JSMAA: open source software for SMAA computations

Tervonen, Tommi

doi:10.1080/00207721.2012.659706

Cited by 73 publications

(39 citation statements)

References 21 publications

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“…The method is designed as an inverse method aimed at exploring the weights for which an alternative achieves a certain rank, which is expressed in terms of a 'rank acceptability index' for each alternative (Lahdelma and Salminen, 2001). Tervonen (2014) developed an open source software which provides a user interface for the application of different SMAA approaches. While it definitively makes the analysis of a decision situation more convenient for a DA, the software does not include NLG techniques for explaining the used terminology or the implications for the decision situation that can be derived from the model results.…”

Section: Modelling Of Multiple Preferential Uncertaintiesmentioning

confidence: 99%

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

Wulf

Bertsch

2017

Expert Systems with Applications

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A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making Abstract 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 understand 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. IntroductionWith the aim of enabling transparent and systematic support in complex decision situations, Multi-Criteria Decision Analysis (MCDA) represents a formalised framework for the analysis of different decision alternatives (Stewart, 1992;Geldermann et al., 2009). While such decision support approaches are aimed at providing guidance to decision makers (DMs), their increasing mathematical complexity often hinders a straightforward understanding and traceability on the part of the DMs. Consequently, a lot of cognitive effort is required in order to analyse, interpret and derive adequate implications from the obtained model results which is particularly challenging for novice users (Spiegelhalter and KnillJones, 1984;Henrion and Druzdzel, 1991;Gregor and Benbasat, 1999). DMs consider such models as a 'black box', so they mistrust or even reject them (Brans and Mareschal, 1994;Bell et al., 2003), which leads to a gap between available information on the one hand and processible information on the other hand.To compensate for this, further explanations of decision analysis results promote understanding of the decision situation and thus help to increase trust and acceptability of the 3 system (Greer et al., 1994;Greef and Neerincx, 1995;Dhaliwal and Benbasat, 1996;Gregor and Benbasat, 1999;Parikh et al., 2001;Geldermann, 2010). The use of natural language generation (NLG) techniques to generate such explanations automatically based on the model results has been proposed, for instance, by Pap...

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Section: Modelling Of Multiple Preferential Uncertaintiesmentioning

confidence: 99%

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

Wulf

Bertsch

2017

Expert Systems with Applications

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“…Developed by Hokkanen et al [23], the SMAA approach allows bypassing problems considering incomplete information and uncertainty by inverse weight space analysis [5,6,24]. In addition, the SMAA algorithms are specially designed for problems involving multiple decision makers or stakeholders, using advantages of comprehensible expert knowledge and outweighing possible risks [25,26].…”

Section: Interpretation Of Resultsmentioning

confidence: 99%

Sustainable decision making under uncertainty: a case study in dredged material management

Scheffler

Roth²,

Ahlf

2014

Environ Sci Eur

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Background: The port of Lübeck is one of Germany's most important harbours for goods traffic to and from the Baltic Sea Region. Sedimentation from the River Trave requires regularly maintenance dredging as well as capital dredging operations in order to maintain the operational capability of the port. A range of solutions for sustainable dredged material handling exist and an assessment of these options often proves to be challenging for decision makers. Multi-criteria decision analysis (MCDA) provides decision support by processing different data sets and evaluating suitable options in a rational way. Using the stochastic multi-criteria acceptability analysis (SMAA)-TRI method with a modification of a previously developed computerised model for decision support in dredged material management, this study was performed to test decision support under uncertainty. Results: The analysis endorsed that relocation to a dredged discharge pool and capping of an ammunition disposal site are viable options for Lübeck port. On the other hand, the use of dredged material from port expansion and its disposal on land are considered to be of very low sustainability under the given circumstances. Conclusions: The case study demonstrates capabilities as well as boundaries of computer-aided decision making under the premise of incomplete information. Despite uncertainties, sustainable decision making is possible with appropriate MCDA methodologies although minimum requirements concerning both data quality and data quantity must be fulfilled. Consequently, one possible alternative could not be integrated into the case study due to incomplete information.

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“…The data files are available online at http://drugis.org/network-br. There we also provide a model for the JSMAA software (Tommi Tervonen, Rotterdam, The Netherlands) [23] that allows the reader to explore the trade-offs in an interactive graphical user interface.…”

Section: Figmentioning

confidence: 99%