Classification and Moral Evaluation of Uncertainties in Engineering Modeling
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Engineers must deal with risks and uncertainties as a part of their professional work and, in particular, uncertainties are inherent to engineering models. Models play a central role in engineering. Models often represent an abstract and idealized version of the mathematical properties of a target. Using models, engineers can investigate and acquire understanding of how an object or phenomenon will perform under specified conditions. This paper defines the different stages of the modeling process in engineering, classifies the various sources of uncertainty that arise in each stage, and discusses the categories into which these uncertainties fall. The paper then considers the way uncertainty and modeling are approached in science and the criteria for evaluating scientific hypotheses, in order to highlight the very different criteria appropriate for the development of models and the treatment of the inherent uncertainties in engineering. Finally, the paper puts forward nine guidelines for the treatment of uncertainty in engineering modeling.
KeywordsModeling Risk Uncertainty Engineering Science
- ASCE task committee to achieve the vision for civil engineering in 2025 (2009). Achieve the vision for civil engineering in 2025: A roadmap for the profession. http://content.asce.org/files/pdf/Vision2025RoadmapReport_ASCE_Aug2009.pdf. Accessed 11 Jun 2010.
- Baker, A. (2010). Simplicity. stanford encyclopedia of philosophy. http://plato.stanford.edu/entries/simplicity/. Accessed 11 Jun 2010.
- Cranor, C. F. (2007). Toward a non-consequentialist approach to acceptable risks. In T. Lewens (Ed.), Risk: Philosophical perspectives (pp. 36–55). New York: Routledge.Google Scholar
- Dusek, V. (2006). Philosophy of technology: An introduction. Madden, MA: Blackwell.Google Scholar
- Frigg, R., and Hartmann, S. (2006). Models in science. Stanford encyclopedia of philosophy. http://plato.stanford.edu/entries/models-science/#SetStr. Accessed 10 Jun 2010.
- Fung, Y. C., & Tong, P. (2001). Classical and computational solid mechanics. Singapore: World Scientific.Google Scholar
- Gould, D.K. (2003). The scientific model concept and realism. A Master of Arts Thesis. Texas A&M University.Google Scholar
- Hacking, I. (1975). The emergence of probability: A philosophical study of early ideas about probability, induction and statistical inference. New York: Cambridge University Press, London.Google Scholar
- Hacking, I. (1984). Experimentation and scientific realism. In J. Leplin (Ed.), Scientific realism (pp. 154–172). Berkeley: University of California Press.Google Scholar
- Harris, C. E., Jr., Pritchard, M. S., & Rabins, M. J. (2009). Engineering ethics: Concepts and cases (4th edition ed.). Belmont, CA: Wadsworth.Google Scholar
- Hempel, C. G. (1966). Philosophy of natural science. Englewood Cliffs, NJ: Pretice.Google Scholar
- Hesse, M. (1963). Models and analogies in science. London: Sheed and Ward.Google Scholar
- Murphy, C. and Gardoni, P. (2010). Evaluating the source of the risks associated with natural events. Res Publica, revised and resubmitted.Google Scholar
- Parsons, K. (2006). Copernican questions. New York: McGraw-Hill.Google Scholar
- Reid, R. L. (2009). Guiding critical infrastructure. ASCE Civil Engineering Magazine, 79(2), 50–55.Google Scholar
- Ryle, G. (1949). Concept of mind. New York: Barnes and Noble.Google Scholar