The optimizer's curse: Skepticism and postdecision surprise in decision analysis

The optimizer's curse: Skepticism and postdecision surprise in decision analysis

0.00 Avg rating0 Votes
Article ID: iaor2008552
Country: United States
Volume: 52
Issue: 3
Start Page Number: 311
End Page Number: 322
Publication Date: Mar 2006
Journal: Management Science
Authors: ,
Abstract:

Decision analysis produces measures of value such as expected net present values or expected utilities and ranks alternatives by these value estimates. Other optimization-based processes operate in a similar manner. With uncertainty and limited resources, an analysis is never perfect, so these value estimates are subject to error. We show that if we take these value estimates at face value and select accordingly, we should expect the value of the chosen alternative to be less than its estimate, even if the value estimates are unbiased. Thus, when comparing actual outcomes to value estimates, we should expect to be disappointed on average, not because of any inherent bias in the estimates themselves, but because of the optimization-based selection process. We call this phenomenon the optimizer's curse and argue that it is not well understood or appreciated in the decision analysis and management science communities. This curse may be a factor in creating skepticism in decision makers who review the results of an analysis. In this paper, we study the optimizer's curse and show that the resulting expected disappointment may be substantial. We then propose the use of Bayesian methods to adjust value estimates. These Bayesian methods can be viewed as disciplined skepticism and provide a method for avoiding this postdecision disappointment.

Reviews

Required fields are marked *. Your email address will not be published.