To people trained in the physical sciences dealing with uncertainty is almost second nature. Serious physical scientists would not report an experimental result without an associated estimate of uncertainty, nor would they design a new experiment without giving careful attention to uncertainties. Thus, when we first left the physical sciences and started doing policy analysis, we simply did what came naturally. We worried about uncertainty. It didn't take long to discover that only a handful of other analysts (most of them trained in decision analysis or engineering) shared our concern. It took only a little longer to understand that, with its enormous inherent uncertainties, the field of policy analysis needs to be concerned with the characterization and analysis of uncertainty to an extent that far exceeds the need in the physical sciences. We set out to do such analysis, and along the way to build computer-based tools which would make the job easier. In the process, we discovered that what really matters is the training and philosophy of the analysts who use these tools. Through the doctoral program in the Department of Engineering and Public Policy at Carnegie Mellon University we have invested heavily in such education.

Today, while the adequate treatment of uncertainty in policy analysis is still the exception, not the rule, the exceptions are becoming more frequent.