Using Markov Chain decision trees to mitigate risk, case study in Python

The aim is to provide to the audience a tool to define risk mitigation strategies based on Markov chain decision trees. This is an extension of Thiele difference equation. The talk involves theory, example and implementation in python. Example you have a disability insurance and options (at a monetary cost) to reintegrate people in the working process. Which spend is optimal? This concept is easily extendible to a variety of questions.

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About The Speakers

Michael Koller

Michael Koller Prof. Dr. ETH Zurich

CRO / Extraordinary professor, Amlin AG / Federal Institute of Technology Zurich (ETH)

CRO and Lecturer at ETH Zurich with proven strategic and turn around skills. Highly analytic and solution driven.