Everyone makes decisions, but not everyone is a decision analyst. A decision analyst uses quantitative models and computational methods to formulate decision algorithms, assess decision performance, identify and evaluate options, determine trade-offs and risks, evaluate strategies for investigation, and so on. Info-Gap Decision Theory is written for decision analysts. The term "decision analyst" covers an extremely broad range of practitioners. Virtually all engineers involved in design (of buildings, machines, processes, etc.) or analysis (of safety, reliability, feasibility, etc.) are decision analysts, usually without calling themselves by this name. In addition to engineers, decision analysts work in planning offices for public agencies, in project management consultancies, they are engaged in manufacturing process planning and control, in financial planning and economic analysis, in decision support for medical or technological diagnosis, and so on and on. Decision analysts provide quantitative support for the decision-making process in all areas where systematic decisions are made. This second edition entails changes of several sorts. First, info-gap theory has found application in several new areas - especially biological conservation, economic policy formulation, preparedness against terrorism, and medical decision-making. Pertinent new examples have been included. Second, the combination of info-gap analysis with probabilistic decision algorithms has found wide application. Consequently "hybrid" models of uncertainty, which were treated exclusively in a separate chapter in the previous edition, now appear throughout the book as well as in a separate chapter. Finally, info-gap explanations of robust-satisficing behavior, and especially the Ellsberg and Allais "paradoxes", are discussed in a new chapter together with a theorem indicating when robust-satisficing will have greater probability of success than direct optimizing with uncertain models. - New theory developed systematically - Many examples from diverse disciplines - Realistic representation of severe uncertainty - Multi-faceted approach to risk - Quantitative model-based decision theory

This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.

This book is a product of applying info-gap decision theory to policy formulation and evaluation in monetary economics and related domains. Info-gap theory has been applied to planning and decision problems in many areas, including engineering, biological conservation, project management, economics, medicine, homeland security, and more.

This book discusses the recent developments in robust optimization (RO) and information gap design theory (IGDT) methods and their application for the optimal planning and operation of electric energy systems. Chapters cover both theoretical background and applications to address common uncertainty factors such as load variation, power market price, and power generation of renewable energy sources. Case studies with real-world applications are included to help undergraduate and graduate students, researchers and engineers solve robust power and energy optimization problems and provide effective and promising solutions for the robust planning and operation of electric energy systems.

Explores how decision-makers can manage uncertainty that varies in both kind and severity by extending and supplementing Bayesian decision theory.

A comprehensive and accessible introduction to all aspects of decision theory, now with new and updated discussions and over 140 exercises.

The market for business knowledge is booming as companies looking to improve their performance pour millions of pounds into training programmes, consultants, and executive education. Why then, are there so many gaps between what firms know they should do and waht they actual do? This volume confronts the challenge of turning knowledge about how to improve performance into actions that produce measurable results. The authors identify the causes of this gap and explain how to close it.

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

The Knowledge Translation Toolkit provides a thorough overview of what knowledge translation (KT) is and how to use it most effectively to bridge the "know-do" gap between research, policy, practice, and people. It presents the theories, tools, and strategies required to encourage and enable evidence-informed decision-making. This toolkit builds upon extensive research into the principles and skills of KT: its theory and literature, its evolution, strategies, and challenges. The book covers an array of crucial KT enablers--from context mapping to evaluative thinking--supported by practical examples, implementation guides, and references. Drawing from the experience of specialists in relevant disciplines around the world, The Knowledge Translation Toolkit aims to enhance the capacity and motivation of researchers to use KT and to use it well. The Tools in this book will help researchers ensure that their good science reaches more people, is more clearly understood, and is more likely to lead to positive action. In sum, their work becomes more useful, and therefore, more valuable.

An expert explains how the conventional wisdom about decision making can get us into trouble—and why experience can’t be replaced by rules, procedures, or analytical methods In making decisions, when should we go with our gut and when should we try to analyze every option? When should we use our intuition and when should we rely on logic and statistics? Most of us would probably agree that for important decisions, we should follow certain guidelines—gather as much information as possible, compare the options, pin down the goals before getting started. But in practice we make some of our best decisions by adapting to circumstances rather than blindly following procedures. In Streetlights and Shadows, Gary Klein debunks the conventional wisdom about how to make decisions. He takes ten commonly accepted claims about decision making and shows that they are better suited for the laboratory than for life. The standard advice works well when everything is clear, but the tough decisions involve shadowy conditions of complexity and ambiguity. Gathering masses of information, for example, works if the information is accurate and complete—but that doesn't often happen in the real world. (Think about the careful risk calculations that led to the downfall of the Wall Street investment houses.) Klein offers more realistic ideas about how to make decisions in real-life settings. He provides many examples—ranging from airline pilots and weather forecasters to sports announcers and Captain Jack Aubrey in Patrick O’Brian’s Master and Commander novels—to make his point. All these decision makers saw things that others didn’t. They used their expertise to pick up cues and to discern patterns and trends. We can make better decisions, Klein tells us, if we are prepared for complexity and ambiguity and if we will stop expecting the data to tell us everything. “I know of no one who combines theory and observation—intellectual rigor and painstaking observation of the real world—so brilliantly and gracefully as Gary Klein.” —Malcolm Gladwell, author of Outliers and Blink