Organizations expect to see consistency in the decisions of their
employees, but humans are unreliable. Judgments can vary a great deal
from one individual to the next, even when people are in the same role
and supposedly following the same guidelines. And irrelevant factors,
such as mood and the weather, can change one person’s decisions from
occasion to occasion. This chance variability of decisions is called
noise, and it is surprisingly costly to companies, which are usually
completely unaware of it. Nobel laureate Daniel Kahneman, a professor of
psychology at Princeton, and Andrew M. Rosenfield, Linnea Gandhi, and
Tom Blaser of TGG Group explain how organizations can perform a noise
audit by having members of a professional unit evaluate a common set of
cases. The degree to which their assessments vary provides the measure
of noise. If the problem is severe, firms can pursue a number of
remedies. The most radical is to replace human judgment with algorithms.
Unlike people, algorithms always return the same output for any given
input, and research shows that their predictions and decisions are often
more accurate than those made by experts. Although algorithms may seem
daunting to construct, the authors describe how to build them with input
data on a small number of cases and some simple commonsense rules. But
if applying formulas is politically or operationally infeasible,
companies can still set up procedures and practices that will guide
employees to make more-consistent decisions. INSETS: Types of Noise and
Bias.;How to Build a Reasoned Rule.. [ABSTRACT FROM AUTHOR]