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This book provides the reader with a domain-independent method for reducing risk through maximizing reliability, reducing epistemic uncertainty, reducing aleatory uncertainty, ranking the reliabilities of systems and processes, minimizing the risk of faulty assemblies, and ranking decision-making alternatives in the presence of deep uncertainty.
List of contents
1. FUNDAMENTAL CONCEPTS RELATED TO RISK AND UNCERTAINTY REDUCTION BY USING ALGEBRAIC INEQUALITIES. 2. PROPERTIES OF ALGEBRAIC INEQUALITIES AND STANDARD ALGEBRAIC INEQUALITIES. 3. BASIC TECHNIQUES FOR PROVING ALGEBRAIC INEQUALITIES. 4. USING OPTIMISATION METHODS FOR DETERMINING TIGHT UPPER AND LOWER BOUNDS: TESTING A CONJECTURED INEQUALITY BY A SIMULATION - EXERCISES. 5. RANKING THE RELIABILITIES OF SYSTEMS AND PROCESSES BY USING INEQUALITIES. 6. USING INEQUALITIES FOR REDUCING EPISTEMIC UNCERTAINTY AND RANKING DECISION ALTERNATIVES. 7. CREATING A MEANINGFUL INTERPRETATION OF EXISTING ABSTRACT INEQUALITIES AND LINKING IT TO REAL APPLICATIONS. 8. OPTIMISATION BY USING INEQUALITIES. 9. DETERMINING TIGHT BOUNDS FOR THE UNCERTAINTY IN RISK-CRITICAL PARAMETERS AND PROPERTIES BY USING INEQUALITIES. 10. USING ALGEBRAIC INEQUALITIES TO SUPPORT RISK-CRITICAL REASONING.
About the author
Michael T. Todinov, PhD, has a background in mechanical engineering, applied mathematics and computer science. Prof.Todinov pioneered reliability analysis based on the cost of failure, repairable flow networks and networks with disturbed flows, domain-independent methods for reliability improvement and risk reduction and reducing risk and uncertainty by using algebraic inequalities.
Summary
This book provides the reader with a domain-independent method for reducing risk through maximizing reliability, reducing epistemic uncertainty, reducing aleatory uncertainty, ranking the reliabilities of systems and processes, minimizing the risk of faulty assemblies, and ranking decision-making alternatives in the presence of deep uncertainty.
