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Friday, May 3, 2019

Friday, May 3, 2019 — 10:30 to 10:30 AM EDT

Optimisation under Uncertainty

Numerical solutions to optimisation problems are of large interest for various applications. Data scarcity, measurement errors and model uncertainty are clear examples where numerical optimisations are made under uncertainty. Robust optimisation is an attempt to automatically find robust optimal solutions, but show that the degree of robustness of the actual procedure depends on the shape of the objective function when finding the optimal reinsurance contract. We first go through some examples that would explain the mechanics behind robust optimisation under uncertainty and we further extend our discussion to the classical data exploratory method, namely Multidimensional Scaling (MDS). We illustrate how the robust optimisation should be adapted in order to achieve more robust decisions.

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