14272 21 77804 2 214809 document 77804 Model_Ambiguity_Pre_Print.pdf application/pdf 10cefc341bc2a1a9f9dfd96ffe4477bd MD5 180723 2017-05-30 13:21:22 https://pure.iiasa.ac.at/id/eprint/14272/1/Model_Ambiguity_Pre_Print.pdf 14272 1 1 application/pdf text en public cc_by

Model_Ambiguity_Pre_Print.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/77804 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/77804 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/77804 archive 5 disk0/00/01/42/72 2017-01-19 10:41:58 2025-03-26 05:00:14 2017-01-19 10:41:58 article show Pflug G. 1361 0000-0001-8215-3550 Timonina-Farkas A. Hochrainer-Stigler S. 7958 0000-0002-9929-8171 pub prog_risk prog_rpv In stochastic optimization models, the optimal solution heavily depends on the selected probability model for the scenarios. However, the scenario models are typically chosen on the basis of statistical estimates and are therefore subject to model error. We demonstrate here how the model uncertainty can be incorporated into the decision making process. We use a nonparametric approach for quantifying the model uncertainty and a minimax setup to find model-robust solutions. The method is illustrated by a risk management problem involving the optimal design of an insurance contract. 2017-03-19 published Elsevier 10.1016/j.insmatheco.2016.11.008 229 126 public Insurance: Mathematics and Economics 73 68-74 TRUE 0167-6687 FALSE no info:eu-repo/semantics/article