"We have too much emphasis on bias and not enough emphasis on random noise"
- Dan Kahneman Speaking at the Kahneman-Treisman Center for Behavioral Science and Public Policy
There has been extensive discussions about behavioral biases in decision-making. The academic and popular articles on the bias topic are endless and there is a huge cottage industry of finding and cataloguing biases, but are biases the core enemy of good decision-making?
Perhaps the bigger issue is noise - the random errors that create decision risk and uncertainty. The spread and inconsistency of noise can actual be more harmful with making good decision. Noisy decision errors are pervasive, but can be reduced. This is the conclusion of Dan Kahneman who certainly has been one of the leaders of the field of behavioral economics.
We usually think of noise as measurement error and bias as judgment error but that is an inappropriate dichotomy. Noise is created with our judgment when we don't behave the same for similar decisions. Noise is an invisible problem because we don't believe we can create it. Noise is random, yet it is persistent when we don't follow an algorithm. Algorithms will shrink the noise both for your own decision-making and the across any set of analysts looking at the same problem.