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The United States and the world face serious threats to nuclear stability and peace, now and in the coming decades. Within the nuclear arena, U.S. policy makers will need to make strategic decisions related to nuclear risks to assist with long-term planning as well as responding in real time to unanticipated events. The occurrence of unanticipated nuclear events is expected to increase as more countries develop, expand, or field nuclear energy capability; more countries consider development of nuclear weapon capability and new nuclear weapon states emerge; and nuclear weapon states expand their nuclear arsenals. At the request of the Department of Defense, the National Academies of Sciences, Engineering, and Medicine established and managed the Committee on Risk Analysis Methods for Nuclear War and Nuclear Terrorism to explore U.S. government methods for assessing nuclear war and nuclear terrorism risks and how those assessments are used to develop strategy and policy. This publication is the unclassified Phase 2 version of the final classified report. Risk Analysis Methods for Nuclear War and Nuclear Terrorism: Phase II builds on an earlier Phase I unclassified report. This book expands upon the use of analytical methods to assess the risks of nuclear terrorism and nuclear war and the role such approaches may play in U.S. security strategy.

The assessment of risk is complex and often controversial. It is derived from the existence of a hazard, and it is characterized by the uncertainty of possible undesirable events and their outcomes. Few outcomes are as undesirable as nuclear war and nuclear terrorism. Over the decades, much has been written about particular situations, policies, and weapons that might affect the risks of nuclear war and nuclear terrorism. The nature of the concerns and the risk analysis methods used to evaluate them have evolved considerably over time. At the request of the Department of Defense, Risk Analysis Methods for Nuclear War and Nuclear Terrorism discusses risks, explores the risk assessment literature, highlights the strengths and weaknesses of risk assessment approaches, and discusses some publicly available assumptions that underpin U.S. security strategies, all in the context of nuclear war and nuclear terrorism.

The Committee on Risk Analysis Methods for Nuclear War and Nuclear Terrorism was established and managed by the National Academies of Sciences, Medicine, and Engineering in response to a congressional mandate to independently explore U.S. government methods for assessing nuclear war and nuclear terrorism risks and how those assessments are used to develop strategy and policy. This publication is the public, abbreviated version of the classified report.

Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale—terabytes and petabytes—is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge—from computer science, statistics, machine learning, and application disciplines—that must be brought to bear to make useful inferences from massive data.

'This report is the output of a panel created under the auspices of the Committee on Applied and Theoretical Statistics (CATS).'--- Pref.