Info-Gap Decision Theory: Decisions Under Severe UncertaintyElsevier, 11/10/2006 - 384 من الصفحات 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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... vector, we are very hard-pressed to make any useful assertions about plausibility among the infinite continuum of alternative r-functions, so a fuzzy model of the uncertainty in the removal rate function is inaccessible without ...
... vector-valued rather than having a one-dimensional scalar representation. Energy-bound uncertainty models arise in many situations, where 'energy' is loosely defined as a quadratic function, in analogy to the energy per unit time of an ...
... vector case, eq.(2.10), such intervals apply to each element of the vector u, producing a family of N-dimensional expanding boxes. The scalar envelope-bound info-gap models can be generalized for vector functions in another way as well ...
... vector-valued functions. Eq.(2.14) is an envelope-bound info-gap model for any set S of functions which obeys eqs ... vector norm. This generalization of the Euclidean length of the vector v, which results when V is the identity matrix ...
... vector of the cosine and sine functions appearing in the relation, and c as the vector of the corresponding Fourier coefficients. Then eq.(2.24) becomes: u(t) = cTφ(t) (2.25) Uncertainty in the Fourier coefficient vector c can be ...
المحتوى
1 | |
9 | |
37 | |
4 Value Judgments | 115 |
5 Antagonistic and Sympathetic Immunities | 129 |
6 Gambling and Risk Sensitivity | 149 |
7 Value of Information | 185 |
8 Learning | 207 |
10 Hybrid Uncertainties | 249 |
11 RobustSatisficing Behavior | 267 |
Risk Assessment in Project Management | 297 |
13 Implications of InfoGap Uncertainty | 317 |
References | 347 |
Author Index | 357 |
Subject Index | 361 |
9 Coherent Uncertainties and Consensus | 231 |