Ben-Haim Info-Gap Decision Theory

Decisions Under Severe Uncertainty
2. Auflage 2006
ISBN: 978-0-08-046570-8
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 384 Seiten, Web PDF

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 systematicallyMany examples from diverse disciplinesRealistic representation of severe uncertaintyMulti-faceted approach to riskQuantitative model-based decision theory

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.

Autoren/Hrsg.

Ben-Haim, Yakov

Inhaltsverzeichnis

1;Front Cover;1
2;Contents;6
3;Preface to the 1st edition;12
4;Preface to the 2nd edition;14
5;1 Overview;18
6;2 Uncertainty;26
6.1;2.1 Historical Perspective;26
6.2;2.2 Is Ignorance Probabilistic?;29
6.3;2.3 Info-Gap Uncertainty, Probability and Fuzziness;31
6.4;2.4 Uncertainty and Convexity;35
6.5;2.5 Some Info-Gap Models;37
6.6;2.6 ‚ Axioms of Info-Gap Uncertainty;48
6.7;2.7 Problems;49
7;3 Robustness and Opportuneness;54
7.1;3.1 Robustness and Opportuneness;55
7.1.1;3.1.1 A First Look;55
7.1.2;3.1.2 Immunity Functions;56
7.1.3;3.1.3 Generic Decision Algorithms;58
7.1.4;3.1.4 Multi-Criterion Reward;60
7.1.5;3.1.5 Three Components of Info-Gap Decision Models;61
7.1.6;3.1.6 Preferences;62
7.1.7;3.1.7 Trade-Offs;63
7.1.8;3.1.8 Zero Robustness and Preference Reversal;65
7.2;3.2 Simple Examples;66
7.2.1;3.2.1 Engineering Design: Cantilever;67
7.2.2;3.2.2 Structural Reliability;71
7.2.3;3.2.3 Structural Reliability with Uncertain Probability;73
7.2.4;3.2.4 Set-Points for Process Control;75
7.2.5;3.2.5 Sequential Decisions;77
7.2.6;3.2.6 Project Scheduling with Uncertain Task Durations;81
7.2.7;3.2.7 Portfolio Investment;87
7.2.8;3.2.8 Monetary Policy;92
7.2.9;3.2.9 Search and Evasion;95
7.2.10;3.2.10 Assay Design: Environmental Monitoring;97
7.2.11;3.2.11 Bio-Terror Preparedness with Epidemiological Models;100
7.2.12;3.2.12 Drug Selection;103
7.2.13;3.2.13 Estimating an Uncertain Probability Density;105
7.3;3.3 Production Volume With Uncertain Costs;108
7.4;3.4 ‚ General Robustness and Opportuneness Functions;116
7.5;3.5 Problems;120
8;4 Value Judgments;132
8.1;4.1 Normalization;133
8.2;4.2 Analogical Reasoning;134
8.3;4.3 Calibration by Consequence Severity;138
8.3.1;4.3.1 Robustness Function for Environmental Management;138
8.3.2;4.3.2 Calibration by Consequence Severity;140
8.4;4.4 Rationality and Preference;141
8.5;4.5 Problems;144
9;5 Antagonistic and Sympathetic Immunities;146
9.1;5.1 Immunity Functions;147
9.2;5.2 Reward-Coherent Action;149
9.3;5.3 Vibrating Mechanical Contact;150
9.4;5.4 Multi-Tasking of Computer Jobs;154
9.4.1;5.4.1 Formulation;154
9.4.2;5.4.2 Deriving Robustness and Opportuneness Functions;157
9.4.3;5.4.3 Results;160
9.5;5.5 Problems;163
10;6 Gambling and Risk Sensitivity;166
10.1;6.1 Preview;167
10.2;6.2 Risk Sensitivity and the Robustness Curve;168
10.3;6.3 Risk Sensitivity and Two Robustness Curves;170
10.4;6.4 Initial Commitment and Uncertain Future;173
10.4.1;6.4.1 Uniformly Bounded Uncertainty;175
10.4.2;6.4.2 Ellipsoidal Fourier Uncertainty;177
10.5;6.5 Risk Sensitivity, Robustness and Opportuneness;177
10.6;6.6 Risk-Neutral Line;180
10.7;6.7 Pure Competition with Uncertain Cost;183
10.8;6.8 Interim Summary;186
10.9;6.9 Risk Assessment in Project Management;188
10.10;6.10 ‚ More on the Robustness Premium;189
10.11;6.11 ‚ Robustness Premium and Resource Commitment;193
10.12;6.12 Problems;196
11;7 Value of Information;202
11.1;7.1 Informativeness of an Info-Gap Model;203
11.2;7.2 Demand Value of Information;205
11.3;7.3 Uncertain Loads on a Cantilever;207
11.4;7.4 Cantilever: Simple and Complex Info-Gap Models;211
11.5;7.5 Gathering Information in Project Management;213
11.6;7.6 Windfall Cost of Information;214
11.6.1;7.6.1 Formulation;215
11.6.2;7.6.2 Discussion;216
11.7;7.7 Initial Commitment and Uncertain Future: Revisited;218
11.8;7.8 Problems;220
12;8 Learning;224
12.1;8.1 Learning and Deciding;224
12.2;8.2 Info-Gap Supervision of a Classifier;226
12.2.1;8.2.1 Robustness of a Classifier;226
12.2.2;8.2.2 Asymptotic Robustness;227
12.2.3;8.2.3 Robust-Optimal Classifier;229
12.2.4;8.2.4 ‚ A Proof;231
12.2.5;8.2.5 Robust Severe Tests of Truth;232
12.2.6;8.2.6 Updating Info-Gap Models;233
12.2.7;8.2.7 Plantar Pressures in Metatarsal Pathology;236
12.3;8.3 Acoustic Noise;239
12.3.1;8.3.1 Empirical Robustness;240
12.3.2;8.3.2 Updating the Acoustic Uncertainty Model;242
12.4;8.4 Summary;243
12.5;8.5 Problems;245
13;9 Coherent Uncertainties and Consensus;248
13.1;9.1 Preference Preservation Under Altered Information;249
13.2;9.2 Examples of Coherent Uncertainties;252
13.3;9.3 Principal-Agent Contract Bidding;258
13.4;9.4 ‚ Proofs;261
13.5;9.5 Problems;263
14;10 Hybrid Uncertainties;266
14.1;10.1 Info-Gap Uncertainty in a Poisson Process;267
14.2;10.2 Embedded Probability Densities;270
14.3;10.3 Example: Endangered Species;273
14.4;10.4 Example: Serving an Uncertain Queue;276
14.5;10.5 Probabilistic Info-Gap Parameter;279
14.6;10.6 Problems;281
15;11 Robust-Satisficing Behavior;284
15.1;11.1 The Ellsberg 'Paradox';285
15.2;11.2 The Allais 'Paradox';288
15.2.1;11.2.1 Formulation;289
15.2.2;11.2.2 Probability Uncertainty;290
15.2.3;11.2.3 Utility Uncertainty;293
15.3;11.3 Info-Gap Analysis of Expected-Utility Risk Aversion;294
15.4;11.4 Probability of Success;296
15.4.1;11.4.1 Robust-Satisficing and Direct-Optimizing;297
15.4.2;11.4.2 Satisficing and Survival;300
15.5;11.5 The Equity Premium Puzzle: A Solution;301
15.5.1;11.5.1 Introduction;301
15.5.2;11.5.2 Dynamics, Uncertainty and Robustness;302
15.5.3;11.5.3 Asset-Pricing Relation;304
15.5.4;11.5.4 Equity Premium;307
15.5.5;11.5.5 Stationarity;309
15.5.6;11.5.6 Discussion;310
16;12 Retrospective Essay: Risk Assessment in Project Management;314
16.1;12.1 Info-Gap Uncertainty: What Is It?;315
16.2;12.2 Info-Gap Uncertainties in Project Management;316
16.3;12.3 Robustness: Greatest Tolerable Info-Gap;319
16.4;12.4 Value Judgments: How Robust Is Robust Enough?;320
16.5;12.5 Risk and the Robustness-vs.-Reward Trade-off;323
16.6;12.6 Improving Robustness by Gathering Information;325
16.7;12.7 Improving Robustness by Restructuring;327
16.8;12.8 What Should We Optimize?;328
16.9;12.9 The Other Face of Uncertainty: Opportuneness;330
16.10;12.10 Quantitative Decision Support Systems;332
17;13 Implications of Info-Gap Uncertainty;334
17.1;13.1 Holism and Uncertainty;335
17.2;13.2 Language, Meaning and Uncertainty;338
17.3;13.3 Warrant and Uncertainty;342
17.4;13.4 Credence for Info-Gap Inference;348
17.4.1;13.4.1 Info-Gap Inference and Robust Severe Tests;349
17.4.2;13.4.2 Warrant and Credence;351
17.4.3;13.4.3 Credibility of Info-Gap Inference;354
17.4.4;13.4.4 Info-Gap Inference with the Opportuneness Function;356
17.5;13.5 Risk and Uncertainty;358
18;References;364
19;Author Index;374
19.1;A;374
19.2;B;374
19.3;C;374
19.4;D;374
19.5;E;375
19.6;F;375
19.7;G;375
19.8;H;375
19.9;J;375
19.10;K;375
19.11;L;375
19.12;M;375
19.13;N;376
19.14;O;376
19.15;P;376
19.16;Q;376
19.17;R;376
19.18;S;376
19.19;T;376
19.20;V;376
19.21;W;376
19.22;Y;377
19.23;Z;377
20;Subject Index;378
20.1;A;378
20.2;B;378
20.3;C;378
20.4;D;379
20.5;E;379
20.6;F;380
20.7;G;380
20.8;H;380
20.9;I;380
20.10;K;381
20.11;L;381
20.12;M;381
20.13;N;382
20.14;O;382
20.15;P;382
20.16;Q;383
20.17;R;383
20.18;S;384
20.19;T;384
20.20;U;384
20.21;V;385
20.22;W;385