Integrating uncertainty of preferences and predictions in decision models: An application to regional wastewater planning

Haag, Fridolin; Reichert, Peter; Maurer, Max; Lienert, Judit

doi:10.1016/j.jenvman.2019.109652

Cited by 20 publications

(16 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the uncertainty in the elicitation process by fitting value functions of different shapes to the same elicitation data (Haag et al, 2019a) allows the decision analyst to assess the structural and parametric uncertainty of the quantified preferences and is thus a cautious way of dealing with elicitation results. In addition, the quantification of the uncertainty in the value or utility function makes it possible to consider preference uncertainty in addition to outcome uncertainty of the decision alternatives in the decision support process (Cyert and de Groot, 1979;Boutilier, 2003;Haag et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

The need for unconventional value aggregation techniques: experiences from eliciting stakeholder preferences in environmental management

Reichert

Niederberger²,

Rey

et al. 2019

EURO Journal on Decision Processes

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

The need for unconventional value aggregation techniques: experiences from eliciting stakeholder preferences in environmental management

Reichert

Niederberger²,

Rey

et al. 2019

EURO Journal on Decision Processes

Self Cite

View full text Add to dashboard Cite

“…We calculated MCDA results in our new open source software "ValueDecisions" (Haag et al, subm.). ValueDecisions is based on the software and programming language R (R Core Team, 2018), earlier R scripts developed in our group (e.g., Haag et al, 2019b), and R "utility" package (Reichert et al, 2013). R scripts were rendered as web application for ValueDecisions with the "shiny" package (Shiny, 2020) Additional analyses were implemented directly in R: aggregating uncertainty of sub-attributes, weight visualization, and statistical analysis of sensitivity analyses.…”

Section: Mcda Model Integrating Predictions and Preferencesmentioning

confidence: 99%

Can MCDA guide transdisciplinary endeavors? A framework applied to co-developing a flood forecasting system in West Africa

Lienert

Andersson

Hofmann

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Climate change is projected to increase flood risks in West Africa. The EU Horizon 2020 project FANFAR co-designed a pre-operational flood forecasting and alert system for West Africa in four workshops with 50–60 stakeholders from 17 countries, adopting a Multi-Criteria Decision Analysis (MCDA) process. Firstly, we aimed to find a robust configuration of the FANFAR system. We document empirical evidence of MCDA, including stakeholder analysis, jointly creating 10 objectives, and 11 FANFAR system configurations. Stakeholders found it most important that the system produces accurate, clear, and accessible flood risk information, well before floods. Monte Carlo simulation and sensitivity analyses helped identifying three configurations that were robust despite uncertainty of expert predictions and different stakeholder preferences, elicited in group sessions. Secondly, we investigated if problem structuring helps focus early technical system development. Although partly achieved, full MCDA was necessary to provide convincingly robust configurations. Thirdly, we critically analyzed MCDA based on literature from sustainability science and transdisciplinary research. Our proposed framework consists of three steps: co-design (joint problem framing), co-production (doing research), and co-dissemination and evaluation of integrated knowledge. MCDA met many requirements, but not all. In step 1, participatory MCDA with problem structuring provides a consistent methodology, and can identify stakeholders and shared objectives to foster joint understanding. MCDA successfully contributes to step 2 by combining interdisciplinary expert knowledge, integrating conflicting stakeholder preferences, handling uncertainty, and providing unambiguous, shared results. Many elements of step 3 are not met by MCDA. We discuss this framework and using MCDA for transdisciplinary hydrology research that engages with stakeholders and society.

show abstract

“…We calculated all MCDA results in our newly developed open source software application "ValueDecisions" (Haag et al, in prep.). ValueDecisions is based on the open source software and programming language R (Team, 2018), earlier R scripts developed in our group (e.g., Haag et al, 2019b), and the R "utility" package (Reichert et al, 2013). R scripts were rendered as web application for ValueDecisions with the "shiny" package (Chang et al, 2020).…”

Section: Mcda Model Integrating Predictions and Preferencesmentioning

confidence: 99%

“…Including the uncertainty of expert estimates and stakeholder preferences in MCDA can blur results, and more elaborate models have been proposed (e.g., Haag et al, 2019b;Scholten et al, 2015). For FANFAR, simpler Multi-Attribute Value Theory and local sensitivity analyses (e.g., as in Zheng et al, 2016) were sufficient, since they enabled identifying options suiting all stakeholder groups.…”

Section: Dealing With Uncertainty Of Predictions Preferences and Model Assumptionsmentioning

confidence: 99%

Using Multi-Criteria Decision Analysis for transdisciplinary co-design of the FANFAR flood forecasting and alert system in West Africa

Lienert

Andersson

Hofmann

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Climate change is projected to increase flood risks in West Africa. The EU Horizon 2020 project FANFAR co-designed a pre-operational flood forecasting and alert system for West Africa in three lively workshops with 50–60 stakeholders, adopting a transdisciplinary framework from Multi-Criteria Decision Analysis (MCDA). We aimed to (i) exemplify MCDA as a structured transdisciplinary process; (ii) prioritize suitable FANFAR system configurations; and (iii) document and discuss empirical evidence. We used various interactive problem structuring methods in stakeholder sessions to generate 10 objectives and design 11 FANFAR system configurations. The non-additive MCDA model combined expert predictions about system performance with stakeholder preferences elicited in group sessions. All groups preferred a system producing accurate, clear, and accessible flood risk information that reaches recipients well before floods. To receive this, most groups would trade off higher operation and maintenance costs, development time, and implementing several languages. We accounted for uncertainty in expert predictions with Monte Carlo simulation. Sensitivity analyses tested the results’ robustness for changing MCDA aggregation models and diverging stakeholder preferences. Despite many uncertainties, three FANFAR system configurations achieved 63–70 % of the ideal case over all objectives in all stakeholder groups, and outperformed other options in cost-benefit visualizations. Stakeholders designed these best options to work reliably under difficult West African conditions rather than incorporating many advanced features. The current FANFAR system combines important features increasing system performance. Most respondents of a small online survey are satisfied, and willing to use the system in future. We discuss our learning drawing from design principles of transdisciplinary research. We attempted to over-come “unbalanced ownership” and “insufficient legitimacy” by including key West African institutions as consortium partners and carrying out co-design workshops with mandated representatives from 17 countries. MCDA overcomes challenges such as “lack of technical integration”, or “vagueness and ambiguity of results”. Whether FANFAR will have a “societal impact” depends on long term financing and system uptake by West African institutions after termination of EU sponsoring. We hope that our promising results will have a “scientific impact” and motivate further stakeholder engagement in hydrology research.

show abstract

Integrating uncertainty of preferences and predictions in decision models: An application to regional wastewater planning

Cited by 20 publications

References 56 publications

The need for unconventional value aggregation techniques: experiences from eliciting stakeholder preferences in environmental management

The need for unconventional value aggregation techniques: experiences from eliciting stakeholder preferences in environmental management

Can MCDA guide transdisciplinary endeavors? A framework applied to co-developing a flood forecasting system in West Africa

Using Multi-Criteria Decision Analysis for transdisciplinary co-design of the FANFAR flood forecasting and alert system in West Africa

Contact Info

Product

Resources

About