The use of multi-criteria decision-making methods in the integrated assessment of climate change: implications for IA practitioners

Bell, Michelle L.; Hobbs, Benjamin F.; Ellis, Hugh

doi:10.1016/s0038-0121(02)00047-2

Cited by 96 publications

(54 citation statements)

References 56 publications

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“…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 Papamichail and French (2003), Geldermann et al (2009) or Clark et al (2010.…”

mentioning

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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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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“…In the area of climate change, MCDA studies constitute a relatively narrow body of analysis in comparison to more established evaluation methods when applied to climate change policy analysis. Indeed, despite MCDA being recognized for some time as a valid tool with an important role to play in evaluating trade-offs between climate policy alternatives over multiple, disparate and often conflicting criteria (Bell et al 2001, Bell et al 2003, its use in this area remains limited. MCDA has been nonetheless applied extensively to environmental management choices (e.g.…”

Section: A Brief Review Of Methodological Approaches To Climate Policmentioning

confidence: 99%

Advancing methodological thinking and practice for development-compatible climate policy planning

Scrieciu

Belton

Chalabi

et al. 2014

Mitig Adapt Strateg Glob Change

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This version is available at https://strathprints.strath.ac.uk/45742/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Authors accepted manuscript. Published as:Scriecu, S., Belton, V., Chalabi, Z., Mechler, R., Puig, D. (2014) There are growing calls for identifying climate mitigation and adaptation policypackages that would also support human development objectives at the national and regional levels. The literature on climate policy analysis and impact assessment continues to be driven by standard economics with its body of competitive general equilibrium optimization models and cost-benefit analysis techniques of aggregation and monetization. However, its recommendations for climate action are often based on highly restrictive underlying assumptions, which have been increasingly criticized for being too prescriptive, not adequately capturing salient observed socioeconomic realities, and not acknowledging pluralism in values.The main aim of this paper is to put forward a new methodological approach that seeks to address these deficiencies. A generic but comprehensive framework eliciting mitigationadaptation-development interactions, accounting for institutional barriers, and drawing on a combination of an emerging body of new climate economics and multi-criteria decision analysis is suggested. We purport that, by using this framework, multi-dimensional impacts and multistakeholder interests could be better represented when planning climate policy actions. We also argue that analytical tools drawing on economic thinking which embraces interdisciplinary analysis and deep uncertainty and avoids the fallacy of unique optimal solutions, may deliver more effective strategies for pushing economies onto the transformational pathways required.

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“…There have been several reviews of prospective decision strategies in the arena of climate mitigation and adaptation (Toth et al 2001;Bell et al 2003;Morgan et al 2009;Means et al 2010). We will not reproduce the insights and conclusions of those reviews here but, rather, will identify some of the key decision strategies that have been identified and then briefly discuss what constraints support of those strategies might place on the UC methods developed for iRESM.…”

Section: Methodsmentioning

confidence: 99%

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

Unwin¹,

Moss²,

Rice³

et al. 2011

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SummaryThis white paper describes the results of new research to develop an uncertainty characterization (UC) process to help address the challenges of regional climate change mitigation and adaptation decisions. This research is being carried out as part of the integrated Regional Earth System Model (iRESM) initiative, a new scientific framework developed at Pacific Northwest National Laboratory to evaluate the interactions between human and environmental systems and mitigation and adaptation decisions at regional scales. The framework integrates a regional climate model; a regional energyeconomy model; and highly spatially-resolved models of crop productivity, building energy demands, electricity infrastructure operation and expansion, and water supply and management. The iRESM framework is intended to help regional stakeholders (scientists as well as decision makers) understand the consequences of climate change as well as the consequences of policies to mitigate or adapt to such change within regions.The initiative has developed the following four science questions to guide its research:• How do intrinsic regional characteristics shape, enhance, or constrain regional mitigation and adaptation opportunities?• How do projected changes in mean climate versus climate extremes affect the development of adaptation and mitigation strategies?• How might interactions between management decisions and natural processes contribute to rapid or nonlinear changes, and do they contribute to climate feedbacks?• How will adaptation and mitigation strategies interact in the next few decades in terms of achieving their respective goals?An important consideration for the iRESM initiative is that Earth system mechanisms and future changes are imperfectly understood and in some cases deeply uncertain-especially at the level of resolution required for regional analyses and decision making. The UC process developed for the initiative addresses uncertainty by first identifying through sensitivity analysis the key uncertainties in data inputs, individual model structures, and coupled models that are important for particular stakeholder questions and evaluation criteria. These key uncertainties are then characterized and propagated to determine the robustness of the framework's results for the particular questions, thereby providing insights for researchers and decision makers alike. The process differs from many traditional applications of uncertainty quantification (UQ) because of its focus on stakeholder needs and its allowance for qualitative and semi-quantitative methods for describing uncertainty. The process not only permits the dimensionality of the UQ problem to be reduced, it also allows research efforts to be targeted at the uncertainties that really matter for the question in hand.This decision-specific orientation for UC has multiple implications for the iRESM initiative that will continue to be explored, including: the importance of stakeholder interactions and the development of methods for communicating results; the dev...

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The use of multi-criteria decision-making methods in the integrated assessment of climate change: implications for IA practitioners

Cited by 96 publications

References 56 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

Advancing methodological thinking and practice for development-compatible climate policy planning

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

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