Using Markov Chain decision trees to mitigate risk, case study in Python
A Workshop by Michael Koller Prof. Dr. ETH Zurich (CRO / Extraordinary professor, Amlin AG / Federal Institute of Technology Zurich (ETH))
About this Workshop
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.