Recommendations for online elicitation of swing weights from citizens in environmental decision-making - Alice H. Aubert; Fabien Esculier; Judit Lienert (Operations Research Perspectives, 2020)
There is a growing demand for public participation in environmental decision-making. However, it is unclear how a large number of citizens can best engage in such complex public policy decision processes. This need from the civil society challenges the OR community to develop online decision-making tools. This article reports on a feasibility assessment of swing weight elicitation, implemented online, for real-world decisions about future wastewater infrastructure. Eliciting weights with the swing method is common in MAVT/MAUT, but not online. A total of 298 affected citizens from the Paris region answered the online swing weight elicitation survey. Another 357 citizens directly rated objectives. Three aspects of learning in the context of MCDA were considered: did participants learn facts about the wastewater topic? Did they comply with the swing elicitation process, i.e. follow the instructions? Did participants learn about their preferences? Factual learning was limited. Process compliance was really low (12%), leading to a number of recommendations for improving the interface for online swing weight elicitation. The collected preferences differed statistically significantly between the compliant and non-compliant participants, and also between the non-compliant and direct rating respondents. This emphasised the effect of the elicitation method on preference construction. Moreover, more participants experienced a strengthening of pre-existing opinions than a change in opinion, and most reported being uncertain about their answers. This calls for better understanding process learning and preference construction. We discuss our developed procedure for online swing weight elicitation, recommend ways to improve swing online surveys, and suggest interesting future research lines that would allow empirically verifying our propositions.
Keywords: 330; Behavioural Operational Research; OR in environment and climate change; Learning; Multi-Criteria Decision Analysis; Decision Support System; Public participation; E-democracy