Investors often select projects whose estimated performance measures meet or exceed a hurdle value. At the time of decision making, the true performance of a project is unknown but uncertain forecasts are available. Decision makers (DMs) often ignore the prediction errors when they use these forecasts to choose projects. To the disappointment of the DMs, many selected projects result in smaller actual yields than those that were forecasted.
Some have attributed the cause of this to the optimistic bias of the predictions. This paper shows that this disappointment can occur even if the prediction is unbiased. In this case, a bias can be introduced by the selection process that will allow more unattractive (overestimated) projects to be accepted than attractive (underestimated) ones. Although a similar phenomenon has been noted in statistics and finance research, it is not well understood in the context of project selection by DMs.
We present a solution method based on Bayesian updating and demonstrate its effectiveness in eliminating the disappointment in project selection with realistic data from oil exploration and production projects.