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Beyond Gaussian Averages: Redirecting Management Research Toward Extreme Events and Power Laws

Author

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  • Pierpaolo Andriani

    (Durham Business School)

  • Bill McKelvey

    (Anderson School of Management, UCLA)

Abstract

Practicing managers live in a world of ‘extremes’ but management research is based on Gaussian statistics that rule out those extremes. On occasion, deviation amplifying mutual causal processes among interdependent data points cause extreme events characterized by power laws. They seem ubiquitous; we list 80 kinds of them – half each among natural and social phenomena. We draw a ‘line in the sand’ between Gaussian (based on independent data points, finite variance and emphasizing averages) and Paretian statistics (based on interdependence, positive feedback, infinite variance, and emphasizing extremes). Quantitative journal publication depends almost entirely on Gaussian statistics. We draw on complexity and earthquake sciences to propose redirecting Management Studies. No statistical findings should be accepted into Management Studies if they gain significance via some assumption-device by which extreme events and infinite variance are ignored. The cost is inaccurate science and irrelevance to practitioners.

Suggested Citation

  • Pierpaolo Andriani & Bill McKelvey, 2006. "Beyond Gaussian Averages: Redirecting Management Research Toward Extreme Events and Power Laws," Working Papers 2006_03, Durham University Business School.
    • Handle: RePEc:dur:durham:2006_03
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    File URL: http://dro.dur.ac.uk/10371
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    References listed on IDEAS

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    Cited by:

    1. Yang, Jianmei & Wang, Wenjie & Chen, Guanrong, 2009. "A two-level complex network model and its application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2435-2449.

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