Inverting the Pareto Boundary: Bayes linear decision support with a soft constraint

Duration: 33 mins 11 secs

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Description:	Goldstein, M Tuesday 10th April 2018 - 11:30 to 12:00


Created:	2018-04-11 14:29
Collection:	Uncertainty quantification for complex systems: theory and methodologies
Publisher:	Isaac Newton Institute
Copyright:	Goldstein, M
Language:	eng (English)


Abstract:	We consider problems of decision support based around computer simulators, where we must take into account a soft constraint on our decision choices. This leads to the problem of identifying and inverting the Pareto boundary for the decision. We show how Bayes linear methods may be used for this purpose and how the sensitivity of the decision choices may be quantified and explored. The approach is illustrated with a problem on wind farm construction. This is joint work with Hailiang Du.

Abstract:

We consider problems of decision support based around computer simulators, where we must take into account a soft constraint on our decision choices. This leads to the problem of identifying and inverting the Pareto boundary for the decision. We show how Bayes linear methods may be used for this purpose and how the sensitivity of the decision choices may be quantified and explored. The approach is illustrated with a problem on wind farm construction. This is joint work with Hailiang Du.

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