Springer Sensitivity Analysis: An Introduction for the Management Scientist: 251 (International Series in Operations Research & Management Science, 251)

This book is an expository introduction to the methodology of sensitivity analysis of model output. It is primarily intended for investigators, students and researchers that are familiar with mathematical models but are less familiar with the techniques for performing their sensitivity analysis. A variety of sensitivity methods have been developed over the years. This monograph helps the analyst in her/his first exploration of this world. The main goal is to foster the recognition of the crucial role of sensitivity analysis methods as the techniques that allow us to gain insights from quantitative models.  Also, exercising rigor in performing sensitivity analysis becomes increasingly relevant both to decision makers and modelers. The book helps the analyst in structuring her/his sensitivity analysis quest properly, so as to obtain the correct answer to the corresponding managerial question. The first part of the book covers Deterministic Methods, including Tornado Diagrams; One-Way Sensitivity Analysis; Differentiation-Based Methods and Local Sensitivity Analysis with Constraints.  The second part looks at Probabilistic Methods, including Regression-Based methods, Variance-Based Methods, and Distribution-Based methods. The final section looks at Applications, including capital budgeting, sensitivity analysis in climate change modelling and in the risk assessment of a lunar space mission. From the Back Cover This book is an expository introduction to the methodology of sensitivity analysis of model output. It is primarily intended for investigators, students and researchers that are familiar with mathematical models but are less familiar with the techniques for performing their sensitivity analysis. A variety of sensitivity methods have been developed over the years. This monograph helps the analyst in her/his first exploration of this world. The main goal is to foster the recognition of the crucial role of sensitivity analysis methods as the techniques that allow us to gain insights from quantitative models.  Also, exercising rigor in performing sensitivity analysis becomes increasingly relevant both to decision makers and modelers. The book helps the analyst in structuring her/his sensitivity analysis quest properly, so as to obtain the correct answer to the corresponding managerial question. The first part of the book covers Deterministic Methods, including Tornado Diagrams; One-Way Sensitivity Analysis; Differentiation-Based Methods and Local Sensitivity Analysis with Constraints.  The second part looks at Probabilistic Methods, including Regression-Based methods, Variance-Based Methods, and Distribution-Based methods. The final section looks at Applications, including capital budgeting, sensitivity analysis in climate change modelling and in the risk assessment of a lunar space mission. About the Author Emanuele Borgonovo is a Professor in the Department of Decision Sciences at Bocconi University, Milan. He is Director of the Bachelor in Economics, Management and Computer Science and Director of the Management Science Laboratory of SDA Bocconi Business School. He is co-editor-in-Chief of the European Journal of Operational Research and a member of the editorial board of several international journals. He has been elected member of the Council of the Decision Analysis Society of INFORMS (2015-2018). He is co-Chair of the Technical Committee on Uncertainty Analysis of the European Safety and Reliability Association. He has received several prestigious national and international awards. He has been invited speaker and keynote speaker at several international conferences and has worked on numerous national and international research projects. He earned his PhD in Probabilistic Risk Assessment at the Massachusetts Institute of Technology under the supervision of Prof. George E. Apostolakis.  His primary research areas are Decision Analysis, Risk Analysis and Business Analyti