High-Speed Idea Filtering With the Bag of Lemons
As part of their involvement in CATALYST, partners participate to research papers on Collective Intelligence based on the experience they gathered. Mark Klein (MIT / University of Zürich, CATALYST’s consortium partner), in collaboration with Ana Cristina Bicharra Garcia, published, as part of the official programme of the 2015 Collective Intelligence Conference their research results on the improvement of the idea filtering processes.
Based on the previously explored concepts of multi-voting and incentive providing, they created a new approach combining the two techniques. The article summarizes their methodology from the general concept to the lesson-learned via the experiment design and the evaluation results. This new approach, called Bag of Lemons, proved to allow a faster and more accurate idea filtering compared to existing models, such as the Likert scale, by asking participants to vote for the less convincing ideas based to pre-established criteria. It seems indeed to be much easier to eliminate an idea if at least one criterion is not reached whereas it is not always as simple to identify most perspicacious ideas when you are not a complete expert.