Can Online Interfaces Enhance Learning for Public Decision-Making? Eliciting Citizens’ Preferences for Multicriteria Decision Analysis

Innovative online interfaces informing and consulting citizens about their preferences for multicriteria decision analysis (MCDA) could make public decision-making more participatory. We propose a three-faceted learning for decision-making framework and used it to test newly-designed online weight elicitation interfaces. We investigated two features meant to enhance learning: fully-fledged gamification with a narrative, interaction with nonplayer characters, and ambient music, and learning loops (LL) using consistency checks of elicited weights and the challenge to resolve inconsistencies. We operationalized our framework with a novel systematic set of measure instruments providing complementary data types. We designed a 2 x 2 between-subject experiment with pre- and postquestionnaires. Answers from 769 respondents, representative of the Swiss population in age and gender, indicated that the interfaces successfully raised awareness about wastewater management. Gamification was helpful: respondents performed better in the factual learning test, and unexpected social learning occurred. However, gamification lowered the perception of process understanding. The LL were beneficial: objectively, respondents performed better in the factual learning test. However, respondents perceived the LL as cognitively demanding and their factual learning as lower. Our structured assessment highlighted the need for further research to investigate, for instance, high interpersonal variability and the disparities between tested and perceived learning. Measuring preference construction remains challenging; and social learning should be added to the assessment framework. Applying such structured assessment of learning outcomes to more traditional operational research interventions would provide a baseline for future comparison.