Info-Gap Decision Theory: Decisions Under Severe Uncertainty
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. This book 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.
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3 Robustness and Opportuneness
4 Value Judgments
5 Antagonistic and Sympathetic Immunities
6 Gambling and Risk Sensitivity
7 Value of Information
9 Coherent Uncertainties and Consensus
Other editions - View all
ambient uncertainty calibration choice choose coherence functions consider critical reward rc critical value decision algorithm decision analyst decision maker decision problem decreases defined demand value denote density discussion empirical robustness entails envelope equity premium puzzle estimated evaluate evidence example expressed formulate Fourier gap function greater greatest horizon of uncertainty immunity functions immunity to uncertainty implies increases increment info-gap decision theory info-gap model info-gap uncertainty investment Knightian uncertainty known level of uncertainty lottery maximizes the robustness measure model of uncertainty nominal duration opportuneness function optimal option path positive preferences Principle of Indifference probabilistic probability distribution relation reward function risk aversion robust-satisficing action robustness and opportuneness robustness curves robustness function robustness premium robustness to uncertainty satisficing strategy structure tainty task theorem tion trade-off uncer uncertain uncertainty model uncertainty parameter uncertainty weights updating variable variation windfall reward zero