Research Papers

Prospect Theory-based Real Options Analysis for Noncommercial Assets

[+] Author and Article Information
Joshua T. Knight

Department of Naval Architecture and Marine Engineering,
University of Michigan, Ann Arbor, MI 48109e-mail: jtknight@umich.edu

David J. Singer

Assistant Professor Department of Naval Architecture and Marine Engineering,
University of Michigan, Ann Arbor, MI 48109

1Corresponding author.

Manuscript received April 3, 2014; final manuscript received November 19, 2014; published online February 27, 2015. Assoc. Editor: Bilal M. Ayyub.

ASME J. Risk Uncertainty Part B 1(1), 011004 (Feb 27, 2015) (9 pages) Paper No: RISK-14-1007; doi: 10.1115/1.4026398 History: Received April 03, 2014; Accepted December 03, 2014; Online February 27, 2015

When an engineering system has the ability to change or adapt based on a future choice, then flexibility can become an important component of that system’s total value. However, evaluating noncommercial flexible systems, like those in the defense sector, presents many challenges because of their dynamic nature. Designers intuitively understand the importance of flexibility to hedge against uncertainties. In the naval domain, however, they often do not have the tools needed for analysis. Thus, decisions often rely on engineering experience. As the dynamic nature of missions and new technological opportunities push the limits of current experience, a more rigorous approach is needed. This paper describes a novel framework for evaluating flexibility in noncommercial engineering systems called prospect theory-based real options analysis (PB-ROA). While this research is motivated by the unique needs of the U.S. Navy ship design community, the framework abstracts the principles of real options analysis to suit noncommercial assets that do not generate cash flows. One contribution of PB-ROA is a systematic method for adjusting agent decisions according to their risk tolerances. The paper demonstrates how the potential for loss can dramatically affect decision making through a simplified case study of a multimission variant of a theoretical high-speed connector vessel.

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Trigeorgis, L., 1995, “Real Options: An Overview,” Real Options in Capital Investment: Models, Strategies, and Applications, L. Trigeorgis, ed., Praeger Publishers, Westport, CT, pp. 1–28.
Parker, M., and Singer, D. J., 2012, “Flexibility and Modularity in Ship Design: An Analytical Approach,” Proceedings of the 11th International Marine Design Conference, Vol. 1, University of Strathclyde, Glasgow, Scotland, pp. 385–396.
Koenig, P., 2009, Real Options in Ship and Force Structure Analysis: A Research Agenda, American Society of Naval Engineers, ANSE Day, National Harbor, MD.
Gregor, J. A., 2003, “Real Options for Naval Ship Design and Acquisition: A Method for Valuing Flexibility under Uncertainty,” Master’s thesis, Massachusetts Institute of Technology, Cambridge, MA.
Page, J., 2011, “Flexibility in Early Stage Design of US Navy Ships: An Analysis of Options,” Master’s thesis, Massachusetts Institute of Technology, Cambridge, MA.
Gonzalez-Zugasti, J., Otto, K., and Whitcomb, C., 2007, “Options-Based Multi-Objective Evaluation of Product Platforms,” Naval Eng. J., 119(3), pp. 89–106. 10.1111/j.1559-3584.2007.00070.x
Koenig, P., Nalchajian, D., and Hootman, J., 2008, Ship Service Life and Naval Force Structure, American Society of Naval Engineers, ANSE Day; Engineering the Total Ship, Tysons Corner, VA.
Koenig, P., Czapiewski, P., and Hootman, J., 2008, “Synthesis and Analysis of Future Naval Fleets?” Ships Offshore Struct., 3(2), pp. 81–89. [CrossRef]
Wang, T., and de Neufville, R., 2005, “Real Options in Projects,” 9th Real Options Annual International Conference, Paris, France.
Wang, T., and de Neufville, R., 2006, “Identification of Real Options in Projects,” 4th Conference on Systems Engineering Research, Los Angeles, CA.
Engel, A., and Browning, T. R., 2008, “Designing Systems for Adaptability by Means of Architecture Options,” Syst. Eng., 11, pp. 125–146. 10.1002/(ISSN)1520-6858
Martin, I. F., 2008, Valuation of Design Adaptability in Aerospace Systems, Ph.D. thesis, Georgia Institute of Technology, Atlanta, GA.
Davis, M. H., 2009, “Option Pricing in Incomplete Markets,” Mathematics of Derivative Securities, M. Dempster, and S. Pliska, eds., Cambridge University Press, New York, pp. 216–226.
Beja, A., 1967, “Capital Markets with Delayed ‘Learning’,” Ph.D. thesis, Stanford University, Stanford, CA.
Nau, R. F., and McCardle, K. F., 1991, “Arbitrage, Rationality, and Equilibrium,” Theory Decis., 31, pp. 199–240. [CrossRef]
Hugonnier, J., Kramkov, D., and Schachermayer, W., 2005, “On Utility-Based Pricing of Contingent Claims in Incomplete Markets,” Math. Finance, 15(2), pp. 203–212. [CrossRef]
Kahneman, D., and Tversky, A., 1979, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica, 47(2), pp. 263–291. 10.2307/1914185
Kahneman, D., and Tversky, A., 1984, “Choices, Values, and Frames,” Am. Psychologist, 39(4), pp. 341–350. 10.1037/0003-066X.39.4.341
Ben-Haim, Y., 2001, Information-Gap Decision Theory: Decisions Under Severe Uncertainty, Academic Press, Oxford, UK.
Fama, E. F., 2000, “Efficient Capital Markets: A Review of Theory and Empirical Work,” J. Finance, 25(2), pp. 383–417. [CrossRef]
Kifer, Y., 2000, “Game Options,” Finance Stochastics, 4, pp. 443–463. [CrossRef]
Smit, H. T., and Ankum, L., 1993, “A Real Options and Game-Theoretic Approach to Corporate Investment Strategy Under Competition,” Financial Manage., 22(3), pp. 241–250. [CrossRef]
Lukas, E., Reuer, J. J., and Welling, A., 2012, “Earnouts in Mergers and Acquisitions: A Game-Theoretic Option Pricing Approach,” Euro. J. Operational Res., (223), pp. 256–263.
Villani, G., 2008, “An R&D Investment Game Under Uncertainty in Real Option Analysis,” Comput. Econ., 32, pp. 199–219. [CrossRef]
Smit, H. T., 2003, “Infrastructure Investment as a Real Options Game: The Case of European Airport Expansion,” Financial Manage., 32(4), pp. 27–57. [CrossRef]
Smit, H. T., and Trigeorgis, L., 2006, “Real Options and Games: Competition, Alliances and Other Applications of Valuation and Strategy,” Rev. Financial Econ., 15, pp. 95–112. [CrossRef]
Smit, H. T., and Trigeorgis, L., 2007, “Strategic Options and Games in Analysing Dynamic Technology Investments,” Long Range Plann., 40, pp. 84–114. [CrossRef]
Rigterink, D., Collette, M., and Singer, D. J., 2012, “A Novel Structural Complexity Metric and its Impact on Structural Cost Estimating,” Proceedings of the 11th International Marine Design Conference, Vol. 2, University of Strathclyde, Glasgow, Scotland, pp. 535–544.
Whitcomb, C., 1998, “Naval Ship Design Philosophy Implementation,” Naval Eng. J., 110(1), pp. 49–63. 10.1111/nej.1998.110.issue-1
Abdellaoui, M., 2000, “Parameter-Free Elicitation of Utility and Probability Weighting Functions,” Manage. Sci., 46(11), pp. 1497–1512. 10.1287/mnsc.46.11.1497.12080
Abdellaoui, M., Bleichrodt, H., and Haridon, O. L., 2008, “A Tractable Method to Measure Utility and Loss Aversion Under Prospect Theory,” J. Risk Uncertainty, 36(3), pp. 245–266. 10.1007/s11166-008-9039-8
Yu, J.-C., and Ishii, K., 1998, “Design for Robustness Based on Manufacturing Variation Patterns,” J. Mech. Design, 120(2), pp. 196–202. [CrossRef]


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Fig. 1

Barriers to transitioning real options analysis to naval applications and the theoretical means to overcome them

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Fig. 2

Hypothetical weighting function in prospect theory, (taken from Ref. [17], p. 283)

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Fig. 3

Process flowchart for PB-ROA framework

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Fig. 4

Status quo game with suboptimal Nash equilibrium

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Fig. 5

Game with option: game now has optimal Nash equilibrium

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Fig. 6

Utility function for HSC hospital design alternatives

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Fig. 7

Expected utility of HSC fleet for the medical mission; n=0, m=1

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Fig. 8

Variation in probability decision threshold, α*, with fleet size using PB-ROA

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Fig. 9

Comparison of the variation in probability decision threshold, α*, with fleet size between PB-ROA and an expected utility approach, for n=0

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Fig. 10

Variation in probability decision threshold using PB-ROA (left), and expected utility theory (right), under the “all-or-nothing” assumption

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Fig. 11

Variation in probability decision threshold over m for PB-ROA and expected utility methods, n=0

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Fig. 12

A hypothetical decision weight in prospect theory, from [17] (left) and the risk-adjusted measure, q, for variant 2 (n=0, m=1) from PB-ROA (right)




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