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Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments

Author

Listed:
  • Iris Lorscheid

    (Hamburg University of Technology)

  • Bernd-Oliver Heine

    (Hamburg University of Technology)

  • Matthias Meyer

    (Hamburg University of Technology)

Abstract

Many still view simulation models as a black box. This paper argues that perceptions could change if the systematic design of experiments (DOE) for simulation research was fully realized. DOE can increase (1) the transparency of simulation model behavior and (2) the effectiveness of reporting simulation results. Based on DOE principles, we develop a systematic procedure to guide the analysis of simulation models as well as concrete templates for sharing the results. A simulation model investigating the performance of learning algorithms in an economic mechanism design context illustrates our suggestions. Overall, the proposed systematic procedure for applying DOE principles complements current initiatives for a more standardized simulation research process.

Suggested Citation

  • Iris Lorscheid & Bernd-Oliver Heine & Matthias Meyer, 2012. "Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 22-62, March.
    • Handle: RePEc:spr:comaot:v:18:y:2012:i:1:d:10.1007_s10588-011-9097-3
    DOI: 10.1007/s10588-011-9097-3
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    References listed on IDEAS

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    3. Apetrei, Cristina I. & Strelkovskii, Nikita & Khabarov, Nikolay & Javalera Rincón, Valeria, 2024. "Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning," Ecological Modelling, Elsevier, vol. 489(C).
    4. Dario Blanco-Fernandez & Stephan Leitner & Alexandra Rausch, 2022. "Interactions between the individual and the group level in organizations: The case of learning and autonomous group adaptation," Papers 2203.09162, arXiv.org.
    5. Patrick Afflerbach & Christopher Dun & Henner Gimpel & Dominik Parak & Johannes Seyfried, 2021. "A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 329-348, August.
    6. Emanuele Borgonovo & Marco Pangallo & Jan Rivkin & Leonardo Rizzo & Nicolaj Siggelkow, 2022. "Sensitivity analysis of agent-based models: a new protocol," Computational and Mathematical Organization Theory, Springer, vol. 28(1), pages 52-94, March.
    7. Leitner, Stephan & Wall, Friederike, 2022. "Micro-level dynamics in hidden action situations with limited information," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 372-393.
    8. Priscilla Avegliano & Jaime Simão Sichman, 2023. "Equation-Based Versus Agent-Based Models: Why Not Embrace Both for an Efficient Parameter Calibration?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(4), pages 1-3.
    9. Davis, Natalie & Jarvis, Andrew & Polhill, J. Gareth, 2022. "Co-evolution of network structure and consumer inequality in a spatially explicit model of energetic resource acquisition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    10. Philippe Collard, 2022. "The “flat peer learning” agent-based model," Journal of Computational Social Science, Springer, vol. 5(1), pages 161-187, May.
    11. 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.

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