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Computational Laboratories for Organization Science: Questions, Validity and Docking

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  • Richard M. Burton

    (Duke University)

Abstract

A computational laboratory is a “place” where we can: ask a question about an organization and its processes, build a computational experiment, design and conduct an experiment, and answer or comment on the question. The questions can be: what is, what might be, and what should be. Validation is a fundamental concern in science; the validity of a laboratory and model depends upon the question being addressed. A laboratory for a descriptive what is question may not be valid for a what should be design question. Docking—the alignment of two models—goes beyond validity. Docking juxtaposes two models to investigate whether they proceed in like manner or yield similar results. I argue that docking provides a guide in the use of different laboratories to address organization questions; and, further computational and non computational models can be docked to deepen and broaden our understanding of organization science.

Suggested Citation

  • Richard M. Burton, 2003. "Computational Laboratories for Organization Science: Questions, Validity and Docking," Computational and Mathematical Organization Theory, Springer, vol. 9(2), pages 91-108, July.
  • Handle: RePEc:spr:comaot:v:9:y:2003:i:2:d:10.1023_b:cmot.0000022750.46976.3c
    DOI: 10.1023/B:CMOT.0000022750.46976.3c
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    Cited by:

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    5. Sasanka Sekhar Chanda, 2017. "Inferring final organizational outcomes from intermediate outcomes of exploration and exploitation: the complexity link," Computational and Mathematical Organization Theory, Springer, vol. 23(1), pages 61-93, March.
    6. Richard M. Burton & Børge Obel, 2011. "Computational Modeling for What-Is, What-Might-Be, and What-Should-Be Studies---And Triangulation," Organization Science, INFORMS, vol. 22(5), pages 1195-1202, October.
    7. Heiko Breitsohl, 2008. "Exploring Organizational Crises from a Legitimation Perspective - Results from a Computer Simulation and Illustrative Cases," Schumpeter Discussion Papers sdp08005, Universitätsbibliothek Wuppertal, University Library.
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    10. Chris P. Long & Sim B. Sitkin & Laura B. Cardinal & Richard M. Burton, 2015. "How controls influence organizational information processing: insights from a computational modeling investigation," Computational and Mathematical Organization Theory, Springer, vol. 21(4), pages 406-436, December.
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    13. Kent D. Miller & Shu-Jou Lin, 2010. "Different Truths in Different Worlds," Organization Science, INFORMS, vol. 21(1), pages 97-114, February.
    14. 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.
    15. Myong-Hun Chang & Joseph E. Harrington, 2007. "Innovators, Imitators, and the Evolving Architecture of Problem-Solving Networks," Organization Science, INFORMS, vol. 18(4), pages 648-666, August.
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    18. Klaus Wersching, 2010. "Schumpeterian Competition, Technological Regimes and Learning through Knowledge Spillover," Post-Print hal-00849408, HAL.
    19. repec:tur:wpapnw:8 is not listed on IDEAS
    20. Wersching, Klaus, 2010. "Schumpeterian competition, technological regimes and learning through knowledge spillover," Journal of Economic Behavior & Organization, Elsevier, vol. 75(3), pages 482-493, September.
    21. Sheen S. Levine & Michael J. Prietula & Ann Majchrzak, 2022. "Advice in Crisis: Principles of Organizational and Entrepreneurial Resilience," Journal of Organization Design, Springer;Organizational Design Community, vol. 11(4), pages 145-168, December.
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