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Found 3 papers, 1 papers with code
Leveraging Language Models and Bandit Algorithms to Drive Adoption of Battery-Electric Vehicles
Prior work has demonstrated that interventions for behavior must be personalized, and that the intervention that is most effective on average across a large group can result in a backlash effect that strengthens opposition among some subgroups.
Understanding the Cognitive Complexity in Language Elicited by Product Images
Product images (e. g., a phone) can be used to elicit a diverse set of consumer-reported features expressed through language, including surface-level perceptual attributes (e. g., "white") and more complex ones, like perceived utility (e. g., "battery").
Understanding and Shifting Preferences for Battery Electric Vehicles
In this work, we focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs).
