Decision Making under Deep Uncertainty
Decision Making under Deep Uncertainty

Author: Vincent A. W. J. Marchau

Publisher: Springer

Published: 2019-04-04

Total Pages: 408

ISBN-13: 3030052524

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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.

Multicriteria Decision-Making Under Conditions of Uncertainty
Multicriteria Decision-Making Under Conditions of Uncertainty

Author: Petr Ekel

Publisher: John Wiley & Sons

Published: 2019-12-12

Total Pages: 368

ISBN-13: 1119534925

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A guide to the various models and methods to multicriteria decision-making in conditions of uncertainty presented in a systematic approach Multicriteria Decision-Making under Conditions of Uncertainty presents approaches that help to answer the fundamental questions at the center of all decision-making problems: "What to do?" and "How to do it?" The book explores methods of representing and handling diverse manifestations of the uncertainty factor and a multicriteria nature of problems that can arise in system design, planning, operation, and control. The authors—noted experts on the topic—and their book covers essential questions, including notions and fundamental concepts of fuzzy sets, models and methods of multiobjective as well as multiattribute decision-making, the classical approach to dealing with uncertainty of information and its generalization for analyzing multicriteria problems in condition of uncertainty, and more. This comprehensive book contains information on "harmonious solutions" in multiobjective problem-solving (analyzing “i>X, F> models), construction and analysis of “i>X, R/i” models, results aimed at generating robust solutions in analyzing multicriteria problems under uncertainty, and more. In addition, the book includes illustrative examples of various applications, including real-world case studies related to the authors’ various industrial projects. This important resource: Explains the design and processing aspect of fuzzy sets, including construction of membership functions, fuzzy numbers, fuzzy relations, aggregation operations, and fuzzy sets transformations Describes models of multiobjective decision-making (“i>X. M/i” models), their analysis on the basis of using the Bellman-Zadeh approach to decision-making in a fuzzy environment, and their diverse applications, including multicriteria allocation of resources Investigates models of multiattribute decision-making (“i>X, R/i” models) and their analysis on the basis of the construction and processing of fuzzy preference relations as well as demonstrating their applications to solve diverse classes of multiattribute problems Explores notions of payoff matrices and fuzzy-set-based generalization and modification of the classic approach to decision-making under conditions of uncertainty to generate robust solutions in analyzing multicriteria problems Written for students, researchers and practitioners in disciplines in which decision-making is of paramount relevance, Multicriteria Decision-Making under Conditions of Uncertainty presents a systematic and current approach that encompasses a range of models and methods as well as new applications.

Decision Making Under Uncertainty, with a Special Emphasis on Geosciences and Education
Decision Making Under Uncertainty, with a Special Emphasis on Geosciences and Education

Author: Laxman Bokati

Publisher: Springer Nature

Published: 2023-03-21

Total Pages: 203

ISBN-13: 3031260864

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This book describes new techniques for making decisions in situations with uncertainty and new applications of decision-making techniques. The main emphasis is on situations when it is difficult to decrease uncertainty. For example, it is very difficult to accurately predict human economic behavior, so in economics, it is very important to take this uncertainty into account when making decisions. Other areas where it is difficult to decrease uncertainty are geosciences and teaching. The book analyzes the general problem of decision making and shows how its results can be applied to economics, geosciences, and teaching. Since all these applications involve computing, the book also shows how these results can be applied to computing, including deep learning and quantum computing. The book is recommended to researchers, practitioners, and students who want to learn more about decision making under uncertainty—and who want to work on remaining challenges.

Decision Making Under Uncertainty
Decision Making Under Uncertainty

Author: Claude Greengard

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 166

ISBN-13: 146849256X

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In the ideal world, major decisions would be made based on complete and reliable information available to the decision maker. We live in a world of uncertainties, and decisions must be made from information which may be incomplete and may contain uncertainty. The key mathematical question addressed in this volume is "how to make decision in the presence of quantifiable uncertainty." The volume contains articles on model problems of decision making process in the energy and power industry when the available information is noisy and/or incomplete. The major tools used in studying these problems are mathematical modeling and optimization techniques; especially stochastic optimization. These articles are meant to provide an insight into this rapidly developing field, which lies in the intersection of applied statistics, probability, operations research, and economic theory. It is hoped that the present volume will provide entry to newcomers into the field, and stimulation for further research.

Info-Gap Decision Theory
Info-Gap Decision Theory

Author: Yakov Ben-Haim

Publisher: Elsevier

Published: 2006-10-11

Total Pages: 385

ISBN-13: 0080465706

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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

Smart Healthcare Applications and Services: Developments and Practices
Smart Healthcare Applications and Services: Developments and Practices

Author: R”cker, Carsten

Publisher: IGI Global

Published: 2010-12-31

Total Pages: 384

ISBN-13: 1609601823

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"This book provides an in-depth introduction into medical, social, psychological, and technical aspects of smart healthcare applications as well as their consequences for the design, use and acceptance of future systems"--Provided by publisher.

Uncertainty in Computational Intelligence-Based Decision Making
Uncertainty in Computational Intelligence-Based Decision Making

Author: Ali Ahmadian

Publisher: Elsevier

Published: 2024-09-16

Total Pages: 340

ISBN-13: 044321476X

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Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others. The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science. Provides readers a thorough understanding of the uncertainty that arises in artificial intelligence (AI), computational intelligence (CI) paradigms, and algorithms Encourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision design Provides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision

An Integrated Approach to Dynamic Decision Making Under Uncertainty
An Integrated Approach to Dynamic Decision Making Under Uncertainty

Author: Tze-Yun Leong

Publisher:

Published: 1994

Total Pages: 176

ISBN-13:

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Abstract: "Decision making is often complicated by the dynamic and uncertain information involved. This work unifies and generalizes the major approaches to modeling and solving a sub-class of such decision problems. The relevant problem characteristics include discrete problem parameters, separable optimality functions, and sequential decisions made in stages. The relevant approaches include semi-Markov decision processes, dynamic decision modeling, and decision-theoretic planning. An analysis of current decision frameworks establishes a unifying task definition and a common vocabulary; the exercise also identifies the trade-off between model transparency and solution efficiency as their most significant limitation. Insights gained from the analysis lead to a new methodology for dynamic decision making under uncertainty. The central ideas involved are multiple perspective reasoning and incremental language extension. Multiple perspective reasoning supports different information visualization formats for different aspects of dynamic decision modeling. Incremental language extension promotes the use of translators to enhance language ontology and facilitate practical development. DynaMoL is a language design that adopts the proposed paradigm; it differentiates inferential and representational support for the modeling task from the solution or computation task. The dynamic decision grammar defines an extensible decision ontology and supports problem specification with multiple interfaces. The graphical presentation convention governs parameter visualization in multiple perspectives. The mathematical representation as semi-Markov decision process facilitates formal model analysis and admits multiple solution methods. A general translation technique is devised for the different perspectives and representations of the decision factors and constraints. DynaMoL is evaluated on a prototype implementation, via a comprehensive case study in medicine. The case study is based on an actual treatment planning decision for a patient with atrial fibrillation. The problems addressed include both long-term discrimination and short-term optimization of different treatment strategies. The results demonstrate practical promise of the framework."