EXPERTISE AND FORECASTING IN THE CONTEXT OF POST-NORMAL SCIENCE

Abstract

In today’s context, the knowledge base of postnormal science must be extremely diverse and diversified to take into account a wide range of potentially useful knowledge and sources of relevant experience. At the same time, reliability is ensured primarily by the political strategy, not the knowledge base: what is most useful for a person, not the most correct scientific interpretation. In a postnormal environment, we should strive for adaptability based on attention to weak signals, preservation of diversity, and flexible management. In the context of the interaction between politics and science, it is necessary, at a minimum, to jointly develop research programmes, and ideally, to jointly produce, interpret and use scientific knowledge with policy makers. The emphasis should not be on knowledge transfer, but on establishing a dialogue to understand and shape what knowledge is needed, how it is understood and used. It is proved that the practical application of a politically grounded approach to knowledge production in scientific institutions is facilitated by post-normal science, the use of which is most promising in the work of interdisciplinary teams on a wide range of issues (environmental problems, sustainable development) and the development of strategic documents. The author analyses the special place of experts in the context of postnormal science, which does not consider science, politics and society as clearly delimited phenomena. Accordingly, the assessment of the quality of decision-making and evidence used in decision-making processes should involve a wide community of colleagues. The author presents the classifications of experts by J. Pielke, J. Wittmayer er and N. Schapke, E. Turnhout, and W. Tuinstra, which substantiate the requirements for experts to be aware of themselves as sharing and (co) creating knowledge together with policy makers and other stakeholders. It is noted that the change in the role of experts also implies a change in methodological approaches to forecasting and problem solving. In the context of post-normal science, four alternative scientific theories of decision-making suitable for situations of radical uncertainty can be distinguished: the concept of an extended peer communities; conviction narrative theory; the dynamic causal model; and quantitative story-telling.