Verwandte Artikel zu Info-Gap Decision Theory: Decisions Under Severe Uncertainty
Inhaltsangabe
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.
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Über die Autorin bzw. den Autor
Yakov Ben-Haim originated info-gap theory which has been applied to decision-making in engineering, biological conservation, behavioral science, medicine, economic policy, project management and homeland security. Dr. Ben-Haim is a professor in Mechanical Engineering at the Technion - Israel Institute of Technology, and holds the Yitzhak Moda'i Chair in Technology and Economics. He has been a visiting professor in Canada, Europe, Japan, Korea and the U.S.
Von der hinteren Coverseite
Info-Gap Decision Theory presents a fresh approach to the age-old problem of deciding responsibly with deficient information. An info-gap is the disparity between what is known and what needs to be known in order to make a well-founded decision. This idea is developed into a quantitative tool for decision-making under severe and unstructured uncertainty. An info-gap has two facets: pernicious uncertainty threatens failure, while propitious uncertainty entails the opportunity for windfall success. Info-gap theory has decision functions for defending against failure and for facilitating windfall, and explores the trade-off between them. The robustness function satisfices by guaranteeing survival while maximizing the immunity to uncertainty. In contrast, the opportunity function "windfalls" by reducing the immunity to sweeping success. These two strategies may be either mutually supporting or antagonistic.
This book is essential for reliability analysis and strategic planning, and includes quantitative tools for decision support, risk assessment, option prioritizing, and trade-off analysis. Examples are presented from engineering analysis and design, project management, economic planning, financial risk assessment, biological conservation, medical decisions, homeland security and more. Theoretical discussions address value judgments, info-gap gambling, value of information, learning, conflict and consensus, robust-satisficing behavior and philosophical implications of info-gap uncertainty.
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