Implementing second-order decision analysis: concepts, algorithms, and tool

10780 12 32918 2 70409 document 32918 519512.pdf application/pdf 99b7d393a0d35fb266fb7309750f2c62 MD5 2486274 2016-02-19 09:36:46 https://pure.iiasa.ac.at/id/eprint/10780/1/519512.pdf 10780 1 1 application/pdf text en public

519512.pdf

published 32919 2 70417 document 32919 lightbox.jpg image/png 5f6322ecd898c2ef33fcdc99c469edda MD5 48271 2016-02-19 09:46:53 https://pure.iiasa.ac.at/id/eprint/10780/2/lightbox.jpg 10780 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

lightbox.jpg

preview.jpg

medium.jpg

small.jpg

indexcodes.txt

http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/32918 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/32918 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/32918 archive 351 disk0/00/01/07/80 2016-01-15 08:50:12 2021-08-27 17:23:56 2016-01-15 08:50:12 article show 1 Larsson A. Kuznetsova A. Caster O. Ekenberg L. 7576 0000-0002-0665-1889 Implementing second-order decision analysis: concepts, algorithms, and tool pub iis_met prog_risk prog_rpv We present implemented concepts and algorithms for a simulation approach to decision evaluation with second-order belief distributions in a common framework for interval decision analysis. The rationale behind this work is that decision analysis with interval-valued probabilities and utilities may lead to overlapping expected utility intervals yielding difficulties in discriminating between alternatives. By allowing for second-order belief distributions over interval-valued utility and probability statements these difficulties may not only be remedied but will also allow for decision evaluation concepts and techniques providing additional insight into a decision problem. The approach is based upon sets of linear constraints together with generation of random probability distributions and utility values from implicitly stated uniform second-order belief distributions over the polytopes given from the constraints. The result is an interactive method for decision evaluation with second-order belief distributions, complementing earlier methods for decision evaluation with interval-valued probabilities and utilities. The method has been implemented for trial use in a user oriented decision analysis software. 2014 published World Scientific Publishing 10.1155/2014/519512 XJ-14-271 Advances in Decision Sciences; 2014:519512 (2014) [doi:10.1155/2014/519512] 79 public Advances in Decision Sciences 519512 1-8 519512 TRUE 0219-5259 FALSE info:eu-repo/semantics/article