NUSAP

Last updated

NUSAP is a notational system for the management and communication of uncertainty in science for policy, based on five categories for characterizing any quantitative statement: Numeral, Unit, Spread, Assessment and Pedigree. NUSAP was introduced by Silvio Funtowicz and Jerome Ravetz in the 1990 book Uncertainty and quality in science for policy. [1] See also van der Sluijs et al. 2005. [2]

Silvio O. Funtowicz is a philosopher of science active in the field of science and technology studies. He created the NUSAP, notational system for characterising uncertainty and quality in quantitative expressions, and together with Jerome R. Ravetz he introduced the concept of post-normal science. He is professor at the University of Bergen (Norway) at the Centre for the Study of the Sciences and the Humanities (SVT).

Jerome Ravetz American philosopher

Jerome (Jerry) Ravetz is a philosopher of science. He is best known for his books analysing scientific knowledge from a social and ethical perspective, focussing on issues of quality. He is the co-author of the NUSAP notational system and of Post-normal science. He is currently an Associate Fellow at the Institute for Science, Innovation and Society, University of Oxford.

Contents

The concept

The name "NUSAP" is an acronym for the categories just mentioned.

The pedigree can be expressed by means of a matrix; the columns represent the various phases of production or use of the information, and each column contains marks to rank the performance. Marks can be numerical as well as qualitative, see an example here. Recent applications of NUSAP are in the field of climate science, [3] [4] hydrology, [5] medical research [6] and risk assessment. [7] [8] Applications relevant to the activities of the European Food Safety Authority EFSA are Bouwknegt and Havelaar (2015) [9] and EFSA BIOHAZ Panel, (2015). [10]

Together with Sensitivity auditing NUSAP can be considered as a method useful at the science policy interface - when numbers produced by either experiment, survey or mathematical modelling need to be used in the appraisal or the formulation of a policy. See also Post-normal science. [11] [12] [13]

Sensitivity auditing is an extension of sensitivity analysis for use in policy-relevant modelling studies. Its use is recommended - e.g. in the European Commission Impact assessment guidelines - when a sensitivity analysis (SA) of a model-based study is meant to demonstrate the robustness of the evidence provided by the model, but in a context where the inference feeds into a policy or decision-making process.

Post-normal science

Post-normal science (PNS) represents a novel approach for the use of science on issues where "facts [are] uncertain, values in dispute, stakes high and decisions urgent". PNS was developed in the 1990s by Silvio Funtowicz and Jerome R. Ravetz. It can be considered as a reaction to the styles of analysis based on risk and cost-benefit analysis prevailing at that time, and as an embodiment of concepts of a new "critical science" developed in previous works by the same authors. In a more recent work PNS is described as "the stage where we are today, where all the comfortable assumptions about science, its production and its use, are in question".

An early description of NUSAP is due to Funtowicz and Ravetz. [14]

Related Research Articles

Broadly speaking, a risk assessment is the combined effort of:

  1. identifying and analyzing potential (future) events that may negatively impact individuals, assets, and/or the environment ; and
  2. making judgments "on the tolerability of the risk on the basis of a risk analysis" while considering influencing factors.

Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system can be divided and allocated to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem.

European Food Safety Authority agency of the European Union

The European Food Safety Authority (EFSA) is the agency of the European Union (EU) that provides independent [disputed] scientific advice and communicates on existing and emerging risks associated with the food chain. EFSA was established in February 2002, is based in Parma, Italy, and has a budget for 2016 of €79.5 million, and a total staff of 447.

In decision theory, the evidential reasoning approach (ER) is a generic evidence-based multi-criteria decision analysis (MCDA) approach for dealing with problems having both quantitative and qualitative criteria under various uncertainties including ignorance and randomness. It has been used to support various decision analysis, assessment and evaluation activities such as environmental impact assessment and organizational self-assessment based on a range of quality models.

In science, engineering, and research, expert elicitation is the synthesis of opinions of authorities of a subject where there is uncertainty due to insufficient data or when such data is unattainable because of physical constraints or lack of resources. Expert elicitation is essentially a scientific consensus methodology. It is often used in the study of rare events. Expert elicitation allows for parametrization, an "educated guess", for the respective topic under study. Expert elicitation generally quantifies uncertainty.

Integrated assessment modelling (IAM) or integrated modelling (IM) is a term used a type of scientific modelling that tries to link main features of society and economy with the biosphere and atmosphere into one modelling framework. The goal of integrated assessment modelling is to accommodate informed environmental policy-making, usually in the context of climate change. While the detail and extent of integrated disciplines varies strongly per model, all climatic integrated assessment modelling includes economic processes as well as processes producing greenhouse gases.

Robust decision-making (RDM) is an iterative decision analytic framework that aims to help identify potential robust strategies, characterize the vulnerabilities of such strategies, and evaluate the tradeoffs among them. RDM focuses on informing decisions under conditions of what is called "deep uncertainty", that is, conditions where the parties to a decision do not know or do not agree on the system model(s) relating actions to consequences or the prior probability distributions for the key input parameters to those model(s).

A food safety-risk analysis is essential not only to produce or manufacture high quality goods and products to ensure safety and protect public health, but also to comply with international and national standards and market regulations. With risk analyses food safety systems can be strengthened and food-borne illnesses can be reduced. Food safety risk analyses focus on major safety concerns in manufacturing premises—not every safety issue requires a formal risk analysis. Sometimes, especially for complex or controversial analyses, regular staff is supported by independent consultants.

Extended peer review is the process of including people and groups with experience beyond that of working academics in the processes of assuring the quality of research. If conducted systematically, this can lead to more reliable, or applicable, results than a peer review process conducted purely by academics.

P-boxes and probability bounds analysis have been used in many applications spanning many disciplines in engineering and environmental science, including:

Pest risk analysis (PRA) is a form of risk analysis conducted by regulatory plant health authorities to identify the appropriate phytosanitary measures required to protect plant resources against new or emerging pests and regulated pests of plants or plant products. Specifically pest risk analysis is a term used within the International Plant Protection Convention (IPPC) and is defined within the glossary of phytosanitary terms. as "the process of evaluating biological or other scientific and economic evidence to determine whether an organism is a pest, whether it should be regulated, and the strength of any phytosanitary measures to be taken against it". In a phytosanitary context, the term plant pest, or simply pest, refers to any species, strain or biotype of plant, animal or pathogenic agent injurious to plants or plant products and includes plant pathogenic bacteria, fungi, fungus-like organisms, viruses and virus like organisms, as well as insects, mites, nematodes and weeds.

The merger of knowledge with power: essays in critical science is a book written in 1990 by Jerome Ravetz.

<i>Uncertainty and Quality in Science for Policy</i>

Uncertainty and Quality in Science for Policy is a 1990 book by Silvio Funtowicz and Jerome Ravetz, in which the authors explain the notational system NUSAP and applies it to several examples from the environmental sciences. The work is considered foundational to the development of post-normal science.

<i>Science on the Verge</i>

Science on the verge is a book written in 2016 by group of eight scholars working in the tradition of Post-normal science. The book analyzes the main features and possible causes of the present science's crisis.

<i>The No-Nonsense Guide to Science</i> book by Jerome Ravetz

The No-Nonsense Guide to Science is a 2006 book by the philosopher Jerome Ravetz, one of the fathers of Post-normal science, in which the author offers a critical account of techno-science and argues for a deeper appreciation of uncertainty and ignorance in scientific knowledge and for a need for citizens’ participation in the appraisal of science when this is used in support or relation to policy.

Quantitative storytelling (QST) is a systematic approach to explore the multiplicity of frames potentially legitimate in a scientific study or controversy. QST assumes that in an interconnected society more frameworks and worldviews are legitimately upheld by different constituencies and social actors. QST looks critically to models used in evidence-based policy (EBP). These are often in the form of risk analyses or cost benefit analyses, and necessarily focus on a single framing of the issue under consideration. QST suggest corrective approaches to this practice.

References

  1. ↑ Funtowicz, S. & Ravetz J., 1990, Uncertainty and quality in science for policy, Kluwer Academic Publishers, Dordrecht.
  2. ↑ van der Sluijs, J., Craye, M., Funtowicz, S., Kloprogge, P., Ravetz, J., and Risbey, J. (2005) Combining Quantitative and Qualitative Measures of Uncertainty in Model based Environmental Assessment: the NUSAP System, Risk Analysis, 25 (2). p. 481-492.
  3. ↑ Van Der Sluijs, J.P., Wardekker, J.A., 2015, Critical appraisal of assumptions in chains of model calculations used to project local climate impacts for adaptation decision support - The case of Baakse Beek, Environmental Research Letters, 10(4), doi:10.1088/1748-9326/10/4/045005.
  4. ↑ Lorenz, S; Dessai, S; Paavola, J; Forster, P M., 2015, The communication of physical science uncertainty in European National Adaptation Strategies, Climatic Change132.1 (Sep 2015): 143-155.
  5. ↑ Zhu, Q., Xu, X., Gao, C., Ran, Q.-H., Xu, Y.-P., 2013, Qualitative and quantitative uncertainties in regional rainfall frequency analysis, Journal of Zhejiang University: Science A, Volume 16, Issue 3, 2015, Pages 194-203.
  6. ↑ Kloprogge, P., Van der Sluijs, J.P., Petersen, A.C., 2011, A method for the analysis of assumptions in model-based environmental assessments, Environmental Modelling and Software, 26(3), 289-301.
  7. ↑ Ides Boone, Yves Van der Stede, Jeroen Dewulf, Winy Messens, Marc Aerts, Georges Daube and Koen Mintiens, 2010, NUSAP: a method to evaluate the quality of assumptions in quantitative microbial risk assessment, Journal of Risk Research, 13(3), 337-352.
  8. ↑ Christine Louise Berner, Roger Flage, 2016, Comparing and integrating the NUSAP notational scheme with an uncertainty based risk perspective, Reliability Engineering & System Safety, 156, Pages 185–194.
  9. ↑ Bouwknegt M and Havelaar AH, 2015. Uncertainty assessment using the NUSAP approach: a case study on the EFoNAO tool. EFSA supporting publication 2015: EN-663, 20 pp.
  10. ↑ EFSA BIOHAZ Panel (EFSA Panel on Biological Hazards), 2015. Scientific Opinion on the development of a risk ranking toolbox for the EFSA BIOHAZ Panel. EFSA Journal 2015;13(1):3939, 131 pp. doi:10.2903/j.efsa.2015.3939.
  11. ↑ Funtowicz, S.O. and Jerome R. Ravetz (1991). "A New Scientific Methodology for Global Environmental Issues." In Ecological Economics: The Science and Management of Sustainability. Ed. Robert Costanza. New York: Columbia University Press: 137–152.
  12. ↑ Funtowicz, S. O., & Ravetz, J. R. 1992. Three types of risk assessment and the emergence of postnormal science. In S. Krimsky & D. Golding (Eds.), Social theories of risk (pp. 251–273). Westport, CT: Greenwood.
  13. ↑ Funtowicz, S. O. & Ravetz, J. R. 1993. Science for the post-normal age. Futures, 25(7), 739–755.
  14. ↑ here S. 0. Funtowicz and J.R. Ravetz, 1992, Uncertainty and quality in science for policy, Radical Statistics, 50 (Spring '92), 31-34.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.