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Decision theory with imperfect information /
Every day decision making in complex human-centric systems are characterized by imperfect decision-relevant information. The principal problems with the existing decision theories are that they do not have capability to deal with situations in which probabilities and events are imprecise. In this bo...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Hackensack] New Jersey :
World Scientific,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Aliev, R. A. |q (Rafik Aziz ogly), |e author. | |
| 245 | 1 | 0 | |a Decision theory with imperfect information / |c Rafik A. Aliev & Oleg H. Huseynov (Azerbaijan State Oil Academy, Azerbaijan). |
| 264 | 1 | |a [Hackensack] New Jersey : |b World Scientific, |c 2014. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 1. Theories of decision making under uncertainty. 1.1. What is decision making? A general framework of a decision making problem. 1.2. Expected utility theory of Von Neumann and Morgenstern. 1.3. Subjective expected utility theory of savage. 1.4. Prospect theory and rank-dependent utility models. 1.5. Choquet expected utility. 1.6. Cumulative prospect theory. 1.7. Multiple priors models. 1.8. Hierarchical uncertainty models. 1.9. Analyses of the existing decision theories -- 2. Fuzzy logic and approximate reasoning. 2.1. Fuzzy sets. 2.2. Fuzzy logic. 2.3. Fuzzy functions. 2.4. Approximate reasoning -- 3. Preferences framework. 3.1. Why vague preferences? 3.2. Fuzzy preferences. 3.3. Linguistic preferences. 3.4. Methods of uncertain preferences modeling -- 4. Imperfect decision-relevant information. 4.1. What is imperfect information? 4.2. Imprecise probabilities. 4.3. Granular states of nature. 4.4 .imprecise outcomes -- 5. Uncertainty measures in decision making. 5.1. Objective and subjective probabilities. 5.2. Non-additive measures. 5.3. Fuzzy measures and fuzzy-valued fuzzy measures. 5.4. Comparative analysis of the existing measures of uncertainty -- 6. Fuzzy logic-based decision theory with imperfect information. 6.1. Decision model. 6.2. Multicriteria decision analysis with imperfect information -- 7. Hierarchical models for decision making with imperfect information. 7.1. Multi-agent fuzzy hierarchical model for decision making. 7.2. Decision making with second-order imprecise probability -- 8. Decision making model without utility. 8.1. State of the art. 8.2. Decision making model without utility. 8.3. Related works -- 9. Behavioral decision making with combined states under imperfect information. 9.1. State of the art. 9.2. Combined states concept and behavioral decision making. 9.3. Agent's behavior models -- 10. Decision making under unprecisiated imperfect information. 10.1. State of the art. 10.2. Fuzzy f-marks. 10.3. Axioms of the fuzzy incidence geometry. 10.4. Statement of a decision making problem. 10.5. F-mark based if-then rules. 10.6. Solution of a decision making problem -- 11. The general theory of decisions. 11.1. State of the art. 11.2. Principles of the suggested general theory of decisions. 11.3. Preliminaries. 11.4. A unified decision model. 11.5. Methodology for the general theory of decisions. 11.6. Related works -- 12. Simulations and applications. 12.1. Decision simulations for benchmark problems. 12.2. Applications in business and economics. 12.3. Applications in production. 12.4. Application in medicine. | |
| 520 | |a Every day decision making in complex human-centric systems are characterized by imperfect decision-relevant information. The principal problems with the existing decision theories are that they do not have capability to deal with situations in which probabilities and events are imprecise. In this book, we describe a new theory of decision making with imperfect information. The aim is to shift the foundation of decision analysis and economic behavior from the realm bivalent logic to the realm fuzzy logic and Z-restriction, from external modeling of behavioral decisions to the framework of combined states. This book will be helpful for professionals, academics, managers and graduate students in fuzzy logic, decision sciences, artificial intelligence, mathematical economics and computational economics. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Fuzzy decision making. | |
| 650 | 0 | |a Decision making |x Mathematical models. | |
| 650 | 2 | |a Decision Support Techniques | |
| 650 | 6 | |a Prise de décision floue. | |
| 650 | 6 | |a Prise de décision |x Modèles mathématiques. | |
| 650 | 7 | |a SCIENCE |x System Theory. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Operations Research. |2 bisacsh | |
| 650 | 7 | |a Decision making |x Mathematical models |2 fast | |
| 650 | 7 | |a Fuzzy decision making |2 fast | |
| 700 | 1 | |a Huseynov, Oleg H., |e author. | |
| 776 | 0 | 8 | |i Print version: |a Aliev, R.A. (Rafik Aziz ogly). |t Decision theory with imperfect information |z 9789814611039 |w (DLC) 2014022702 |w (OCoLC)880370406 |
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| 938 | |a EBSCOhost |b EBSC |n 839649 | ||
| 938 | |a YBP Library Services |b YANK |n 12058386 | ||
| 994 | |a 92 |b IZTAP | ||