One of the questions in the fight against terrorism is whether the United States needs a counterterrorism domestic intelligence agency separate from law enforcement. Drawing on an analysis of current counterterrorism efforts, an examination the domestic intelligence agencies in six other democracies, and interviews with intelligence and law enforcement experts, this volume lays out the relevant considerations for creating such an agency.
This report extends research on using scenarios for strategic planning, with experiments in what can be called massive scenario generation (MSG), a computationally intensive technique that seeks to combine virtues of human- and model-based exploration of "the possibility space." The authors measure particular approaches to MSG against four metrics: not needing a good initial model; the dimensionality of the possibility space considered; the degree of exploration of that space; and the quality of resulting knowledge. The authors then describe two MSG experiments for contrasting cases, one that began with a reasonable but untested analytical model, and one that began without an analytical model, but with a thoughtful list of the conditions that might characterize and distinguish among circumstances in the situation considered, a list derived from a combination of single-analyst thinking and group brainstorming. The authors experimented with a variety of methods and tools for interpreting and making sense of the "data" arising from MSG, using ordinary linear sensitivity analysis, a generalization using analyst-inspired aggregation fragments, some advanced filtering methods drawing on data-mining and machine-learning methods, and motivated metamodeling. On the basis of this preliminary work, we conclude that MSG has the potential to expand the scope of what are recognized as possible developments, provide an understanding of how those developments might come about, and help identify aspects of the world that should be studied more carefully, tested, or monitored. It should assist planners by enriching their mental library of the patterns used to guide reasoning and action at the time of crisis or decision and should help them identify anomalous situations requiring unusual actions. Finally, it should identify crucial issues worthy of testing or experimentation in games or other venues and, in some cases, suggest better ways to design mission rehearsals. If MSG can be built into training, education, research, and socialization exercises, it should leave participants with a wider and better sense of the possible, while developing skill at problem-solving in situations other than those of the "best estimate." Much development is needed, but prospects are encouraging.
RAND researchers supported a high-level Israeli government team tasked with improving long-term socioeconomic strategy for the state. This report highlights selected inputs made to the government team to summarize the essential mechanics and roles for bringing a strategic perspective to policy consideration. To show how one can use a strategic perspective in an analysis of policy choices, the report uses the example of an aging population.
Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.
This document assesses the opportunities and risks that the government of Israel faces in shifting to an energy mix increasingly dominated by domestic and imported natural gas. The analysis seeks to help the Israeli government choose robust strategies for exploiting the use of natural gas by minimizing the potential consequences of relying more heavily on natural gas. It does this by applying newly developed methods for strategic planning and decisionmaking under deep uncertainty for these assessments. The analysis also considers broader issues related to energy in Israel." --Preface.
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