Prospect Theory-based Real Options Analysis for Noncommercial Assets

Author(s):  
Joshua T. Knight ◽  
David J. Singer

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.

2021 ◽  
Vol 1 ◽  
pp. 3121-3130
Author(s):  
Cesare Caputo ◽  
Michel-Alexandre Cardin

AbstractFlexibility analysis helps improve the expected value of engineering systems under uncertainty (economic and/or social). Designing for flexibility, however, can be challenging as a large number of design variables, parameters, uncertainty drivers, decision making possibilities and metrics must be considered. Many available techniques either rely on assumptions that are not suitable for an engineering setting, or may be limited due to computational intractability. This paper makes the case for an increased integration of Machine Learning into flexibility and real options analysis in engineering systems design to complement existing design methods. Several synergies are found and discussed critically between the fields in order to explore better solutions that may exist by analyzing the data, which may not be intuitive to domain experts. Reinforcement Learning is particularly promising as a result of the theoretical common grounds with latest methodological developments e.g. decision-rule based real options analysis. Relevance to the field of computational creativity is examined, and potential avenues for further research are identified. The proposed concepts are illustrated through the design of an example infrastructure system.


2010 ◽  
Vol 13 (7) ◽  
pp. A280
Author(s):  
J Grutters ◽  
K Abrams ◽  
D Deruysscher ◽  
P Lambin ◽  
M Pijls-Johannesma ◽  
...  

Energy ◽  
2015 ◽  
Vol 80 ◽  
pp. 41-54 ◽  
Author(s):  
Giorgio Locatelli ◽  
Sara Boarin ◽  
Francesco Pellegrino ◽  
Marco E. Ricotti

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