Risk : a multidisciplinary introduction
著者
書誌事項
Risk : a multidisciplinary introduction
Springer, c2014
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注記
Includes bibliographical references
内容説明・目次
内容説明
This is a unique book addressing the integration of risk methodology from various fields. It will stimulate intellectual debate and communication across disciplines, promote better risk management practices and contribute to the development of risk management methodologies. Individual chapters explain fundamental risk models and measurement, and address risk and security issues from diverse areas such as finance and insurance, the health sciences, life sciences, engineering and information science. Integrated Risk Sciences is an emerging discipline that considers risks in different fields, aiming at a common language, and at sharing and improving methods developed in different fields. Readers should have a Bachelor degree and have taken at least one basic university course in statistics and probability. The main goal of the book is to provide basic knowledge on risk and security in a common language; the authors have taken particular care to ensure that all content can readily be understood by doctoral students and researchers across disciplines. Each chapter provides simple case studies and examples, open research questions and discussion points, and a selected bibliography inviting readers to further study.
目次
- Introduction.- Part One. Risk in History and Science: Zachmann, K.: Risk in historical perspective: concepts, contexts, and conjunctions.- Lutge, C., Schnebel, E., Westphal, N.: Risk management and business ethics: integrating the human factor.- Straub, D., Welpe, I.: Decision-making under risk: a normative and behavioral perspective.- Mainzer, K.: The new role of mathematical modelling and its importance for society.- Part Two. Quantitative Risk Methodology: Biagini, F. , Meyer-Brandis, T. and Svindland, G. :The mathematical concept of risk.- Fasen, V., Kluppelberg, C., Menzel, A.: Quantifying extreme event risk. Schoen, C.-C. and Wimmer, V.: Statistical models for the prediction of genetic values.- Brechmann, E. and Czado, C.: Bayesian risk analysis.- Kluppelberg, C., Stelzer, R.: Dealing with dependent risks.- Bannoer, K. and Scherer, M.: Model risk and uncertainty
- illustrated with examples from Mathematical finance.- Part Three. Risk Treatment in Various Applications: Roosen, J.: Cost-benefit analysis.- Straub, D.: Engineering risk assessment.- Vogel-Heuser, B.: Integrated modeling of complex production automation systems to increase dependability.- Wiesche, M., Hoermann, S., Schermann, M., Krcmar. H.: Information technology risks: an interdisciplinary challenge.- Klinker, G.: Risks in developing novel user interfaces for Human-Computer interaction.- Ankerst, D., Seifert-Klauss, V., Kiechle, M.: Translational risk models.- Seifert-Klauss, V., Thumer, L., Protzer, U.: Risk reduction of cervical cancer through HPV screening and vaccination - assumptions and reality.
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