Biased Judgment in Censored Environments

Daniel C. Feiler (), Jordan D. Tong () and Richard P. Larrick ()
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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

Management Science, 2013, vol. 59, issue 3, 573-591

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.

Keywords: inference; heuristics and biases; demand estimation; inventory decisions; risky choice; learning (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (19)

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