Using imprecise estimates for weights

Jessop, Alan

doi:10.1057/jors.2010.46

Cited by 12 publications

(12 citation statements)

References 37 publications

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“…The application of precise preference information in the DSS might therefore not be feasible motivating the application of parameter ranges for one or multiple preference parameters (Ríos Insua and French, 1991;Butler et al, 1997;Matsatsinis and Samaras, 2001;Jiménez et al, 2005;Mustajoki et al, 2005;Mateos et al, 2006;Mavrotas and Trifillis, 2006;Jessop, 2011;Jessop, 2014;Scholten et al, 2015).…”

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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“…They all acknowledge that variations in the criteria weights can have big influences on the results of a MCDA. Recently Jessop [10] has suggested a method for modelling uncertainties using probabilistic weight, Rios Insua and French [11] propose an SA method to find the competitors of a current optimal alternative. In Butler et al [12] and Butler and Olson [13] a method for doing simulation over the criteria weights while the rank order weights on the measures is maintained, but the weights are otherwise generated at random.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Comparisons of robustness measures as a communicative means for involvement of decision makers

Jensen¹

2012

WIT Transactions on the Built Environment

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Decisions about infrastructure projects or new policies in the transport sector have traditionally been based on cost benefit analysis (CBA). However, as society in general becomes more and more complex, this affects the decisionmaking process. Thus decision-makers are confronted with the difficult problem of evaluating potential outcomes and choosing policies to achieve the desired outcomes in the presence of this complexity. In this respect, multi-criteria decision analysis (MCDA) becomes a useful tool for the decision-makers as this type of analysis is able to perform an assessment based on a more comprehensive evaluation framework by also taking into account non-quantifiable impacts. This article concerns decision-making relating to transport projects involving multiple objectives (MCDA); especially it addresses how to measure the robustness of these decisions as regards involving views of multiple and diversified stakeholders within the MCDA. The communicative means for involving stakeholders and decision-makers in the decision process are also discussed. Specifically, based on theory and case studies, a comparison of different measures for decision robustness are treated including also how these measures can be communicated to the decision-makers. Furthermore, it is examined how the choice of MCDA methodology can affect the robustness. Finally, in addition to summarising the findings, some recommendations for applying robustness measures are given.

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“…Since the swing weight values are relative, w 0 can be fixed to 1 throughout. In a standard decision analysis, the remaining swing weights are known to represent the decisionmaker’s preferences with certainty because they are elicited by the analyst directly from the decisionmaker (though see Jessop ( 20 ) for an example with weight uncertainty for an elicited decisionmaker).…”

Section: Formalismmentioning

confidence: 99%

“…( 19 ) utilized Monte Carlo simulations to model weight uncertainty in a multiattribute utility model for the purposes of sensitivity analysis, identifying some situations where superior alternatives eliminated the need to conduct a formal weight assessment. Jessop ( 20 ) used properties of Dirichlet distributions to incorporate uncertainty and inconsistency in weight judgments.…”

Section: Introductionmentioning

confidence: 99%

Estimating Conditional Probabilities of Terrorist Attacks: Modeling Adversaries with Uncertain Value Tradeoffs

Levine¹

2011

Risk Analysis

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Many analyses conducted to inform security decisions depend on estimates of the conditional probabilities of different attack alternatives. These probabilities are difficult to estimate since analysts have limited access to the adversary and limited knowledge of the adversary's utility function, so subject matter experts often provide the estimates through direct elicitation. In this article, we describe a method of using uncertainty in utility function value tradeoffs to model the adversary's decision process and solve for the conditional probabilities of different attacks in closed form. The conditional probabilities are suitable to be used as inputs to probabilistic risk assessments and other decision support techniques. The process we describe is an extension of value-focused thinking and is broadly applicable, including in general business decision making. We demonstrate the use of this technique with simple examples.

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Using imprecise estimates for weights

Cited by 12 publications

References 37 publications

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

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

Comparisons of robustness measures as a communicative means for involvement of decision makers

Estimating Conditional Probabilities of Terrorist Attacks: Modeling Adversaries with Uncertain Value Tradeoffs

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