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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... show that info-gap theory provides simple explanations of these results. We then develop an info-gap model of expected-utility risk aversion, and conclude the chapter with a theorem showing that robust-satisficing behavior has a better ...
... , and this book is no different. We will forever strive to enhance our ability to understand and manage the gap between what we do know and what we could know. • • • The following table shows the 13 chapters Chapter 1. Overview 7.
Decisions Under Severe Uncertainty Yakov Ben-Haim. • • • The following table shows the 13 chapters in road-map form. Chapters 3 through 6 are the core of the book, and section 3.1 is the kernel of this core. Chapter 2 provides a ...
... shows us uncertainties even in the position and momentum of individual molecular motion. Unlike earlier theories, quantum mechanics suggests a basic physical indeterminacy of natural phenomena, perhaps even a violation of traditional ...
... Show that: u(t) ∈ U1 (α, ̃u) for all α ≥ √ 1 2λ (2.52) u(t) ∈ U2(α, ̃u) for all α ≥ 1 (2.53) Note that compatibility √12λ of ≫ 1. What does this imply about the coherence or this function (u(t) in eq.(2.51)) with each of the ...
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 |