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The methodology explored in this thesis has several limitations; decision results should thus be used carefully in the context of a deliberative-analytic process, as a guide and source of information rather than as direct prescription for action. Several of the most important limitations to the methodology are discussed here, with suggestions for future work to understand and alleviate them.

On major limitation of the work presented in Chapters 5, 6, and 7 is that it only considers two system objectives. The nuclear fuel cycle, like many other complex systems, involves more than two system aspects that must be considered and traded off in making decisions about system evolution. This thesis does not consider proliferation resistance or nuclear safety, despite the fact

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that these are two very important issues for stewards of nuclear power. In principle, further work could continue this analysis and incorporate the full complement of system objectives.

Presentation and interpretation of the results, however, would be extremely difficult. Two- and three-dimensional graphs would only show part of the picture, and unpacking the drivers for decision space shapes would be made significantly more complicated with even a few more parameters. Alternatively, the analysis could be done individually with one attribute traded off against another for all possible pairs, but this would be time-consuming and results could be difficult to compare. The methodology might thus be best suited to two-objective systems.

Another limitation of this work is that only bounding point estimates are considered for the uncertainties and decision options. This is likely of greatest importance for the decision alternatives, which are modeled as stylized and discretized versions of the actual options. Future work should relax the discretization assumption, evaluating continuous portfolios of reactor build options. Follow-on studies might also consider full distributions for uncertain parameter values in place of point estimates.

A related limitation of the methodology is that any decision alternatives that are not modeled are not assessed. This is potentially problematic because there may be other decision options that would dominate the choices considered, and this methodology does not help identify what they might be or whether they exist. One example includes the possibility that the best strategy would involve all three options, where traditional fast reactors mitigate some of the existing LWR waste and then the system transitions to exclusively include enriched-uranium fed reactors. The decision options follow the same rigid structure as those of traditional fuel cycle models, where the type of reactor and date of introduction has to be specified before running a scenario. Future work could help alleviate this limitation in two ways. The simplest solution might be to employ creativity and brainstorming tools with a decision-making group in order to identify a wide range of decision options, and then to evaluate them all. Another option includes building a much more complex fuel cycle (or other system) model, upon which e.g. the

introduction date for a reactor type could be optimized or decisions made endogenously. With the latter approach, the basic structure of a decision alternative (reactor type, date of

introduction, date(s) of change in strategy), would be fixed. Both directions for further research could yield insights.

159 Another potential limitation of this approach is that it relies heavily on the accuracy of the system dynamics model. This is a challenge often described colloquially as “garbage in, garbage out,” indicating that unless the underlying system model adequately represents system impacts, the decision model is useless. In this thesis, the underlying fuel cycle model FANTSY was checked extensively with widely used fuel cycle codes (see Appendix B), but the decision results should nevertheless be confirmed with one of the more sophisticated fuel cycle models. Future analysts should be aware of the importance of system modeling accuracy.

Decision analysis heavily emphasizes the relative values of scenario outcomes (e.g. one scenario scores higher than another) without considering differences in magnitude between scenario scores. Relative scenario magnitude information is used (but not shown) by TreeAge when it calculates and draws the solid-color decision spaces. In general, close to the dividing lines between decisions, scenario outcomes are closer in magnitude than elsewhere on the graph. Further unpacking of the underlying data reveals which parameters drive the magnitude

differences, but an understanding of the relative “betterness” of decisions in a certain area is not immediately available with the TreeAge visualization options. Future work on advanced decision software, focusing especially on graphing features, could help make magnitude variations more clear. Use of different colors or contour lines could show places where the scenario value difference grows and shrinks, and creative calculations and uses of color might help identify which parameters are driving the differences between options. The needed information is available, but more research could help illuminate the best ways to display and digest it.

A final limitation of this work is particular to the fuel cycle system: nuclear power growth is modeled as an exogenous variable, so there is no accounting for the potential substitution of other electricity sources. Were nuclear electricity to become expensive, other types of generation would become more desirable and might reduce demand for nuclear power. These relationships are not currently modeled, though the framework does allow examination of various scenarios where there are sudden changes in nuclear demand. A more important

implication of this limitation stems from the fact that government-level decisions made about the nuclear fuel cycle will have an impact on the broader electricity market. For example, if the government decides to build a fast reactor demonstration, the funds for the project cannot be applied to research in renewable energy. Similarly, if the government offers support to fast reactors, demand for the nuclear may increase even if it is not the cheapest available option. In

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essence, the “real” government will be deciding not only on how to evolve nuclear power, but also on which sources of electricity more generally are worth supporting. This broader decision is not considered here. Future work could address the larger question, for example by linking a model of the U.S. electricity market to the nuclear fuel cycle model and then re-framing decision analysis questions.

Despite the limitations listed in this section, the work in this thesis contributes to two areas of knowledge.