Late lessons from early warnings: towards precaution and realism in research and policy.

This paper focuses on the evidentiary aspects of the precautionary principle. Three points are highlighted: (i) the difference between association and causation; (ii) how the strength of scientific evidence can be considered; and (iii) the reasons why regulatory regimes tend to err in the direction of false negatives rather than false positives. The point is made that because obtaining evidence of causation can take many decades of research, the precautionary principle can be invoked to justify action when evidence of causation is not available, but there is good scientific evidence of an association between exposures and impacts. It is argued that the appropriate level of proof is context dependent, as "appropriateness" is based on value judgements about the acceptability of the costs, about the distribution of the costs, and about the consequences of being wrong. A complementary approach to evaluating the strength of scientific evidence is to focus on the level of uncertainty. If decision makers are made aware of the limitations of the knowledge base, they can compensate by adopting measures aimed at providing early warnings of un-anticipated effects and mitigating their impacts. The point is made that it is often disregarded that the Bradford Hill criteria for evaluating evidence are asymmetrical, in that the applicability of a criterion increases the strength of evidence on the presence of an effect, but the inapplicability of a criterion does not increase the strength of evidence on the absence of an effect. The paper discusses the reason why there are so many examples of regulatory "false negatives" as opposed to "false positives". Two main reasons are put forward: (i) the methodological bias within the health and environmental sciences; and (ii) the dominance within decision-making of short term economic and political interests. Sixteen features of methods and culture in the environmental and health sciences are presented. Of these, only three features tend to generate "false positives". It is concluded that although the different features of scientific methods and culture produce robust science, they can lead to poor regulatory decisions on hazard prevention.

Using the W&H integrated uncertainty analysis framework with non-initiated experts.

The W&H (Walker and Harremoës) integrated uncertainty analysis framework was put forward with the aim of providing 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 provides a heuristic tool that can be applied in decision support exercises to classify and report the various dimensions of uncertainty. The intention is to stimulate better communication among analysts as well as between them and policymakers and stakeholders. The framework successfully articulates diverse scholarly understandings of 'uncertainty', 'ignorance', and 'quality' in science for policy, Nevertheless, experience with the W&H framework has revealed that many of the concepts put forward are relatively unfamiliar--and perhaps somewhat controversial--to experts practising decision support. Thus, efforts are required to communicate the W&H framework to experts in such a way that their knowledge of uncertainty is elicited adequately, without them being overly intimidated or confused by the novelty of the concepts presented to them. After introducing the W&H conceptual framework, this paper presents the methodology that was used in applying the W&H framework in expert elicitations on uncertainty in the risk assessment of genetically modified crops. Experiences with the use of this methodology are discussed and recommendations for further improvement are given.