The aim of this paper is to provide a conceptual basis for the systematic treatment of uncertainty in model-based decision support activities such as policy analysis, integrated assessment and risk assessment. It focuses on the uncertainty perceived from the point of view of those providing information to support policy decisions (i.e., the modellers' view on uncertainty) -uncertainty regarding the analytical outcomes and conclusions of the decision support exercise. Within the regulatory and management sciences, there is neither commonly shared terminology nor full agreement on a typology of uncertainties. Our aim is to synthesise a wide variety of contributions on uncertainty in model-based decision support in order to provide an interdisciplinary theoretical framework for systematic uncertainty analysis. To that end we adopt a general definition of uncertainty as being any deviation from the unachievable ideal of completely deterministic knowledge of the relevant system. We further propose to discriminate among three dimensions of uncertainty: location, level and nature of uncertainty, and we harmonise existing typologies to further detail the concepts behind these three dimensions of uncertainty. We propose an uncertainty matrix as a heuristic tool to classify and report the various dimensions of uncertainty, thereby providing a conceptual framework for better communication among analysts as well as between them and policymakers and stakeholders. Understanding the various dimensions of uncertainty helps in identifying, articulating, and prioritising critical uncertainties, which is a crucial step to more adequate acknowledgement and treatment of uncertainty in decision support endeavours and more focused research on complex, inherently uncertain, policy issues.

One of the lay public's concerns about genetically modified (GM) organisms (GMO) and related emerging technologies is that not all the important risks are evaluated or even identified yet--and that ignorance of the unanticipated risks could lead to severe environmental or public health consequences. To some degree, even the scientists who participated in the analysis of the risks from GMOs (arguably the people most qualified to critique these analyses) share some of this concern. To formally explore the uncertainty in the risk assessment of a GM crop, we conducted detailed interviews of seven leading experts on GM oilseed crops to obtain qualitative and quantitative information on their understanding of the uncertainties associated with the risks to agriculture from GM oilseed crops (canola or rapeseed). The results of these elicitations revealed three issues of potential concern that are currently left outside the scope of risk assessments. These are (1) the potential loss of the agronomic and environmental benefits of glyphosate (a herbicide widely used in no-till agriculture) due to the combined problems of glyphosate-tolerant canola and wheat volunteer plants, (2) the growing problem of seed lot contamination, and (3) the potential market impacts. The elicitations also identified two areas where knowledge is insufficient. These are: the occurrence of hybridization between canola and wild relatives and the ability of the hybrids to perpetuate themselves in nature, and the fate of the herbicide-tolerance genes in soil and their interaction with soil microfauna and -flora. The methodological contribution of this work is a formal approach to analyzing the uncertainty surrounding complex problems.

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Uncertainty often becomes problematic when science is used to support decision making in the policy process. Scientists can contribute to a more constructive approach to uncertainty by making their uncertainties transparent. In this article, an approach to systematic uncertainty diagnosis is demonstrated on the case study of transgene silencing and GMO risk assessment. Detailed interviews were conducted with five experts on transgene silencing to obtain quantitative and qualitative information on their perceptions of the uncertainty characterising our knowledge of the phenomena. The results indicate that there are competing interpretations of the cause-effect relationships leading to gene silencing (model structure uncertainty). In particular, the roles of post-transcriptional gene silencing, position effects, DNA-DNA interactions, direct-repeat DNA structures, recognition factors and dsRNA and aberrant zRNA are debated. The study highlights several sources of uncertainty beyond the statistical uncertainty commonly reported in risk assessment. The results also reveal a discrepancy between the way in which uncertainties would be prioritized on the basis of the uncertainty analysis conducted, and the way in which they would be prioritized on the basis of expert willingness to pay to eliminate uncertainty. The results also reveal a diversity of expert opinions on the uncertainty characterizing transgene silencing. Because the methodology used to diagnose uncertainties was successful in revealing a broad spectrum of uncertainties as well as a diversity of expert opinion, it is concluded that the methodology used could contribute to increasing transparency and fostering a critical discussion on uncertainty in the decision making process.

One of the concerns often voiced by critics of the precautionary principle is that a widespread regulatory application of the principle will lead to a large number of false positives (i.e., over-regulation of minor risks and regulation of nonexisting risks). The present article proposes a general definition of a regulatory false positive, and seeks to identify case studies that can be considered authentic regulatory false positives. Through a comprehensive review of the science policy literature for proclaimed false positives and interviews with authorities on regulation and the precautionary principle we identified 88 cases. Following a detailed analysis of these cases, we found that few of the cases mentioned in the literature can be considered to be authentic false positives. As a result, we have developed a number of different categories for these cases of "mistaken false positives," including: real risks, "The jury is still out," nonregulated proclaimed risks, "Too narrow a definition of risk," and risk-risk tradeoffs. These categories are defined and examples are presented in order to illustrate their key characteristics. On the basis of our analysis, we were able to identify only four cases that could be defined as regulatory false positives in the light of today's knowledge and recognized uncertainty: the Southern Corn Leaf Blight, the Swine Flu, Saccharin, and Food Irradiation in relation to consumer health. We conclude that concerns about false positives do not represent a reasonable argument against future application of the precautionary principle.