The Real Option method allows infrastructure owners to evaluate the advantage of options that an infrastructure manager has over time. As time passes, a manager will have the ability to intervene as as an object may deteriorate at a faster rate than expected. Likewise, a manager may postpone a planned intervention if the condition is better than expected. In addition to the option to defer, a manager may have the option to expand or contract the infrastructure or the infrastructure network, as well as to shut it down temporarily, abandon it, grow it or switch it (de Neufville and Scholtes, 2011).
The options provide an owner with the flexibility adapting the infrastructure to uncertain future needs. Owners, thus, neither under-, nor overinvest and consequently minimize the risks of their decisions. The external factors affecting risk include weather events, condition development, system demands, funding and other critical variables. The methodology proposes a way to systematically analyse and define these uncertainties and make predictions taking the defined uncertainty fully into consideration.
Real option valuation is known using binomial lattices (a form of decision trees) and/or Brownian motion random walk algorithms. Infrastructure life-time net benefits can also be calculated by simulating the uncertainty using continuous Monte Carlo simulations. Using different stakeholders’ costs of different design alternatives and management strategies, the costs can be calculated over a large sample of potential futures. The methodology is able to address multiple levels of risk and weight them as necessary and thus make multi-objective, cross-asset investment decisions under uncertainty to best support the national goals identified in 23 USC 150(b).
The ultimate objective is to provide the decision-maker with tools that add value to the decision-making process and improve the robustness of the infrastructure network as a whole. In that sense, novel approaches for the evaluation of risk will be sought to capture the stochastic nature of interdependent infrastructure. A graph theory approach to evaluate criticality of network node failure as shown by Buldyrev and colleagues (2010) may prove interesting for the evaluation of consequences, and thus the real option value for the infrastructure, simulated by network programming methods.
The application and evaluation of a large sample of data and data simulations is computationally challenging. Furthermore, decision-making tools are urged to be simple and understandable. As big data may improve predictability and performance of models, strong emphasis must be laid on the usability of such models. In this project, it is suggested that particular focus will be on addressing these challenges with the outlook of combining big data and the model’s user interface design.
Buldyrev, S. V., R. Parshani, G. Paul, H. E. Stanley and S. Havlin (2010) Catastrophic cascade of failures in interdependent networks, Nature, 464, 1025-1028.
de Neufville, R. and S. Scholtes (2011) Flexibility in Engineering Design, Engineering Systems, MIT Press, ISBN 978-0262297332.
Savage, S. (2012) The Flaw of Averages: Why we underestimate Risk in the face of Uncertainty, Wiley, ISBN 978-1118073759.
Prof. Dr. Rade Hajdin, July 2019