A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making

April 21, 2017 | Journal Article

Authors: David Wulf , Valentin Bertsch
Expert Systems With Applications , Vol. 83, 2017 , pp. 131-144


Multi-dimensional sensitivity analysis is crucial in multi-criteria decision making.

Natural language generation techniques increase understanding and traceability.

Explanatory concept for multi-dimensional sensitivity analysis developed.

Concept validation by experts and online survey.

Results show that concept enhances understanding notably in complex situations.


Multi-Criteria Decision Analysis (MCDA) enables decision makers (DM) and decision analysts (DA) to analyse and understand decision situations in a structured and formalised way. With the increasing complexity of decision support systems (DSSs), it becomes challenging for both expert and novice users to under- stand and interpret the model results. Natural language generation (NLG) techniques are used in various DSSs to cope with this challenge as they reduce the cognitive effort to achieve understanding of decision situations. However, NLG techniques in MCDA have so far mainly been developed for deterministic decision situations or one-dimensional sensitivity analyses. In this paper, a concept for the generation of textual explanations for a multi-dimensional preferential sensitivity analysis in MCDA is developed. The key contribution is a NLG approach that provides detailed explanations of the implications of preferential uncertainties in Multi-Attribute Value Theory (MAVT). It generates a report that assesses the influences of simultaneous or separate variations of inter-criteria and intra-criteria preferential parameters determined within the decision analysis. We explore the added value of the natural language report in an online survey. Our results show that the NLG approach is particularly beneficial for difficult interpretational tasks.

  • Publication Details

    Journal Article

    ESRI Series Number: 201716
    Research Area: Energy and Environment
    Date of Publication: April 21, 2017
    Published Online: April 21, 2017
    Publisher: Elsevier
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