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Although one might be tempted to infer from the foregoing arguments that judgments of likelihood should be considered only with caution, for some decision analytic frameworks that often appear in the climate policy literature (.g., cost-benefit analysis and IAMs), there often are few viable alternatives. However, as noted in the decision analysis frameworks guidance paper (Toth, 2000a; see also Section 2.4), several alternative decisional analytic methods are less dependent on subjective probability distributions; virtually all frameworks do require subjective judgments, however. Although physical properties such as weight, length, and illumination have objective methods for their measurement, there are no objective means for assessing in advance the probability of such things as the value future societies will put on now-endangered species or the circulation collapse of the North Atlantic Ocean from anticipated anthropogenic emissions. Even a highly developed understanding of probability theory would be of little avail because no empirical data set exists, and the underlying science is not fully understood. Some authors have argued that under these circumstances, for any practical application one ought to abandon any attempt to produce quantitative forecasts and instead use more qualitative techniques such as scenario planning (e.g., Schoemaker, 1991; van der Heijden, 1998) or argumentation (Fox, 1994). On the other hand, others—though noting the cognitive difficulties with estimation of unique events—have argued that quantitative estimations are essential in environmental policy analyses that use formal and explicit methods (e.g., Morgan and Henrion, 1990).
Given its potential utility in applied and conservation ecology, it seems surprising that Bayesian analysis is relatively uncommon. However, logical and theoretical virtue is not sufficient to encourage its use by managers and scientists. The spread of a new idea or practice is an example of cultural evolution (in this case, within the scientific community). It is best understood as a social and psychological phenomenon (Anderson, 1998).
Helping to achieve such penetration of awareness of uncertainty analyses will be a multi-step process that includes "1) consistent methods for producing verbal summaries from quantitative data, 2) translation of single-event probabilities into frequencies with careful definition of reference classes, 3) attention to different cognitive interpretations of probability concepts, and 4) conventions for graphic displays" (Anderson, 1998). The latter also is advocated in the uncertainties guidance paper (Moss and Schneider, 2000), and an example is provided in Chapter 7 (Figure 7-2).
Although all arguments in the literature agree that it is essential to represent uncertainties in climatic assessments, analysts disagree about the preferred approach. Some simply believe that until empirical information becomes available, quantitative estimates of uncertain outcomes should be avoided because "science" is based on empirical testing, not subjective judgments. It certainly is true that "science" itself strives for "objective" empirical information to test, or "falsify," theory and models (caveats in Section 2.5.2 about frequentism as a heuristic notwithstanding). At the same time, "science for policy" must be recognized as a different enterprise than "science" itself. Science for policy (e.g., Ravetz, 1986) involves being responsive to policymakers' needs for expert judgment at a particular time, given information currently available, even if those judgments involve a considerable degree of subjectivity. The methods outlined above and in Moss and Schneider (2000) are designed to make such subjectivity more consistently expressed (linked to quantitative distributions when possible, as needed in most decision analytic frameworks) across the TAR and more explicitly stated so that well-established and highly subjective judgments are less likely to get confounded in media accounts or policy debates. The key point is that authors should explicitly state their approach in each case. Transparency is the key to accessible assessments.
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