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Risk attitudes in risk-based design: Considering risk attitude using utility theory in risk-based design

Published online by Cambridge University Press:  02 November 2012

Douglas Van Bossuyt*
Affiliation:
Complex Engineered Systems Design Laboratory, School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
Chris Hoyle
Affiliation:
Complex Engineered Systems Design Laboratory, School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
Irem Y. Tumer
Affiliation:
Complex Engineered Systems Design Laboratory, School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
Andy Dong
Affiliation:
Faculty of Engineering and Information Technologies, University of Sydney, Sydney, Australia
*
Reprint requests to: Douglas Van Bossuyt, Complex Engineered Systems Design Laboratory, School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, 204 Rogers Hall, Corvallis, OR 97331, USA; E-mail: douglas.vanbossuyt@gmail.com

Abstract

Engineering risk methods and tools account for and make decisions about risk using an expected-value approach. Psychological research has shown that stakeholders and decision makers hold domain-specific risk attitudes that often vary between individuals and between enterprises. Moreover, certain companies and industries (e.g., the nuclear power industry and aerospace corporations) are very risk-averse whereas other organizations and industrial sectors (e.g., IDEO, located in the innovation and design sector) are risk tolerant and actually thrive by making risky decisions. Engineering risk methods such as failure modes and effects analysis, fault tree analysis, and others are not equipped to help stakeholders make decisions under risk-tolerant or risk-averse decision-making conditions. This article presents a novel method for translating engineering risk data from the expected-value domain into a risk appetite corrected domain using utility functions derived from the psychometric Engineering Domain-Specific Risk-Taking test results under a single-criterion decision-based design approach. The method is aspirational rather than predictive in nature through the use of a psychometric test rather than lottery methods to generate utility functions. Using this method, decisions can be made based upon risk appetite corrected risk data. We discuss development and application of the method based upon a simplified space mission design in a collaborative design-center environment. The method is shown to change risk-based decisions in certain situations where a risk-averse or risk-tolerant decision maker would likely choose differently than the expected-value approach dictates.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Arrow, K.J. (1950). A difficulty in the concept of social welfare. Journal of Political Economy 58(4), 328346.CrossRefGoogle Scholar
Bernoulli, D. (1954). Exposition of a new theory on the measurement of risk. Econometrica 22(1), 2336.CrossRefGoogle Scholar
Bindschadler, D.L., Theilig, E.E., Schimmels, K.A., & Vandermey, N. (2003). Project Galileo: Final Mission Status (Technical Report). Pasadena, CA: Jet Propulsion Laboratory.Google Scholar
Cooper, A.C., Woo, C.Y., & Dunkelberg, W.C. (1988). Entrepreneurs' perceived chances for success. Journal of Business Venturing 3, 97108.CrossRefGoogle Scholar
Dong, H., & Wood, W. (2004). Integrating computational synthesis and decision-based conceptual design. Proc. ASME 2004 Int. Design Engineering Technology Conf. Computers in Information & Engineering Conf., Paper No. IDETC/CIE2004, pp. 361–371. Salt Lake City, UT: ASME.CrossRefGoogle Scholar
Du, X., & Chen, W. (2000). Towards a better understanding of modeling feasibility robustness in engineering design. ASME Journal of Mechanical Design 122(4), 385394.CrossRefGoogle Scholar
Dvir, R., & Pasher, E. (2004). Innovation engines for knowledge cities: an innovation ecology perspective. Journal of Knowledge Management 8(5), 1627.CrossRefGoogle Scholar
Federal Aviation Administration. (2006). National airspace system system engineering manual (3rd ed.). Washington, DC: Federal Aviation Administration, ATO Operations Planning.Google Scholar
Gerber, A. (2002). Super Nova-Acc Probe (SNAP) (Technical report). Pasadena, CA: National Aeronautics & Space Administration.Google Scholar
Grantham-Lough, K., Stone, R., & Tumer, I.Y. (2007). The risk in early design method. Journal of Engineering Design 20, 155173.CrossRefGoogle Scholar
Hazelrigg, G.A. (1996). The implications of arrow's impossibility theorem on approaches to optimal engineering design. Journal of Mechanical Design 118(2), 161164.CrossRefGoogle Scholar
Hazelrigg, G.A. (1998). A framework for decision-based engineering design. Journal of Mechanical Design 120(4), 653659.CrossRefGoogle Scholar
Hillson, D., & Murray-Webster, R. (2007). Understanding and Managing Risk Attitude. Aldershot: Gower.Google Scholar
Howard, R.A. (1988). Decision analysis: practice and promise. Management Science 34, 679695.CrossRefGoogle Scholar
Hoyle, C., Tumer, I.Y., Mehr, A.F., & Chen, W. (2009). Health management allocation for conceptual system design. Journal of Computing and Information Sciences in Engineering 9(2).Google Scholar
Hubbard, D. (2007). How to Measure Anything: Finding the Value of Intangibles in Business. Hoboken, NJ: Wiley.Google Scholar
IEEE. (1990). IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. New York: IEEE.Google Scholar
International Electrotechnical Commission. (1990). International Standard IEC 61025 Fault Tree Analysis. Geneva: International Electrotechnical Commission.Google Scholar
International Organization for Standardization. (1997). ISO 10628: Flow Diagrams for Process Plants: General Rules. Geneva: International Organization for Standardization.Google Scholar
Ji, H., Yang, M.C., & Honda, T. (2007). A probabilistic approach for extracting design preferences from design team discussion. ASME 2007 Int. Design Engineering Technical Conf. Computers & Information in Engineering Conf., Paper No. IDETC/CIE2007, pp. 297–306. Las Vegas, NV: ASME.CrossRefGoogle Scholar
Jones, J.A. (2005). An Introduction to Factor Analysis of Information Risk. New York: Risk Management Insight.Google Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263291.CrossRefGoogle Scholar
Keeney, R.L., & Raiffa, H. (1993). Decisions With Multiple Objectives: Preferences and Value Tradeoffs. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kirkwood, C.W. (1997, January). Notes on Attitude Toward Risk Taking and the Exponential Utility Function. Tempe, AZ: Arizona State University, Department of Management.Google Scholar
Kurtoglu, T., & Tumer, I.Y. (2008). A graph-based fault identification and propagation framework for functional design of complex systems. Journal of Mechanical Design 30(5).Google Scholar
Lewis, K., Chen, W., & Schmidt, E.L. (2006). Decision Making in Engineering Design. New York: ASME Press.CrossRefGoogle Scholar
MacCrimmon, K., & Wehrung, D.A. (1990). Characteristics of risk taking executives. Management Science 36, 422435.CrossRefGoogle Scholar
MacCrimmon, K.R., & Wehrung, D.A. (1986). Taking Risks: The Management of Uncertainty. New York: Free Press.Google Scholar
Martin, J.D., & Simpson, T.W. (2006). A methodology to manage system-level uncertainty during conceptual design. Journal of Mechanical Design 128, 959968.CrossRefGoogle Scholar
McNamee, P., & Celona, J. (1990). Decision Analysis With Supertree (2nd ed.). San Francisco, CA: Scientific Press.Google Scholar
Meshkat, L. (2007). A holistic approach for risk management during design. Proc. Aerospace Conf., IEEE, 2007, pp. 1–5.CrossRefGoogle Scholar
NASA. (1995). NASA Systems Engineering Handbook. Pasadena, CA: NASA.Google Scholar
Oberto, R.E., Nilsen, E., Cohen, R., Wheeler, R., DeFlorio, P., & Borden, C. (2005). The NASA exploration design team: blueprint for a new design paradigm. Proc. 2005 Aerospace Conf., pp. 4398–4405.CrossRefGoogle Scholar
Papalambros, P.Y., & Wilde, D.J. (2000). Principles of Optimal Design: Modeling and Computation. New York: Cambridge University Press.CrossRefGoogle Scholar
Pennings, J.M.E., & Smidts, A. (2000). Assessing the construct validity of risk attitude. Management Science 46(10), 13371348.CrossRefGoogle Scholar
Pratt, J.W. (1964). Risk aversion in the small and in the large. Econometrica 32, 122136.CrossRefGoogle Scholar
Ross, A.M., Hastings, D.E., Warmkessel, J.M., & Diller, N.P. (2004). Multi-attribute tradespace exploration as front end for effective space system design. Journal of Spacecraft and Rockets 41(1), 2029.CrossRefGoogle Scholar
Russell, J.S., & Skibniewski, M.J. (1988). Decision criteria in contractor prequalification. Journal of Management in Engineering 4(2), 148164.CrossRefGoogle Scholar
Schoemaker, P.J.H. (1990). Are risk-preferences related across payoff domains and response modes? Management Science 36, 14511463.CrossRefGoogle Scholar
Shah, J.J., & Wright, P.K. (2000). Developing theoretical foundations of DFM. Proc. ASME 2000 Int. Design Engineering Technology Conf. Computers in Information & Engineering Conf., Paper No. IDETC/CIE2000. New York: ASME.Google Scholar
Slovic, P. (1964). Assessment of risk taking behavior. Psychological Bulletin 61, 330333.CrossRefGoogle ScholarPubMed
Stamanis, D.H. (2003). Failure Modes and Effects Analysis: FMEA From Theory to Execution (2nd ed.). Milwaukee, WI: ASQ Quality Press.Google Scholar
Standards Australia New Zealand. (2009). AS/NZS ISO 31000:2009 Risk management: Principles and Guidelines. Sydney: Standards Australia New Zealand.Google Scholar
Stone, R.B., Tumer, I.Y., & Van Wie, M. (2005). The function–failure design method. Journal of Mechanical Design 127(3), 397407.CrossRefGoogle Scholar
Stump, G.M., Lego, S., Yukish, M., Simpson, T.W., & Donndelinger, J.A. (2009). Visual steering commands for trade space exploration: user-guided sampling with example. Journal of Computing and Information Science in Engineering 9(4), 110.CrossRefGoogle Scholar
Ullman, D. (2009). Accord [Computer software]. Portland, OR: Robust Decisions Inc.Google Scholar
US Department of Defense. (1980). Procedures for Performing Failure Mode, Effects, and Criticality Analysis. Military Standard MIL-STD-1629A. Washington, DC: US Department of Defense.Google Scholar
Van Bossuyt, D., Carvalho, L., Dong, A., & Tumer, I.Y. (2011). On measuring engineering risk attitudes. ASME 2011 Int. Design Engineering Technical Conf. Computers & Information in Engineering Conf., Paper No. IDETC/CIE2011, pp. 425–434. Washington, DC: ASME.CrossRefGoogle Scholar
Van Bossuyt, D.L., & Tumer, I.Y. (2010). Toward understanding collaborative design center trade study software upgrade and migration risks. Proc. ASME 2010 Int. Mechanical Engineering Congr. Exposition, Paper No. IMECE2010, pp. 315–328. Vancouver: ASME.CrossRefGoogle Scholar
Van Bossuyt, D.L., Wall, S., & Tumer, I. (2010). Towards risk as a tradeable parameters in complex systems design trades. Proc. ASME 2010 Int. Design Engineering Technology Conf. Computers in Information & Engineering Conf., Paper No. IDETC/CIE2010, pp. 1271–1286. Montreal: ASME.CrossRefGoogle Scholar
Villemeur, A. (2000). Reliability, Availability, Maintainability, and Safety Assessment. New York: Wiley.Google Scholar
von Winterfeldt, D., & Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge: Cambridge University Press.Google Scholar
Wassenaar, H.J., & Chen, W. (2003). An approach to decision-based design with discrete choice analysis for demand modeling. Journal of Mechanical Design 125(3), 490497.CrossRefGoogle Scholar
Weber, E.U., Blais, A.R., & Betz, N.E. (2002). A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors. Journal of Behavioral Decision Making 15(4), 263290.CrossRefGoogle Scholar
Wertz, J.R., & Larson, W.J. (Eds.). (1999). Space Mission Analysis and Design. London: Springer.Google Scholar