This paper (Schuyler, J. R., & Nieman, T. (2007, January 1). Optimizer’s Curse: Removing the Effect of this Bias in Portfolio Planning. Society of Petroleum Engineers. doi:10.2118/107852-MS; earlier version) has some simple recommendations for dealing with the Optimizer’s Curse:
The impacts of the OC will be evident for any decisions involving ranking and selection among alternatives and projects. As described in Smith and Winkler, the effects increase when the true values of alternatives are more comparable and when the uncertainty in value estimations is higher. This makes intuitive sense: We expect a higher likelihood of making incorrect decisions when there is little true difference between alternatives and where there is significant uncertainty in our ability to asses value.
(...) Good decision-analysis practice suggests applying additional effort when we face closely competing alternatives with large uncertainty. In these cases, we typically conduct sensitivity analyses and value-of-information assessments to evaluate whether to acquire additional information. Incremental information must provide sufficient additional discrimination between alternatives to justify the cost of acquiring the additional information. New information will typically reduce the uncertainty in our values estimates, with the additional benefit of reducing the magnitude of OC.
The paper’s focus is actually on a more concrete Bayesian approach, based on modelling the population from which potential projects are sampled.
This paper (Schuyler, J. R., & Nieman, T. (2007, January 1). Optimizer’s Curse: Removing the Effect of this Bias in Portfolio Planning. Society of Petroleum Engineers. doi:10.2118/107852-MS; earlier version) has some simple recommendations for dealing with the Optimizer’s Curse:
The paper’s focus is actually on a more concrete Bayesian approach, based on modelling the population from which potential projects are sampled.