It goes almost without saying that doing quantitative analysis means creating computer models, especially if you want to treat the uncertainty explicitly. It is possible to do back-of-the-envelope calculations for a handful of variables with pencil and paper, using simple error-propagation methods (Section 8.3.5) or probability trees (Section 8.4). Indeed, that is an excellent way to develop your intuitions when you start a project. Any serious uncertainty analysis, however, requires a computer. Fortunately, there are several commercially available software products that make the probabilistic treatment of uncertainty quite straightforward. These tools obviate the need for the analyst to master the intricacies of implementing complicated Monte Carlo codes.

How should we choose and use software for quantitative modeling and risk analysis? At first blush, you might think that quantitative modeling — especially when it treats uncertainty — is primarily a matter of number crunching. Certainly, that is a critical part of it; but if the ultimate purpose of the computation is improved insight into complicated situations, and enhanced understanding of the risks and opportunities, then modeling comprises a great deal more. The treatment of uncertainty (the primary focus of this book) is only one of many objectives.

In Chapter 3, we discussed the goals and motivations for risk and policy analysis, culminating in our suggested “ten commandments for good policy analysis” (Section 3.8).