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Biased Judgment in Censored Environments

Author

Listed:
  • Daniel C. Feiler

    () (Tuck School of Business, Dartmouth College, Hanover, New Hampshire 03755)

  • Jordan D. Tong

    () (Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin 53706)

  • Richard P. Larrick

    () (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Abstract

Some environments constrain the information that managers and decision makers can observe. We examine judgment in censored environments where a constraint, the censorship point , systematically distorts the observed sample. Random instances beyond the censorship point are observed at the censorship point, whereas uncensored instances are observed at their true value. Many important managerial decisions occur in censored environments, such as inventory, risk taking, and employee evaluation decisions. In this research, we demonstrate a censorship bias --individuals tend to rely too heavily on the observed censored sample, biasing their belief about the underlying population. We further show that the censorship bias is exacerbated for higher degrees of censorship, higher variance in the population, and higher variability in the censorship points. In four studies, we find evidence of the censorship bias across the domains of demand estimation and sequential risk taking. The bias causes individuals to make costly decisions and behave in an overly risk-averse manner. This paper was accepted by Teck Ho, judgment and decision making.

Suggested Citation

  • Daniel C. Feiler & Jordan D. Tong & Richard P. Larrick, 2013. "Biased Judgment in Censored Environments," Management Science, INFORMS, vol. 59(3), pages 573-591, January.
  • Handle: RePEc:inm:ormnsc:v:59:y:2013:i:3:p:573-591
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    File URL: http://dx.doi.org/10.1287/mnsc.1120.1612
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhao, Yingshuai & Zhao, Xiaobo, 2015. "On human decision behavior in multi-echelon inventory management," International Journal of Production Economics, Elsevier, vol. 161(C), pages 116-128.
    2. Angelovski, Andrej & Brandts, Jordi & Sola, Carles, 2016. "Hiring and escalation bias in subjective performance evaluations: A laboratory experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 121(C), pages 114-129.
    3. Yingshuai Zhao & Xiaobo Zhao & Zuo-Jun Max Shen, 2016. "On learning process of a newsvendor with censored demand information," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1200-1211, September.
    4. John S. Jatta & Krishna Kumar Krishnan, 2016. "An empirical assessment of a univariate time series for demand planning in a demand-driven supply chain," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(3), pages 269-290.

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