Inevitable disappointment and decision making based on forecasts
In decision problems where decisions on risky pro jects are made based on the forecasts of their performance, ignoring the prediction errors can cause the problem known as post-decision disappointment. We describe the disappointment in situations when pro jects are accepted by comparing their forecasts with a threshold value directly, and discuss the conditions when it occurs. Next we study a general decision problem where one single unbiased estimate is available for the pro ject under consideration. A decision-theoretic model is proposed to show that the optimal decision can be obtained through a Bayesian updating procedure. And special interest is paid to a judgment decision procedure, in which the unknown true outcome is substituted by a forecast. Further, by extending the model we demonstrate that the Bayesian approach can aid the DM in nding the optimal decision using multiple forecasts. We also consider aggregating several correlated forecasts into a single predictor, and show that a mixed two-stage approach, by rst combining the forecasts into a weighted average and then applying the Bayesian updating procedure, vi is possible if the weighted average is a su cient statistic. Finally we describe how to dynamically update the estimate of a pro ject with forecasts from other correlated projects.