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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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References

Figures

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Fig. 3

Process flowchart for PB-ROA framework

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Fig. 2

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

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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. 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. 11

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

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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. 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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