60 -I 137.0~29 ERROR BARS ¿ tONE (1" 4 E a. a. Z30 137.0388 0 137.0377 ~ ~20 0 to 0 '50 Fig.1. Successive recommended values of the fine-structure constand IX-I (B. N. Taylor et 01., 1969,7) reminder that the value is not fully accepted by colleagues, since they will expect it to jump about for a while longer. Our next example is taken from a recent study in the social sciences. It shows how a set of related estimates of uncertainty can be expressed clearly and effectively by NUSAP. Suppose that we wish to forecast what the future price of a basic commodity might be, especially when at the moment its price is artificially maintained by a cartel of producers. There is no experimental evidence on such a future contingency, and yet we are not completely in the dark. There is a long history of expertise in the field; and there is a well-tried standard model by which experts' guesses can be translated into mathematical form.

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Prologue.- Introduction: Some Illustrative Examples.- 1. Science for Policy: Uncertainty and Quality.- 1.1 Information for Policy-Related Research.- 1.2 How to Cope with Uncertainty?.- 1.3 Dilemmas for Science.- 1.4 Quality Assurance and Policy.- 1.5 Uncertainty and Policy.- 2. Uncertainty and Its Management.- 2.1 Uncertainty in Probability.- 2.2 Statistics, Computers and Uncertainty.- 2.3 Types of Uncertainty.- 2.4 Uncertainty-Avoidance in Bureaucracies.- 2.5 Criticism: Technical, Methodological and Philosophical.- 2.6 The NUSAP Scheme, Uncertainty and Quality.- 2.7 NUSAP: Philosophy and Practice.- 3. The Mathematical Language.- 3.1 Historical Perspective.- 3.2 Mathematical Language and Uncertainty.- 3.3 Formalization and Infinite Regress.- 3.4 Rules: When to Over-Ride?.- 3.5 Ambiguity and Vagueness.- 3.6 Arithmetical Rules: The Fossils Joke.- 3.7 Zero: Counter or Filler?.- 3.8 Rounding-off: The ?-Dilemma.- 3.9 Craft Skills and "Monsters".- 4. Craft Skills with Numbers.- 4.1 Skills in Statistics.- 4.2 Skills in Cost-Benefit Analysis.- 4.3 Skills in Science.- 4.4 Degeneration of Skills.- 4.5 Policy-Related Research and Skills.- 4.6 New Skills for Policy-Related Research.- 4.7 Diffusing the Skills of Quality Assurance.- 5. Measurements.- 5.1 History in Science.- 5.2 Uncertainties at the Foundations of Science.- 5.3 N. R. Campbell: Measuring Length.- 5.4 Temperature: Measurement and Calculation.- 5.5 Uncertainties in Practice and Theory.- 5.6 Scientific Uncertainty: Philosophy and Practice.- 6. Maps.- 6.1 "Soft" Maps v. "Hard" Numbers.- 6.2 Maps and their Uncertainties.- 6.3 The "Border with Ignorance".- 6.4 Maps: Why Quality Counts.- 6.5 Intermediate Cases: Theme and Graph Maps.- 6.6 Graphs.- 6.7 Dials v. Digits.- 6.8 The Fruitful Vagueness of Maps.- 7.Mathematical Notations: Functions and Design.- 7.1 Mathematics and Symbolism.- 7.2 Designing for Uncertainty.- 7.3 Functions of Numbers.- 7.4 Names for Numbers: The "Billion" Story.- 7.5 Place-Value Scheme.- 7.6 Fruitful Contradiction.- 7.7 Symbolism in Chemistry.- 8. The NUSAP Scheme: Introduction.- 8.1 NUSAP: Design Criteria.- 8.2 NUSAP: Place-Value.- 8.3 Trading-Off Uncertainties.- 9. The NUSAP Categories: Numeral, Unit and Spread.- 9.1 Numeral.- 9.2 Unit.- 9.3 Numeral and Unit: Nuances of Expression.- 9.4 Spread.- 9.5 Topology: Grid and Resolution.- 9.6 Spread as a Quasi-Quantity.- 10. The NUSAP Categories: Assessment and Pedigree.- 10.1 Assessment.- 10.2 Examples of Assessment.- 10.3 Pedigree.- 10.4 The Pedigree Matrix for Research.- 10.5 Applications of Pedigree.- 10.6 A NUSAP Expression for a Policy Forecast.- 10.7 Pedigrees for Environmental Models and for Functional Quality.- 10.8 Elicitation: Use and Design of Pedigree.- 11. The NUSAP Pedigree for Statistical Information.- 11.1 Statistical Information: Its Production in Bureaucracies.- 11.2 The Pedigree Matrix.- 11.3 Practical Problems.- 11.4 An Illustrative Example.- 11.5 Indicators: The Elucidation of Quality.- 12. Mapping Uncertainties of Radiological Hazards.- 12.1 Quality of Radiological Data.- 12.2 Quality Evaluation for Radiological Model Parameters.- 12.3 Illustration of Pedigree Ratings for Model Parameters.- 12.4 Parameter Uncertainty and Model Reliability.- 12.5 Parameter Contribution to Model Spread.- 12.6 Illustrative Example.- 13. Further Applications of The NUSAP System.- 13.1 An Arithmetic for Assessment.- 13.2 An Example: The Valuation of Ecosystems.- 13.3 Risk Indices: A NUSAP Analysis.- 13.4 Calculating the Risk Indices for Energy Technologies.- Epilogue.- References.