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

Author

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    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.

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