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Decision Control, Management, and Support in Adaptive and Complex Systems

Book Description

In order to ensure the criteria for monitoring and managing the various problems and design for decision control, a mathematical description of exact human knowledge is required for the management of adaptive and complex systems.  presents an application and demonstration of a new mathematical technique for descriptions of complex systems. This comprehensive collection contains scientific results in the field of contemporary approaches to adaptive decision making that is essential for researchers, scholars, and students alike. Decision Control, Management, and Support in Adaptive and Complex Systems: Quantitative Models

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Foreword
  5. Preface
  6. Acknowledgment
  7. Chapter 1: Decision Support Fundamentals
    1. ABSTRACT
    2. 1. DECISION-MAKING AND DECISION SUPPORT
    3. 2. DECISION THEORY: RATIONAL DECISION-MAKING
    4. 3. DECISION SUPPORT BASIS: DECISION-MAKING PROCESS
    5. 4. DECISION-MAKING SUPPORT SYSTEMS: CHARACTERISTICS AND CAPABILITIES
    6. 5. IMPLEMENTATION OF DECISION-MAKING SUPPORT SYSTEMS
    7. 6. THE BOOK VIEWPOINT AND ORGANIZATION
  8. Chapter 2: Mathematical Preliminaries
    1. ABSTRACT
    2. 1. SOME PROBABILISTIC CONCEPTS
    3. 2. STOCHASTIC PROGRAMMING
    4. 3. PREFERENCE’S RELATIONS
  9. Chapter 3: Preferences-Based Performance Measurement Models
    1. ABSTRACT
    2. 1. THEORY OF MEASUREMENT AND SOME BASIC MEASUREMENT SCALES
    3. 2. DISCRETE DECISION MODELS, GROUP DECISION-MAKING, ARROW'S IMPOSSIBILITY THEOREM
    4. 3. VALUE FUNCTION AND MEASUREMENT SCALE
    5. 4. UTILITY FUNCTION AND MEASUREMENT SCALE
    6. 5. SUBJECTIVE PROBABILITY
    7. 6. USE CASES
  10. Chapter 4: Elements of Utility Theory
    1. ABSTRACT
    2. 1. BEGINNINGS: THE ST. PETERSBURG PARADOX
    3. 2. AXIOMATIC APPROACH
    4. 3. SOME POPULAR UTILITY FUNCTIONS: RISK AVERSION
    5. 4. FIELD STUDIES
  11. Chapter 5: Elements of Stochastic Programming
    1. ABSTRACT
    2. 1. BEGINNINGS: STOCHASTIC PROGRAMMING
    3. 2. ROBBINS-MONRO CLASSICAL METHOD
    4. 3. POTENTIAL FUNCTION METHOD AND PATTERN RECOGNITION
    5. 4. FIELD STUDIES
  12. Chapter 6: Stochastic Utility Evaluation
    1. ABSTRACT
    2. 1. PREFERENCES RELATIONS, AXIOMS, UTILITY EXISTENCE, INTERVAL SCALE, GAMBLING APPROACH
    3. 2. PATTERN RECOGNITION OF POSITIVE AND NEGATIVE DM ANSWERS
    4. 3. UTILITY POLYNOMIAL APPROXIMATION AND CONVERGENCE OF THE PROCEDURE
    5. 4. EXAMPLES OF EVALUATION AND NUMERICAL VERIFICATIONS
  13. Chapter 7: A Preferences-Based Approach to Subjective Probability Estimation
    1. ABSTRACT
    2. 1. PREFERENCES RELATIONS: GAMBLING APPROACH
    3. 2. PATTERN RECOGNITION OF POSITIVE AND NEGATIVE DM ANSWERS: ROBBINS-MONRO STOCHASTIC APPROXIMATION
    4. 3. USE CASES
  14. Chapter 8: Extrapolation Methods in Control and Adaptive System
    1. ABSTRACT
    2. 1. THEORETICAL BASIS OF EXTRAPOLATION IN CONTROL DESIGN
    3. 2. LINEAR SPACES AND LINEAR TRANSFORMATIONS
    4. 3. MULTIATTRIBUTE LINEAR EXTRAPOLATION WITH EUCLIDEAN NORM
    5. 4. RECURRENT FORMULAS
  15. Chapter 9: Preferences, Utility Function, and Control Design of Complex Cultivation Process
    1. ABSTRACT
    2. 1. DESCRIPTION OF THE PROBLEM
    3. 2. UNSTRUCTURED BIOTECHNOLOGICAL MODELS: MONOD KINETIC MODELS, MONOD-WANG MODELS
    4. 3. UTILITY FUNCTION DETERMINATION OF THE BEST GROWTH RATE
    5. 4. OPTIMAL CONTROL
    6. 5. ITERATIVE CONTROL DESIGN
  16. Chapter 10: Personalized E-Learning Systems
    1. ABSTRACT
    2. 1. LEARNING SYSTEM AND LEARNING ENVIRONMENT
    3. 2. E-LEARNING USABILITY
    4. 3. ADAPTIVE AND ADAPTABLE E-LEARNING ENVIRONMENT
    5. 4. LEARNER MODEL AND LEARNER’S PREFERENCES
    6. 5. MEASUREMENT OF LEARNER’S AND TEACHER’S PREFERENCES
    7. 6. MEASUREMENT OF HUMAN PREFERENCES AND LEARNER’S MODEL CONSTRUCTION: USE CASES
  17. Chapter 11: Decision Support in Bird’s Production Farms
    1. ABSTRACT
    2. 1. INTRODUCTION TO THE PROBLEM
    3. 2 FACTOR ANALYSIS
    4. 3. PROGNOSIS WITH MULTIATTRIBUTE LINEAR EXTRAPOLATION
    5. 4. PROGNOSIS WITH UTILITY FUNCTION DESCRIPTION AND POLYNOMIAL PRESENTATION
    6. 5. EGG-PRODUCTION MODEL DECISION SUPPORT AND PROGNOSES
    7. 6. EGG-PRODUCTION DECISION SUPPORT INFORMATION SYSTEMS
  18. Chapter 12: A Preference Utility-Based Approach for Qualitative Knowledge Discovery
    1. ABSTRACT
    2. 1. DATA MINING AND KNOWLEDGE DISCOVERY
    3. 2. DATA MINING: DECISION-MAKING SUPPORT PROCESS
    4. 3. PREFERENCES, ORDERING, GAMBLING
    5. 4. STOCHASTIC PATTERN RECOGNITION OF POSITIVE AND NEGATIVE ANSWERS
    6. 5. ITERATIVE UTILITY EVALUATIONS AND CORRECTIONS OF THE DECISION MAKER’S WRONG ANSWERS
  19. Compilation of References
  20. About the Authors