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Causality in the context of analytical models and numerical experiments

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  • Balakrishnan, Ramji
  • Penno, Mark

Abstract

Intuition tends to guide model formulation, as it is generally impossible to consider all dimensions of a problem. The ability to surprise, heightening the focus on paradox and the contradiction of reality, is therefore more useful than a literal representation of reality. While numerical experiments are useful in exploring patterns not well suited to analytic approaches, features of the model that underlies the experiment determines the experiments’ ability to provide insight and offer surprise.

Suggested Citation

  • Balakrishnan, Ramji & Penno, Mark, 2014. "Causality in the context of analytical models and numerical experiments," Accounting, Organizations and Society, Elsevier, vol. 39(7), pages 531-534.
    • Handle: RePEc:eee:aosoci:v:39:y:2014:i:7:p:531-534
    DOI: 10.1016/j.aos.2013.09.004
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    References listed on IDEAS

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    Cited by:

    1. Gassen, Joachim, 2014. "Causal inference in empirical archival financial accounting research," Accounting, Organizations and Society, Elsevier, vol. 39(7), pages 535-544.
    2. 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.
    3. Eva Labro, 2015. "Using simulation methods in accounting research," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 26(2), pages 99-104, August.

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