NUSAP

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]

The concept

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

  • Numeral will usually be an ordinary number;
  • Unit refers to the units used in Numeral;
  • Spread is an assessment of the error in the value of the Numeral;
  • Assessment is a summary of salient qualitative judgements about the information - this can be of statistical nature (a significance level) or more general, e.g. involving terms such as 'conservative' or 'optimistic'. If the number is model-generated Assessment may include an estimate of the quality of the related uncertainty and sensitivity analysis;
  • Pedigree is an evaluative description of the mode of production and of anticipated use of the information.

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]

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

gollark: Mildly more frankly than usual, this appears like an apioformic attempt to remain smugly intellectually superior at the expense of everyone else.
gollark: <:bees:724389994663247974> <:bees:724389994663247974> <:bees:724389994663247974> <:bees:724389994663247974>
gollark: Consume bees, then.
gollark: Yes, this is known.
gollark: Trivial BF substitution using these WHEN?

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. S. 0. Funtowicz and J.R. Ravetz, 1992, Uncertainty and Quality in Science for Policy, Radical Statistics, 50 (Spring '92), 31-34.
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