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Daniel Straub, Technical University of Munich
Within the idealized world of Bayesian decision analysis, optimal decisions are well defined. Unfortunately – or maybe fortunately – finding optimal decisions in real life is complicated by the fact that multiple decision makers are involved, preferences are often difficult to specify and probabilistic quantification of uncertainties is challenging and can be misleading. This talk will focus on the uncertainty quantification challenge. I will examine common arguments for and against probabilistic quantification based on specific examples. The talk will not provide answers to all open questions, but should provide some food-for-thought to the discussion, if, when and how uncertainty should be quantified.
Speaker biosketch
Daniel Straub is currently Associate Professor for engineering risk and reliability analysis at the Technical University of Munich (TUM), Germany. His interest is in developing physics-based stochastic models and methods for decision support in infrastructure, environmental and general engineering systems, with a particular focus on Bayesian techniques and decision analysis for risk and reliability analysis.
Daniel is particularly interested in linking fundamental research to application-specific challenges. He is developing novel models and algorithms for reliability assessment, data analysis, decision, risk and sensitivity analysis. Concurrently, he works successfully with partners in multiple industries, including infrastructure engineering, offshore and marine engineering, geotechnical engineering, natural hazards, automotive as well as aero- and astronautical engineering.
Daniel is past president of IFIP WG 7.5 and Geosnet. He is active in multiple professional organizations and code committees in Germany, and in the editorial boards of the leading journals in engineering reliability and risk. His awards include the ETH Silbermedaille and the Early Achievement Research Award of IASSAR. He is also an Honorary Professor at the University of Aberdeen, UK.
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