Economics of Adaptation: Generalized Optimal Switching
Open Access
- Author:
- Choi, Byunghee
- Graduate Program:
- Agricultural, Environmental and Regional Economics
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 20, 2020
- Committee Members:
- Robert D Weaver, Dissertation Advisor/Co-Advisor
Robert D Weaver, Committee Chair/Co-Chair
Terry Lee Friesz, Committee Member
Stephan J Goetz, Committee Member
Vinayak V Shanbhag, Outside Member
Edward C Jaenicke, Program Head/Chair - Keywords:
- Adaptation
Optimal switching
Risk
Uncertainty
Robustness
Bioenergy - Abstract:
- Valuing flexibility in real options is the beginning of the research and this dissertation. Translated the core problem is valuation of capital budgeting investment problems that allow for switching across a set of investment and operating decisions. Typically, these problems are faced in a variety of economic applications which include multiple stochastic state variables, multiple investment and operating decisions, and different types of risks and/or uncertainties. After reviewing the current literature on real options and optimal switching in the first essay with some implications, a unified framework is suggested to reflect the real options aspects within the generalized optimal switching model suggested by Brekke and Oksendal (1994). The idea is to transform the optimal switching framework into a sequence of multiple but simple optimal stopping problems and directly demonstrates that the corresponding optimal switching policies can be described as switching boundaries. Main contribution is a new method of numerical solution for the generalized optimal switching problems which contains the following novel features: (i) A number of stochastic state variables can be incorporated with possibilities of infrequent jumps and extreme events; (ii) a number of modes can be built into the system with possibilities of reversible decisions, and (iii) different types of risks including operational constraints can be incorporated. The third essay studies the ``robust'' optimal switching problem in the presence of uncertainty on probabilistic measures. This implies that decision-makers have limited subjective prior knowledge of the underlying stochastic processes. Following the theory of convex measures of preference on uncertainty suggested in Riedel (2009), a numerically implementable approach is suggested to solve the robust optimal switching model. The key to the method is to suitably extend and exploit duality theory so that the extended version of dynamic programming formulation can be achieved. Moreover, the robust model works in a generalized setting, allowing for time-consistent convex measure of risk, stochastic system state processes which can incorporate extreme events or infrequent jumps, and a number of available decision modes with reversibility. Compared to the standard optimal switching model, the robust switching boundaries make the hysteresis band wider. This implies in the economic point of view that decision-makers prefer maintaining current decision to switching into the alternative when their decisions are under uncertainty. In the final essay, a farmer's decision problem of switching between field crop production and bioenergy crop production is studied by using standard and robust optimal switching frameworks. Several features in the problem, i.e. switching costs, uncertainties in stochastic crop returns, and operational constraints of perennial species, justify the application of optimal switching built upon a real option model. The optimal switching decision rule is illustrated as switching boundaries, is shown to differ significantly from the switching policy derived from the classical net present value approach. In addition, the robust switching boundaries define a significantly wider hysteresis band compared to the band generated from the standard optimal switching boundaries. These results imply that the farmer is reluctant to change land use under a situation in which the stochastic features of system process are partially informed to the farmer. Such results presented provide one rationale for low adoption of these crops. These results differ from those presented in previous literature such as Thomson et al. (2009). Further, they provide a support for recent waiver of U.S. policy mandate to promote bioenergy production was due to the low adoption rates of bioenergy crop production by farmers.