Decision theory with imperfect information:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[Hackensack] New Jersey
World Scientific
2014
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| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Print version record |
| Beschreibung: | 1 online resource |
| ISBN: | 9789814611039 9789814611046 9814611034 9814611042 |
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| 505 | 8 | |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 -- | |
| 505 | 8 | |a 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 -- | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |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 | |
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Datensatz im Suchindex
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| contents | 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 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 |
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| indexdate | 2024-07-10T07:15:22Z |
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| spelling | Aliev, R. A., (Rafik Aziz ogly) Verfasser aut Decision theory with imperfect information Rafik A. Aliev & Oleg H. Huseynov (Azerbaijan State Oil Academy, Azerbaijan) [Hackensack] New Jersey World Scientific 2014 1 online resource txt rdacontent c rdamedia cr rdacarrier Print version record 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 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 SCIENCE / System Theory bisacsh TECHNOLOGY & ENGINEERING / Operations Research bisacsh Decision making / Mathematical models fast Fuzzy decision making fast Mathematisches Modell Fuzzy decision making Decision making Mathematical models Entscheidungstheorie (DE-588)4138606-1 gnd rswk-swf Fuzzy-Logik (DE-588)4341284-1 gnd rswk-swf Entscheidungstheorie (DE-588)4138606-1 s Fuzzy-Logik (DE-588)4341284-1 s 1\p DE-604 Huseynov, Oleg H. Sonstige oth Erscheint auch als Druck-Ausgabe Aliev, R A. (Rafik Aziz ogly), author. Decision theory with imperfect information http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=839649 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Aliev, R. A., (Rafik Aziz ogly) Decision theory with imperfect information 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 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 SCIENCE / System Theory bisacsh TECHNOLOGY & ENGINEERING / Operations Research bisacsh Decision making / Mathematical models fast Fuzzy decision making fast Mathematisches Modell Fuzzy decision making Decision making Mathematical models Entscheidungstheorie (DE-588)4138606-1 gnd Fuzzy-Logik (DE-588)4341284-1 gnd |
| subject_GND | (DE-588)4138606-1 (DE-588)4341284-1 |
| title | Decision theory with imperfect information |
| title_auth | Decision theory with imperfect information |
| title_exact_search | Decision theory with imperfect information |
| title_full | Decision theory with imperfect information Rafik A. Aliev & Oleg H. Huseynov (Azerbaijan State Oil Academy, Azerbaijan) |
| title_fullStr | Decision theory with imperfect information Rafik A. Aliev & Oleg H. Huseynov (Azerbaijan State Oil Academy, Azerbaijan) |
| title_full_unstemmed | Decision theory with imperfect information Rafik A. Aliev & Oleg H. Huseynov (Azerbaijan State Oil Academy, Azerbaijan) |
| title_short | Decision theory with imperfect information |
| title_sort | decision theory with imperfect information |
| topic | SCIENCE / System Theory bisacsh TECHNOLOGY & ENGINEERING / Operations Research bisacsh Decision making / Mathematical models fast Fuzzy decision making fast Mathematisches Modell Fuzzy decision making Decision making Mathematical models Entscheidungstheorie (DE-588)4138606-1 gnd Fuzzy-Logik (DE-588)4341284-1 gnd |
| topic_facet | SCIENCE / System Theory TECHNOLOGY & ENGINEERING / Operations Research Decision making / Mathematical models Fuzzy decision making Mathematisches Modell Decision making Mathematical models Entscheidungstheorie Fuzzy-Logik |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=839649 |
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