Info-Gap Decision Theory: Decisions Under Severe Uncertainty

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Elsevier, Oct 11, 2006 - Computers - 384 pages

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

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1 Overview
2 Uncertainty
3 Robustness and Opportuneness
4 Value Judgments
5 Antagonistic and Sympathetic Immunities
6 Gambling and Risk Sensitivity
7 Value of Information
8 Learning
10 Hybrid Uncertainties
11 RobustSatisficing Behavior
Risk Assessment in Project Management
13 Implications of InfoGap Uncertainty
Author Index
Subject Index

9 Coherent Uncertainties and Consensus

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Page 23 - I hold space to be something merely relative, as time is ; that I hold it to be an order of co-existences, as time is an order of successions. For space denotes, in terms of possibility, an order of things which exist at the same time, considered as existing together; without inquiring into their particular manner of existing.
Page 16 - Uncertainty is defined as the difference between the amount of information required to perform the task and the amount of information already possessed by the organization (1973, p.
Page 347 - He not only wanted no one to coerce him, but he also wanted to coerce no one, either by force or by proofs. He regarded the tyranny of those who attempt to dominate thinking by reasoning and sophistries, by compelling argumentation, as more dangerous to freedom than orthodoxy.

About the author (2006)

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.

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