Thesis Conclusions 154 

Conclusions from this work fall into two categories. The first body of conclusions concerns the methodology explored and tested in the thesis, which includes a hybrid decision analysis and system dynamics model applied to a complex system. The second body of conclusions offers new knowledge on evolution pathways for the U.S. nuclear fuel cycle.

8.1.1 Methodological Conclusions

Perhaps the most valuable conclusion to be drawn from this thesis is that a decision- analytic approach to synthesizing system dynamics results can help illuminate the parameters that are important decision drivers, and teach the analyst about the system. For example, it is very apparent throughout Chapters 5, 6, and 7 that decision maker preferences are a major driver of the decision outcome. The decision space is almost always more sensitive to the weighting for cost (which represents [1- the weight for waste]) than it is to the other parameters.

The parameter ranges were purposefully chosen to span nearly all plausible values, so that scenario outcomes would represent the full range of possibilities. We can thus conclude, for example, that over the range of possible cost premiums for fast reactors, decisions about closing the fuel cycle are not as sensitive to the rate of nuclear power growth as they are to the relative preference between cost and waste minimization. Similar conclusions can be drawn with confidence for several other parameters examined in Chapters 5 and 6. The particular

combination of a system dynamics model that calculates outcomes combined with an orderly exploration of the parameter space via decision analysis allows us to quickly identify the parameters of significance to the decision.

155 A second major conclusion is that we gain important insights by considering both an “ideal” and a more realistic decision maker when modeling the system. Neither perspective should necessarily be considered in the absence of the other. From the “ideal” decision maker analysis, we learned about the effects on general welfare of various uncertainties and new structures for alternatives. These represent the best all-around decisions if a decision maker has full power over the system. Changing the perspective slightly, so that the decision maker only has power over a piece of the system and outcomes become more uncertain, shows that different decisions may need to be taken in order to get closest to the “best” decision outcomes. For example, the nuclear fuel cycle example of Chapter 7 tells us that the uncertainty in industry response indicates an earlier start to fast reactor builds than otherwise might be prescribed if the government decision maker had full control. The methodology could be useful for comparing a range of ways in which the decision maker could interact with the system (i.e. different policy structures) in order to learn about the most robust ways to move toward the “best” decisions, given limited decision-maker authority.

Two further conclusions concern the incorporation of multi-attribute utility analysis into the hybrid decision analysis-system dynamics methodology. Multi-attribute utility theory is controversial, because existing methods for eliciting proper utility curves and attribute weights may not actually account properly for decision maker preferences.(McCord & de Neufville, 1983)4 Indeed, so far, hybrid DA-SD analyses have avoided consideration of more than one attribute (see section 2.3). But this analysis has shown that many of the general trends and conclusions are not highly dependent on the utility function shapes or scenario value function structure. The most important difference occurred when the utility function for waste changed from linear to a diminishing returns function, but conclusions about which parameters are important decision drivers and which decisions are robust to a range of preferences remained generally the same. Analysts should certainly tread carefully when choosing a utility function shape, especially when advising final decisions, but can still draw solid conclusions from this style of analysis.

4

McCord and de Neufville in fact question utility theory even more deeply, asserting that the commonly-held axiom of decision maker utility curves existing independently of situational probabilities is false. If, however, one believes that a utility curve at least exists to describe preferences in the case of a decision tree, the fact that very disparate curves produce similar results, as demonstrated in Chapter 6, may mean that the results are believable despite this limitation.

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The challenges associated with weight elicitation were avoided by employing sensitivity graphs to present the analysis results. In this way, readers see the best decision outcomes over the whole range of possible decision maker preferences. This approach has two advantages: decision makers can see which choices are robust, and arguments about the proper way to elicit weight values (or whether it can or should be done) are avoided. One major challenge is that interpreting results can be difficult, because explaining the slope of a line in the decision space requires simultaneous consideration of many different parameters. Overall, the benefits of showing decision robustness outweigh the extra burden on the analyst in explaining decision results.

Overall, the methodology shows promise for providing insights on the evolution of complex systems. Similar decision-making problems, involving high levels of

interconnectedness and inertia and multiple, ill-defined decision-making groups, may benefit from this framework. Potential examples of systems to study include the U.S. electricity system as a whole, U.S. transportation infrastructure, and climate change mitigation systems and policy.

8.1.2 Conclusions for U.S. Nuclear Fuel Cycle Evolution

The most important fuel cycle conclusion drawn from this work is that the option to change course later in the century has a dramatic impact on the desirability of closing the fuel cycle now. Indeed, decision makers can wait until later in the century to significantly deploy traditional, self-sustaining fast reactors, and doing so will have cost benefits and few drawbacks in terms of waste buildup by the end of the century. Waiting to deploy large amounts of TFRs is the best decision, as long as the only alternatives are LWRs or EUFRs, and as long as the amount of SNF in the temporary stockpile is not an important factor.

Building a few fast reactors as early as 2040, however, is also highly desirable, and this conclusion is confirmed by both the “ideal” and more “realistic” decision maker perspectives. From an ideal perspective, building a few fast reactors early will entail a modest waste benefit by the end of the century and comes at relatively little added cost. It also provides for a smooth building curve (if there are no natural or artificial constraints on fast reactor builds), which could allow companies to take better advantage of learning. From a more realistic perspective,

government will not be able to mandate precise numbers of fast reactors be built by industry, but will be able to offer incentives to bring TFRs into the system. This means that the government will want to learn quickly about FR costs, so that incentives can be set properly and early enough to allow for adjustments enabling a desirable build pattern for fast reactors. Overall, it makes

157 sense to build a small fleet early on, and to reserve judgment about larger-scale deployment until later in the century.

A second conclusion is that the desirability of building FRs in the first period (in this case, at about 2040) decreases if nuclear power growth is likely to be low. Though the results of sections 5.4 and 5.5 demonstrated that building FRs in the second period will be attractive for low growth cases in order to get a waste benefit, a new decision will need to be made when those later periods are reached. The conclusion holds that a low likelihood for significant LWR waste generation now decreases the urgency of building TFRs.

A final conclusion regarding the nuclear fuel cycle is that for high-level decisions on whether to employ advanced recycling, the price of uranium, the achievable separations efficiency for spent fuel, and the shape and size of the cost learning curve are relatively

unimportant parameters. All three fuel cycle system parameter sets had very little effect on the decision results, even when the parameters were varied across their full plausible ranges. This model fails, however, to account for some considerations related to the variables, including the desirability of uranium/fuel security of supply, the safety implications of different separations efficiencies, and others. Under the current paradigm, where the uranium market appears stable and safety issues surrounding recycling appear soluble, explicit consideration of the set of related issues is unlikely to produce a significant change in the results. This could change, however, and further work should explore the impact of these and other considerations of potential importance.