By Silvio Funtowicz
Post-normal science comes into play for decision-making on policy issues where facts are uncertain, values in dispute, stakes high and decisions urgent.
A good example of a problem requiring post-normal science is the actions that need to be taken to mitigate the effects of sea level rise consequent on global climate change. All the causal elements are uncertain in the extreme, at stake is much of the built environment and the settlement patterns of people, what to save and what to sacrifice is in dispute, and the window for decision-making is shrinking. The COVID-19 pandemic is another instance of a post-normal science problem. The behaviour of the current and emerging variants of the virus is uncertain, the values of socially intrusive remedies are in dispute, and obviously stakes are high and decisions urgent.
In such contexts of policy making, normal science (in the Kuhnian sense, see Kuhn 1962) is still necessary, but no longer sufficient. We can locate post-normal science in relation to the more traditional problem-solving strategies in the figure below. This has two axes:
- ‘Systems uncertainties’ which conveys the principle that the problem is concerned not with the discovery of a particular fact, but with the comprehension or management of an inherently complex reality.
- ‘Decision stakes’ which concerns all the various costs, benefits, and value commitments that are involved in the issue through the various stakeholders.
When systems uncertainties or decision stakes are small, we are in the realm of ‘normal’ science, where expertise is fully effective. When either systems uncertainties or decision stakes rise then skill, judgement and sometimes even courage are required. This is the realm of professional consultancy. And when either or both systems uncertainties or decision stakes are high this is the realm of post-normal science.
In post-normal science the problems are set, and the solutions evaluated, by the criteria of the broader communities that are affected. Nevertheless, post-normal science is a valid form of enquiry – a type of science – and not merely politics or public participation. Post-normal science has the paradoxical feature that in its problem-solving activity the traditional domination of ‘hard facts’ over ‘soft values’ has been inverted.
It is important to appreciate that post-normal science is complementary to applied science and professional consultancy. It is not a replacement for traditional forms of science, nor does it contest the claims to reliable knowledge or certified expertise that are made on behalf of science in its legitimate contexts.
In post-normal science the activity of science encompasses the management of irreducible uncertainties in knowledge and in ethics, and the recognition of different legitimate perspectives and ways of knowing. The epistemological analysis of post-normal science, rooted in the practical tasks of quality assurance, shows that such an extension of peer communities, with the corresponding extension of facts, is necessary for the effectiveness of science in meeting the new challenges of global environmental problems.
Extended peer communities and quality assurance
The dynamic of resolution of policy issues in post-normal science involves the inclusion of an ever-growing set of legitimate participants in the process of quality assurance of the scientific inputs.
When problems lack neat solutions, when ethical aspects of the issues are prominent, when the phenomena themselves are ambiguous, and when all research techniques are open to methodological criticism, then the debates on quality are not enhanced by the exclusion of all but the specialist researchers and official experts. The extension of the peer community can positively enrich the processes of scientific investigation.
Knowledge of local conditions may determine which data are strong and relevant and can also help to define the policy problems. Those whose lives and livelihood depend on the solution of the problems will have a keen awareness of how the general principles are realized in their ‘back yards’. They will also have ‘extended facts’, including anecdotes and informal surveys. While they lack theoretical knowledge and are biased by self-interest, specialist researchers and official experts lack practical knowledge and have their own forms of bias.
Research science, professional practice, and industrial development each have means for quality assurance of the products of the work, be they peer review, professional associations, or the market. For the problems addressed by post-normal science, quality depends on open dialogue between all those affected.
NUSAP – The Management of Uncertainty and Quality in Quantitative Information
The notational system “NUSAP” enables the different sorts of uncertainty in quantitative information to be displayed in a standardized and self-explanatory way. It enables providers and users of such information to be clear about its uncertainties.
The NUSAP system is based on five categories, which allow each aspect of the information to be expressed in a flexible way. By means of NUSAP, nuances of meaning about quantities can be conveyed concisely and clearly, to a degree that is quite impossible otherwise. The name “NUSAP” is an acronym for the categories:
- Numeral, which will usually be an ordinary number; but when appropriate it can be a more general quantity, such as the expression “a million.”
- Unit, which may be of the conventional sort, but which may also contain extra information, such as the date at which the unit is evaluated (most commonly with money).
- Spread, which generalizes from the “random error” of experiments or the “variance” of statistics. Although spread is usually conveyed by a number (either ±, % or “factor of”) it is not an ordinary quantity, for its own inexactness is not of the same sort as that of measurements.
This brings us to the more qualitative side of the NUSAP expression:
- Assessment, which provides a place for a concise expression of the salient qualitative judgements about the information. In the case of statistical tests, this might be the significance-level; in the case of numerical estimates for policy purposes, it might be the qualifier “optimistic” or “pessimistic”.
- Pedigree, which is an evaluative description of the mode of production (and where relevant, of anticipated use) of the information. Each special sort of information has its own pedigree. Pedigree is expressed by means of a matrix; the columns represent the various phases of production or use of the information, and within each column there are modes, normatively ranked descriptions.
Conclusion
In this quick guide, I have aimed to set out key insights of post-normal science. If you identify as a post-normal scientist, I would be interested to hear how you have applied these ideas in your work. I would also be interested to hear how they resonate with systems thinkers, inter- and trans-disciplinarians and others who seek to support policy making on problems where facts are uncertain, values in dispute, stakes high and decisions urgent.
To find out more:
Funtowicz, S. O. and Ravetz, J. R. (1993). Science for the Post-Normal Age. Futures, 25, 7: 739-755. Republished in 2020 in Commonplace with a new COVID-19 related foreword. (Online – Open access): https://commonplace.knowledgefutures.org/pub/6qqfgms5/release/1
For applications of post-normal science:
Buschke, F. T., Botts, E. A. and Sinclair, S. P. (2019). Post-normal conservation science fills the space between research, policy, and implementation. Conservation Science and Practice, 1, 8, e73. (Online – Open access): https://doi.org/10.1111/csp2.73
Nogueira, L. A., Bjørkan, M. and Dale, B. (2021). Conducting Research in a Post-normal Paradigm: Practical Guidance for Applying Co-production of Knowledge. Frontiers in Environmental Science. 9. (Online – Open access): https://doi.org/10.3389/fenvs.2021.699397
For an application of NUSAP:
Van der Sluijs, J. P., Risbey, J. S. and Ravetz, J. (2005). Uncertainty assessment of VOC emissions from paint in The Netherlands using the NUSAP system. Environmental Monitoring and Assessment, 105, 1-3: 229-59 (Online – Open access): http://nusap.net/intareseuncertaintytraining/envmonass2005.pdf (PDF 740KB); or, (Online): https://doi.org/10.1007/s10661-005-3697-7
Reference:
Kuhn, T. S. (1962). The Structure of Scientific Revolutions. University of Chicago Press: Chicago, United States of America.
Biography: Silvio Funtowicz is guest researcher at the Centre for the Study of the Sciences & the Humanities at the University of Bergen, Norway. He is a philosopher of science active in the field of science and technology studies. @SFuntowicz.