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-011-9098-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-011-9098-2?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Davis, Douglas D. & Holt, Charles a., 1993. "Experimental economics: Methods, problems and promise," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 8(2), pages 179-212.
    2. Daniel Houser & Michael Keane & Kevin McCabe, 2004. "Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm," Econometrica, Econometric Society, vol. 72(3), pages 781-822, May.
    3. Brian Heath & Raymond Hill & Frank Ciarallo, 2009. "A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-9.
    4. Matthias Meyer & Iris Lorscheid & Klaus G. Troitzsch, 2009. "The Development of Social Simulation as Reflected in the First Ten Years of JASSS: a Citation and Co-Citation Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-12.
    5. 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.
    6. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    7. Duffy, John, 2001. "Learning to speculate: Experiments with artificial and real agents," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 295-319, March.
    8. 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.
    9. Axelrod, Robert & Tesfatsion, Leigh, 2006. "A Guide for Newcomers to Agent-Based Modeling in the Social Sciences," Staff General Research Papers Archive 12515, Iowa State University, Department of Economics.
    10. Wolfers, Justin & Zitzewitz, Eric, 2006. "Five Open Questions About Prediction Markets," CEPR Discussion Papers 5562, C.E.P.R. Discussion Papers.
    11. Smith, Vernon L, 1982. "Microeconomic Systems as an Experimental Science," American Economic Review, American Economic Association, vol. 72(5), pages 923-955, December.
    12. Arianna Dal Forno & Ugo Merlone, 2004. "From Classroom Experiments to Computer Code," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(3), pages 1-2.
    13. Hanson, Robin & Oprea, Ryan & Porter, David, 2006. "Information aggregation and manipulation in an experimental market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(4), pages 449-459, August.
    14. Robin Hanson, 2007. "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 3-15, February.
    15. Riccardo Boero & Giangiacomo Bravo & Marco Castellani & Flaminio Squazzoni, 2010. "Why Bother with What Others Tell You? An Experimental Data-Driven Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(3), pages 1-6.
    16. Heiko Rauhut & Marcel Junker, 2009. "Punishment Deters Crime Because Humans Are Bounded in Their Strategic Decision-Making," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(3), pages 1-1.
    17. Vernon L. Smith, 1994. "Economics in the Laboratory," Journal of Economic Perspectives, American Economic Association, vol. 8(1), pages 113-131, Winter.
    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. Cary Deck & David Porter, 2013. "Prediction Markets In The Laboratory," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 589-603, July.
    2. Matthias Meyer & Klaus G. Troitzsch, 2012. "Epistemological perspectives on simulation: overview and introduction," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 1-4, March.
    3. Sina Hocke & Matthias Meyer & Iris Lorscheid, 2015. "Improving simulation model analysis and communication via design of experiment principles: an example from the simulation-based design of cost accounting systems," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 26(2), pages 131-155, August.
    4. Iris Lorscheid & Matthias Meyer, 2021. "Toward a better understanding of team decision processes: combining laboratory experiments with agent-based modeling," Journal of Business Economics, Springer, vol. 91(9), pages 1431-1467, November.
    5. Najib A. Mozahem, 2022. "Social cognitive theory and women’s career choices: an agent—based model simulation," Computational and Mathematical Organization Theory, Springer, vol. 28(1), pages 1-26, March.

    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. 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.
    2. Marco LiCalzi & Lucia Milone & Paolo Pellizzari, 2011. "Allocative Efficiency and Traders’ Protection Under Zero Intelligence Behavior," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 5-28, Springer.
    3. Baghestanian, Sascha & Walker, Todd B., 2015. "Anchoring in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 116(C), pages 15-25.
    4. 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.
    5. Patrick Buckley & Fergal O’Brien, 0. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-13.
    6. Olivier Brandouy & Angelo Corelli & Iryna Veryzhenko & Roger Waldeck, 2012. "A re-examination of the “zero is enough” hypothesis in the emergence of financial stylized facts," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 223-248, October.
    7. 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.
    8. 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.
    9. repec:wyi:journl:002151 is not listed on IDEAS
    10. Wayne Edwards & Lee Huskey, 2014. "The search goes on: Parameter effects on the return migration decision," Migration Letters, Migration Letters, vol. 11(1), pages 79-89, January.
    11. Edoardo Gaffeo & Mauro Gallegati & Umberto Gostoli, 2015. "An agent-based “proof of principle” for Walrasian macroeconomic theory," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 150-183, June.
    12. Jakob Grazzini, 2013. "Information dissemination in an experimentally based agent-based stock market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 179-209, April.
    13. Paola Tubaro, 2011. "Computational Economics," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 10, Edward Elgar Publishing.
    14. 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".
    15. Patrick Buckley & Fergal O’Brien, 2017. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 19(3), pages 611-623, June.
    16. Vincze, János & Varga, Gergely, 2016. "Megtakarítási típusok - egy adaptív-evolúciós megközelítés [Types of saving - an adaptive-evolutionary approach]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 162-187.
    17. Kluger, Brian D. & McBride, Mark E., 2011. "Intraday trading patterns in an intelligent autonomous agent-based stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 79(3), pages 226-245, August.
    18. Flåm, S.D. & Godal, O., 2008. "Market clearing and price formation," Journal of Economic Dynamics and Control, Elsevier, vol. 32(3), pages 956-977, March.
    19. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    20. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
    21. Edward Bishop Smith & William Rand, 2018. "Simulating Macro-Level Effects from Micro-Level Observations," Management Science, INFORMS, vol. 64(11), pages 5405-5421, November.

    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:18:y:2012:i:1:d:10.1007_s10588-011-9098-2. 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.