Risk - A Multidisciplinary Introduction

The research interests of Claudia Klüppelberg combine various disciplines of applied probability theory and statistics with applications to risk measurement in the areas of economic, technical and climate risks. Her fundamental research concentrates on advancing the modeling and the methodology for risk analysis and risk measurement with applications to real-world problems. Before being appointed Full Professor of Mathematical Statistics at the Technische Universität München (TUM), she spent several years at ETH Zurich and at Mainz University. From 2008 to 2011 she chaired the focus group Risk Analysis and Stochastic Modeling at the TUM’s Institute for Advanced Study. Daniel Straub develops and teaches reliability analysis, risk assessment and probabilistic decision making for infrastructure, environmental and general engineering systems. He received his doctorate in 2004 at ETH Zurich. Prior to joining the TUM as a Professor of Engineering Risk Analysis in 2008, he was a postdoctoralresearcher and adjunct professor at the University of California, Berkeley (2006 to 2008). Beyond his academic work, he is active as a consultant to various industries, including the offshore, maritime, energy, infrastructure and transportation sectors. Isabell M. Welpe conducts research in the area of strategy and organization from a behavioral science perspective, with a focus on performance management, strategic leadership, organizational design, behavior in organizations and the role of digital technology and social media for and in organizations. She has held the Chair of Strategy and Organization at the TUM since 2009.

• Introduction.- Part One. Risk in History and Science: Zachmann, K.: Risk in historical perspective: concepts, contexts, and conjunctions.- Lütge, 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., Klüppelberg, C., Menzel, A.: Quantifying extreme event risk. Schön, C.-C. and Wimmer, V.: Statistical models for the prediction of genetic values.- Brechmann, E. and Czado, C.: Bayesian risk analysis.- Klüppelberg, C., Stelzer, R.: Dealing with dependent risks.- Bannör, 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., Hörmann, 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., Thümer, L., Protzer, U.: Risk reduction of cervical cancer through HPV screening and vaccination – assumptions and reality.