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Modelling, making inferences and making decisions: The roles of sensitivity analysis

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Abstract

Sensitivity analysis, robustness studies and uncertainty analyses are key stages in the modelling, inference and evaluation used in operational research, decision analytic and risk management studies. However, sensitivity methods -or others so similar technically that they are difficult to distinguish from sensitivity methods- are used in many different circumstances for many different purposes; and the manner of their use in one context may be inappropriate in another. Thus in this paper, I categorise and explore the use of sensitivity analysis and its parallels, and in doing so I hope to provide a guide and typology to a large growing literature.

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References

  • Bauer K.W., Parnell G. and Meyers D.A. (1999). Response surface methodology as a sensitivity analysis tool in decision analysis.Journal of Multi Criteria Decision Analysis 8, 162–180.

    Article  Google Scholar 

  • Bedford T. and Cooke R. (2001).Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press.

  • Berger J.O. (1994). An overview of robust Bayesian analysis (with discussion).Test 3, 5–124.

    Article  Google Scholar 

  • Berger J.O., Bayarri M.J. and Molina G. (2001). Fast simulators for assessment and propagation of uncertainty. In: Prado R. and Bolado R. (eds), SAMO 2001: Third international Symposium on Sensitivity Analysis of Model Output. CIEMAT, Madrid, Spain, 193–200.

    Google Scholar 

  • Chambers J.M., Cleveland W.S., Kleiner B. and Tukey P.A. (1983).Graphical Methods for Data Analysis. Duxbury.

  • Chan K., Tarantola S. and Campolongo F. (1998) SAMO: Second International Symposium on Sensitivity Analysis in Model Output. EU Joint research Centre, European Commission, Luxembourg.

    Google Scholar 

  • Clemen R.T. (1996)Making Hard Decisions: an Introduction to Decision Analysis (second edition). PWS-KENT.

  • Cleveland W.S. (1994).The Elements of Graphing Data. Murray Hill.

  • Craig P.S., Goldstein M., Seheult A.H. and Smith J.A. (1997). Pressure matching for hydrocarbon reservoirs: A case study in the use of Bayes Linear strategies for large computer experiments, with discussion. In: Gatsonis C., Hodges J.S., Kass R.E., McCulloch R., Rossi P. and Singpurwalla, N.D. (eds.),Case Studies in Bayesian Statistics III. Springer, 37–93.

  • Daellenbach H.G. (1994).Systems and Decision Making: a Management Science Approach. John Wiley.

  • Ehrhardt J. and Weiss A.(2000).RODOS: Decision Support System for Off-Site Nuclear Emergency Management in Europe. EUR 19144 EN. European Commission, Office for Official Publications of the European Communities.

  • Flew A. (1971).An Introduction to Western Philosophy. Thames and Hudson.

  • Fox L. and Meyers D.F. (1968).Computing Methods for Scientists and Engineers. Oxford University Press.

  • Fraedrich D. and Gover R. (2001). Statistical uncertainty analysis as a tool for model validation. In: Prado R. and Bolado R. (eds), SAMO 2001: Third international Symposium on Sensitivity Analysis of Model Output. CIEMAT, Madrid, Spain, 165–168.

    Google Scholar 

  • French S. (1984). Interactive multi-objective programming: its aims, applications and demands.Journal of the Operational Research Society 35, 827–834.

    Article  Google Scholar 

  • French S. (1992). The Role of Sensitivity Analysis in Decision Analysis. In: Holtham C. (ed.),Executive Information Systems and Group Decision Support. Chapman and Hall, 99–123.

  • French S. (1995). Uncertainty and Imprecision: Modelling and Analysis.Journal of the Operational Research Society 46, 70–79.

    Article  Google Scholar 

  • French S. (1998). Sensitivity analysis and uncertainty modelling in decision support systems. In: Chan K., Tarantola S. and Campolongo F. (1998) SAMO: Second International Symposium on Sensitivity Analysis in Model Output. EU Joint research Centre, European Commission, Luxembourg, 117–120.

    Google Scholar 

  • French S., Kelly G.N. and Morrey M. (1992). Decision conferencing and the International Chernobyl Project.Journal Radiological Protection Dosimetry 12, 17–28.

    Article  Google Scholar 

  • French S. and Smith J.Q. (1997).The Practice of Bayesian Analysis. Edward Arnold.

  • French S. and Rios Insua D. (2000) Statistical Decision Analysis. Edward Arnold.

  • Geweke J. (1998). Simulation methods for model criticism and robustness analysis In: Bernardo J.M., Berger J.O., Dawid A.P. and Smith A.F.M. (eds.)Bayesian Statistics 6. Oxford University Press, 275–299

  • Gilks W.R., Richardson S. and Spiegelhalter D.J. (1996).Markov Chain Monte Carlo in Practice. Chapman and Hall.

  • Gottlieb J.E. and Pauker S.G. (1981). Whether or not to administer to an immunosuppressed patient with Hematologic malignancy and undiagnosed fever.Medical Decision Making 1, 75–93.

    Article  Google Scholar 

  • Hodgkin J., Belton V. and Koulouri A (2002). Intelligent user support for MCDA: a case study. Dept of Management Science Working Paper, University of Strathclyde.

  • Kadane J.B. and Srinivasan C. (1994). Discussion of: Berger J.O., An overview of robust Bayesian analysis.Test 3, 93–95.

    Google Scholar 

  • Kennedy M.C. and O’Hagan A. (2001). Bayesian calibration of computer models (with discussion).Journal of the Royal Statistical Society, Series B 63, 425–464.

    Article  Google Scholar 

  • Leamer E.E. (1977).Specification Searches. John Wiley.

  • Liseo B. Petrella L. and Salinetti G. (1996). Robust Bayesian Analysis: an interactive approach. In: Bernardo J.M., Berger J.O., Dawid A.P. and Smith A.F.M. (eds.),Bayesian Statistics 5. Oxford University Press, 661–666.

  • Marseguerra M., Masini R., Zio E. and Cojazzi G. (2001). Using neural nets as non- linear model interpolators in variance decomposition based sensitivity analysis. In: Prado R. and Bolado R. (eds), SAMO 2001: Third international Symposium on Sensitivity Analysis of Model Output. CIEMAT, Madrid, Spain, 23–26.

    Google Scholar 

  • McKay M.D., Beckman R.J. and Campbell K. (2001). On prediction and model validation. In: Prado R. and Bolado R. (eds), SAMO 2001: Third international Symposium on Sensitivity Analysis of Model Output. CIEMAT, Madrid, Spain, 161–164.

    Google Scholar 

  • Migon H.S. and Gamerman D. (1999).Statistical Inference: an Integrated Approach. Edward Arnold.

  • Morgan B.J.T. (2000).Applied Stochastic Modelling. Edward Arnold.

  • O’Hagan A., Kennedy M.C. and Oakley J.E. (1998). Uncertainty analysis and other inference tools for complex computer codes. In: Bernardo J.M., Berger J.O., Dawid A.P. and Smith A.F.M. (eds.),Bayesian Statistics 6. Oxford University Press, 503–524.

  • Pang A.T., Wittenbrink C.M. and Lodha S.K. (1996). Approaches to uncertainty visualisation. Technical report UCSC-CRL-96-21, Computer Science Department, University of California at Santa Cruz. http://www.cse.ucsc.edu/research/slvg/uncertainty.htm.

  • Phillips L.D. (1982). Requisite decision modelling: a case study.Journal of the Operational Research Society 33, 303–311.

    Article  Google Scholar 

  • Phillips L.D. (1984). A theory of requisite decision models.Acta Psychologica 56, 29–48.

    Article  Google Scholar 

  • Prado R. and Bolado R. (2001).SAMO 2001: Third international Symposium on Sensitivity Analysis of Model Output. CIEMAT, Spain.

    Google Scholar 

  • Renn O., Webler T., Wiedermann P. (1995).Fairness and Competence in Citizen Participation: Evaluating Models and Environmental Discourse. Kluwer Academic Publishers.

  • Rios Insua D. (1990). Sensitivity Analysis in Multi-objective Decision Making.Lecture Notes in Economics and Mathematical Systems 347. Springer Verlag.

  • Rios Insua D. (1999). Special Issue on Sensitivity Analysis in MCDA.Journal of Multi Criteria Decision Analysis 8.

  • Rios Insua D. and French S. (1991). A framework for sensitivity analysis in discrete multi-objective decision making.European Journal of Operational Research. 54, 176–190.

    Article  Google Scholar 

  • Rios Insua D. and Ruggeri F. (2000). Robust Bayesian Analysis.Lecture Notes in Statistics. Springer Verlag.

  • Rios Insua D., Ruggeri F. and Martin J. (1998). Bayesian Sensitivity Analysis: a Review. In: Chan K., Tarantola S. and Campolongo F. (eds.),SAMO: Second International Symposium on Sensitivity Analysis in Model Output, EU Joint research Centre, 239–241.

  • Saltelli A., Tarantola S. and Chan K. (1999). A role for sensitivity analysis in presenting the results from MCDA to decision makers.Journal of Multi Criteria Decision Analysis 8, 139–140.

    Article  Google Scholar 

  • Saltelli A., Chan K. and Scott E.M. (eds.) (2000).Sensitivity Analysis. John Wiley.

  • Saltelli A., Tarantola S. and Campolongo F. (2001). Sensitivity analysis as an ingredient in modelling.Statistical Science 15, 377–395.

    Google Scholar 

  • Savage L.J. (1972).The Foundations of Statistics (second Edition). Dover.

  • Spielgelhalter D.J., Aylin P., Best N.G., Evans S.J.W. and Murray G.D. (2001). Commissioned analysis of surgical performance by using routine data: lessons from the Bristol Inquiry (with discussion).Journal of the Royal Statistical Society, Series A 165, 191–231.

    Google Scholar 

  • von Winterfeldt D. and Edwards W. (1986).Decision Analysis and Behavioural Research. Cambridge University Press.

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  1. Manchester Business School, The University of Manchester, Booth Street West, M15 6PB, Manchester, UK

    Simon French

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French, S. Modelling, making inferences and making decisions: The roles of sensitivity analysis. Top 11, 229–251 (2003). https://doi.org/10.1007/BF02579043

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  • DOI: https://doi.org/10.1007/BF02579043

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