Krayer von Krauss M, van Asselt M B A, Henze M, Ravetz J, Beck M B
Environment & Resources, Technical University of Denmark, Bygningstorvet, Denmark.
Water Sci Technol. 2005;52(6):1-9.
In this paper, two different visions of the relationship between science and policy are contrasted with one another: the "modern" vision and the "precautionary" vision. Conditions which must apply in order to invoke the Precautionary Principle are presented, as are some of the main challenges posed by the principle. The following central question remains: If scientific certainty cannot be provided, what may then justify regulatory interventions, and what degree of intervention is justifiable? The notion of "quality of information" is explored, and it is emphasized that there can be no absolute definition of good or bad quality. Collective judgments of quality are only possible through deliberation on the characteristics of the information, and on the relevance of the information to the policy context. Reference to a relative criterion therefore seems inevitable and legal complexities are to be expected. Uncertainty is presented as a multidimensional concept, reaching far beyond the conventional statistical interpretation of the concept. Of critical importance is the development of methods for assessing qualitative categories of uncertainty. Model quality assessment should observe the following rationale: identify a model that is suited to the purpose, yet bears some reasonable resemblance to the "real" phenomena. In this context, "purpose" relates to the policy and societal contexts in which the assessment results are to be used. It is therefore increasingly agreed that judgment of the quality of assessments necessarily involves the participation of non-modellers and non-scientists. A challenging final question is: How to use uncertainty information in policy contexts? More research is required in order to answer this question.
在本文中,科学与政策关系的两种不同观点相互对照:“现代”观点和“预防”观点。文中阐述了援引预防原则必须适用的条件,以及该原则带来的一些主要挑战。以下核心问题依然存在:如果无法提供科学确定性,那么什么可以证明监管干预的合理性,以及何种程度的干预是合理的?文中探讨了“信息质量”的概念,并强调对于信息质量的好坏不存在绝对定义。只有通过对信息特征以及信息与政策背景相关性的审议,才能做出关于质量的集体判断。因此,参考相对标准似乎不可避免,并且可以预期会出现法律复杂性问题。不确定性被呈现为一个多维概念,远远超出了对该概念的传统统计解释。至关重要的是开发评估不确定性定性类别的方法。模型质量评估应遵循以下基本原理:确定一个适合目的但与“真实”现象有一定合理相似性的模型。在此背景下,“目的”与评估结果将被应用的政策和社会背景相关。因此,越来越多的人认同评估质量的判断必然需要非建模人员和非科学家的参与。一个具有挑战性的最终问题是:如何在政策背景中使用不确定性信息?为了回答这个问题,还需要更多研究。