Organization Structure: Cybernetic Systems Foundation

Organization Structure: Cybernetic Systems Foundation utilizes a cybernetic systems framework for the study of organizations using GST (General Systems Theory) and presents a comprehensive formal view of organizations assessing regulation, coordination and adaptation managements. The use of GST in this book is in sharp contrast to previous attempts. It addresses structural problems totally based on qualitative, non-numerical mathematics. The book lays a framework for initial efforts to investigate the potential of using formal GST to address organizational dilemmas. The text has been tested in several graduate courses. It can serve as an excellent textbook or reference for graduate level research in this field, as well as a reference for researchers in related fields.

Texte du rabat

Organization Structure: Cybernetic Systems Foundation utilizes a cybernetic systems framework for the study of organizations using GST (General Systems Theory) and presents a comprehensive formal view of organizations assessing regulation, coordination and adaptation managements. The use of GST in this book is in sharp contrast to previous attempts. It addresses structural problems totally based on qualitative, non-numerical mathematics. The book lays a framework for initial efforts to investigate the potential of using formal GST to address organizational dilemmas. The text has been tested in several graduate courses. It can serve as an excellent textbook or reference for graduate level research in this field, as well as a reference for researchers in related fields.

Résumé
Organization Structure: Cybernetic Systems Foundation utilizes a cybernetic systems framework for the study of organizations using GST (General Systems Theory) and presents a comprehensive formal view of organizations assessing regulation, coordination and adaptation managements. The use of GST in this book is in sharp contrast to previous attempts. It addresses structural problems totally based on qualitative, non-numerical mathematics. The book lays a framework for initial efforts to investigate the potential of using formal GST to address organizational dilemmas. The text has been tested in several graduate courses. It can serve as an excellent textbook or reference for graduate level research in this field, as well as a reference for researchers in related fields.

Contenu
List of Figures. List of Tables. Preface. Acknowledgements. Introduction. I: Background. 1. Model-Based Diagnosis. 1. Introduction. 2. Fundamentals of Model-Based Diagnosis. 3. Model-Based Diagnosis of Dynamic Systems. 4. Summary. 2: Diagnosis of Discrete-Event Systems. 1. Introduction. 2. Diagnoser Approach. 3. Decentralized Diagnoser Approach. 4. Incremental Decentralized Diagnoser Approach. 5. Decentralized Protocol Approach. 6. Process Algebra Approach. 7. Quantized System Approach. 8. Summary. II: Diagnosis of Active Systems. 3. Active Systems. 1. Introduction. 2. Component. 3. Link. 4. System. 5. Reaction. 6. Observer. 7. Observation. 8. Diagnostic Problem. 9. Summary. 4. Monolithic Diagnosis. 1.Introduction. 2. Behavior Reconstruction. 3. Diagnosis Generation. 4. Summary. Appendices. Algorithms. Proofs of Theorems. 5. Modular Diagnosis. 1. Introduction. 2. Cluster. 3. Decomposition. 4. Modular Reconstruction. 5. Compositional Definition of Active Space. 6. Problem Decomposition. 7. Summary. Appendices. Algorithms. Proofs of Theorems. III: Polymorphic Systems. 6. Simulation-Based Diagnosis. 1. Introduction. 2.Polymorphic System. 3. Monolithic Reconstruction. 4. Modular Reconstruction. 5. Formalization of a Polymorphic ActiveSpace. 6. Summary. 7. Rule-Based Diagnosis. 1. Introduction. 2. Rule Generation. 3. Route. 4. Diagnostic Space. 5. Matching Graph. 6. Diagnostic Rule. 7. Rule Exploitation. 8. Summary. Appendices. Algorithms. Proofs of Theorems. 8. Monitoring-Based Diagnosis. 1. Introduction. 2. Abductive Space. 3. Diagnostic Space. 4. Monitoring Space. 5. Monitoring Graph. 6. Continuous Diagnosis. 7. Summary. Appendices. Algorithms. IV: Advanced Topics. 9. Uncertain Observations. 1. Introduction. 2. Uncertainty Requirements. 3. Uncertain Observation. 4. Solving Uncertain Diagnostic Problems. 5. Summary. Appendices. Algorithms. Proofs of Theorems. 10. Complex Problems. 1. Introduction. 2. Complex Observation. 3. Solving Complex Diagnostic Problems. 4. Summary. 11. Uncertain Events. 1. Introduction. 2. Uncertainty Requirements. 3. Reconstruction. 4. Summary. 12. Distributed Observations. 1. Introduction. 2. Observer. 3. Distributed Observation. 4. Distributed Diagnostic Problem. 5. Solving Distributed Diagnostic Problems. 6. Summary. 13. Sample. 1. Introduction. 2. Modeling. 3. Simulation-Based Diagnosis. 4. Uncertain Observations. 5. Monitoring-Based Diagnosis. References. Index.