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.

An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

This book is a collection of selected papers presented at the SIGEF conference, held at the Faculty of Economics and Business of the University of Girona (Spain), 06-08 July, 2015. This edition of the conference has been presented with the slogan “Scientific methods for the treatment of uncertainty in social sciences”. There are different ways for dealing with uncertainty in management. The book focuses on soft computing theories and their role in assessing uncertainty in a complex world. It gives a comprehensive overview of quantitative management topics and discusses some of the most recent developments in all the areas of business and management in soft computing including Decision Making, Expert Systems and Forgotten Effects Theory, Forecasting Models, Fuzzy Logic and Fuzzy Sets, Modelling and Simulation Techniques, Neural Networks and Genetic Algorithms and Optimization and Control. The book might be of great interest for anyone working in the area of management and business economics and might be especially useful for scientists and graduate students doing research in these fields.

The U.S. Environmental Protection Agency (EPA) is one of several federal agencies responsible for protecting Americans against significant risks to human health and the environment. As part of that mission, EPA estimates the nature, magnitude, and likelihood of risks to human health and the environment; identifies the potential regulatory actions that will mitigate those risks and protect public health1 and the environment; and uses that information to decide on appropriate regulatory action. Uncertainties, both qualitative and quantitative, in the data and analyses on which these decisions are based enter into the process at each step. As a result, the informed identification and use of the uncertainties inherent in the process is an essential feature of environmental decision making. EPA requested that the Institute of Medicine (IOM) convene a committee to provide guidance to its decision makers and their partners in states and localities on approaches to managing risk in different contexts when uncertainty is present. It also sought guidance on how information on uncertainty should be presented to help risk managers make sound decisions and to increase transparency in its communications with the public about those decisions. Given that its charge is not limited to human health risk assessment and includes broad questions about managing risks and decision making, in this report the committee examines the analysis of uncertainty in those other areas in addition to human health risks. Environmental Decisions in the Face of Uncertainty explains the statement of task and summarizes the findings of the committee.

A brilliant and courageous doctor reveals, in gripping accounts of true cases, the power and limits of modern medicine. Sometimes in medicine the only way to know what is truly going on in a patient is to operate, to look inside with one's own eyes. This book is exploratory surgery on medicine itself, laying bare a science not in its idealized form but as it actually is -- complicated, perplexing, and profoundly human. Atul Gawande offers an unflinching view from the scalpel's edge, where science is ambiguous, information is limited, the stakes are high, yet decisions must be made. In dramatic and revealing stories of patients and doctors, he explores how deadly mistakes occur and why good surgeons go bad. He also shows us what happens when medicine comes up against the inexplicable: an architect with incapacitating back pain for which there is no physical cause; a young woman with nausea that won't go away; a television newscaster whose blushing is so severe that she cannot do her job. Gawande offers a richly detailed portrait of the people and the science, even as he tackles the paradoxes and imperfections inherent in caring for human lives. At once tough-minded and humane, Complications is a new kind of medical writing, nuanced and lucid, unafraid to confront the conflicts and uncertainties that lie at the heart of modern medicine, yet always alive to the possibilities of wisdom in this extraordinary endeavor. Complications is a 2002 National Book Award Finalist for Nonfiction.

For thirty years, the literature on decision-making and planning has been divided into two camps : work premised on rational models of choice and work designed to discredit such models. The sustained critic of fully rational decision-making theories has al ready a long history and a constant message to deliver : in practice, consequential decision-making hardly fulfills the canons of perfect rationality. There is also evidence that decision-making and planning are not unitary processes. Although the concept of "decision-making" connotes the idea of a single process, making a single choice involves a complex of processing tasks : structuring the problem, finding alternatives worth considering, deciding what information is relevant, assessing various consequences, and a variety of others. The aim of this volume is to bring together and try to inter relate some of the concepts and relevant knowledge from various disciplines concerned with one important aspect of this complex process : the management of uncertainty. It is hardly necessary to reiterate the case made by numerous authors about our changing and increasingly uncertain world. Suffice it to say here that it is uncertainty about the future, and in many cases about the past and the present also, which makes decision-making and planning so difficul t. The management of uncertainty may be defined as the way in which uncertainty is treated and processed in decision-making.

The objective of this investigation is to discuss qualitatively the different methodological approaches developed to deal with uncertainty in decision making processes. For its preparation were used mainly the analysis of documents, the historicallogical method and the analytical-synthetic method which allowed an assessment of the state of the art in the topic. It was possible to identify that the phenomenon of uncertainty has two natures: one aleatory and other epistemic. Aleatory uncertainty arises from stochastic processes, while epistemic uncertainty is caused by imprecision, ignorance, credibility or incompleteness in the information necessary to make the decision. Aleatory uncertainty is effectively modeled by probability theory, which constitutes the starting point for maximizing expected utility in decision processes. Epistemic uncertainty is modeled, depending on the characteristic of the information, mainly through fuzzy sets theory, rough sets or gray systems. Each of these approaches has its advantages and disadvantages, so in order to take advantage of their strengths, hybrid models have been created. Nowadays, given the need to make more robust decisions, all these theories are being refined by the scientific community because, although uncertainty cannot be completely eliminated they have shown that it can be dealt with effectively.