Publications

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The objective of this report is to promote increased understanding of decision making processes and hopefully to enable improved decision making regarding high-consequence, highly sophisticated technological systems. This report brings together insights regarding risk perception and decision making across domains ranging from nuclear power technology safety, cognitive psychology, economics, science education, public policy, and neural science (to name a few). It forms them into a unique, coherent, concise framework, and list of strategies to aid in decision making. It is suggested that all decision makers, whether ordinary citizens, academics, or political leaders, ought to cultivate their abilities to separate the wheat from the chaff in these types of decision making instances. The wheat includes proper data sources and helpful human decision making heuristics; these should be sought. The chaff includes ''unhelpful biases'' that hinder proper interpretation of available data and lead people unwittingly toward inappropriate decision making ''strategies''; obviously, these should be avoided. It is further proposed that successfully accomplishing the wheat vs. chaff separation is very difficult, yet tenable. This report hopes to expose and facilitate navigation away from decision-making traps which often ensnare the unwary. Furthermore, it is emphasized that one's personal decision making biases can be examined, and tools can be provided allowing better means to generate, evaluate, and select among decision options. Many examples in this report are tailored to the energy domain (esp. nuclear power for electricity generation). The decision making framework and approach presented here are applicable to any high-consequence, highly sophisticated technological system.

Large complex teams (e.g., DOE labs) must achieve sustained productivity in critical operations (e.g., weapons and reactor development) while maintaining safety for involved personnel, the public, and physical assets, as well as security for property and information. This requires informed management decisions that depend on tradeoffs of factors such as the mode and extent of personnel protection, potential accident consequences, the extent of information and physical asset protection, and communication with and motivation of involved personnel. All of these interact (and potentially interfere) with each other and must be weighed against financial resources and implementation time. Existing risk analysis tools can successfully treat physical response, component failure, and routine human actions. However, many ''soft'' factors involving human motivation and interaction among weakly related factors have proved analytically problematic. There has been a need for an effective software tool capable of quantifying these tradeoffs and helping make rational choices. This type of tool, developed during this project, facilitates improvements in safety, security, and productivity, and enables measurement of improvements as a function of resources expended. Operational safety, security, and motivation are significantly influenced by ''latent effects'', which are pre-occurring influences. One example of these is that an atmosphere of excessive fear can suppress open and frank disclosures, which can in turn hide problems, impede correction, and prevent lessons learned. Another is that a cultural mind-set of commitment, self-responsibility, and passion for an activity is a significant contributor to the activity's success. This project pursued an innovative approach for quantitatively analyzing latent effects in order to link the above types of factors, aggregating available information into quantitative metrics that can contribute to strategic management decisions, and measuring the results. The approach also evaluates the inherent uncertainties, and allows for tracking dynamics for early response and assessing developing trends. The model development is based on how factors combine and influence other factors in real time and over extended time periods. Potential strategies for improvement can be simulated and measured. Input information can be determined by quantification of qualitative information in a structured derivation process. This has proved to be a promising new approach for research and development applied to personnel performance and risk management.

This report began with a Laboratory-Directed Research and Development (LDRD) project to improve Sandia National Laboratories multidisciplinary capabilities in energy systems analysis. The aim is to understand how various electricity generating options can best serve needs in the United States. The initial product is documented in a series of white papers that span a broad range of topics, including the successes and failures of past modeling studies, sustainability, oil dependence, energy security, and nuclear power. Summaries of these projects are included here. These projects have provided a background and discussion framework for the Energy Systems Analysis LDRD team to carry out an inter-comparison of many of the commonly available electric power sources in present use, comparisons of those options, and efforts needed to realize progress towards those options. A computer aid has been developed to compare various options based on cost and other attributes such as technological, social, and policy constraints. The Energy Systems Analysis team has developed a multi-criteria framework that will allow comparison of energy options with a set of metrics that can be used across all technologies. This report discusses several evaluation techniques and introduces the set of criteria developed for this LDRD.

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