Modeling for decision making under uncertainty in energy and U.S. foreign policy
Abstract/Contents
- Abstract
- Mathematical models of the energy system are very successful tools for domestic policy analysis. However, these models have not been widely used to support decision making at the intersection of energy and U.S. foreign policy. This dissertation argues energy modeling must adapted to be relevant in this unique context including making greater use of uncertainty analysis. Criteria are identified to contrast four approaches to uncertainty analysis: predictive scenario analysis, Monte Carlo analysis, decision analysis, and exploratory modeling and analysis. Using an optimization and simulation model of the energy system, each approach to uncertainty analysis is used to analyze a current U.S. foreign policy problem: Should the United States provide incentives to promote natural gas in the electricity mix of low income countries? The results of the analysis are used to make a recommendation about U.S. policy and about the approach to uncertainty analysis that is most appropriate for energy and foreign policy decision making. Based on the approaches contrasted, decision analysis and exploratory modeling and analysis used together are found to have the greatest potential as a tool for energy and foreign policy analysis. When combined, these approaches have the most suitable implications for encoding uncertain variables, conducting broad policy search, quickly providing updated results, producing results that can be correctly interpreted by decision makers to provide direction and intuition, and directing the collection and integration of new information. In combination these approaches shift the burden of probabilistic reasoning away from the decision maker, while providing insights into the energy system that a decision maker can integrate with other types of policy judgment.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Culver, Lauren C | |
|---|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering. | |
| Primary advisor | Weyant, John P. (John Peter) | |
| Thesis advisor | Weyant, John P. (John Peter) | |
| Thesis advisor | Blacker, Coit D | |
| Thesis advisor | Zoback, Mark D | |
| Advisor | Blacker, Coit D | |
| Advisor | Zoback, Mark D |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Lauren C. Culver. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Lauren Claire Culver
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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