Contrast between Predictive and Vulnerability Approaches

While efforts to predict natural phenomena have become an important aspect of the Earth sciences, the value of such efforts, as judged especially by their capacity to improve decision making and achieve policy goals, has been questioned by a number of constructive critics. The relationship between prediction and policy making is not straightforward for many reasons. In practice, consolidative and exploratory models are often confused by both scientists and policy-makers alike, thus a central challenge facing the community is the appropriate use of such models and resulting predictions (Sarewitz and Pielke 1999). Among the reasons for this criticism is that accurate prediction of phenomena may not be necessary to respond effectively to political or socio-economic problems created by such phenomena (for example, see Pielke et al. 1999). Indeed, phenomena or processes of direct concern to policy-makers may not be easily predictable or useful. Likewise, predictive research may reflect discipline-specific scientific perspectives that do not provide “answers” to policy problems since these may be complex mixtures of facts and values, and which are perceived differently by different policy-makers (for example, see Jamieson and Herrieck 1995). In addition, necessary political action may be deferred in anticipation of predictive information that is not forthcoming in a time frame compatible with such action. Similarly, policy action may be delayed when scientific uncertainties associated with predictions become politically charged as in the issue of global climate change, for example (Rayner and Malone 1997). Predictive information may also be subject to manipulation and misuse, either because the limitations and uncertainties associated with predictive models are not readily apparent or because the models are applied in a climate of political controversy and high economic stakes. In addition, emphasis on predictive sciences moves both financial and intellectual resources away from other types of research that might better help to guide decision making as, for example, incremental or adaptive approaches to environmental management that require monitoring and assessment instead of prediction (see Lee and Black 1993). These considerations suggest that the usefulness of scientific prediction for policy making and the resolution of societal problems depends on relationships among several variables, such as the timescales under consideration, the scientific complexity of the phenomena being predicted, the political and economic context of the problem, and the availability of alternative scientific and political approaches to the problem. In light of the likelihood of complex interplay among these variables, decision makers and scientists would benefit from criteria that would allow them to judge the potential value of scientific prediction and predictive modelling for different types of political and social problems related to Earth processes and the environment. Pielke et al. (1999) provide the following six guidelines for the effective use of prediction in decision making.