Distributed Analogical Idea Generation with Multiple Constraints
Lixiu Yu, Robert E. Kraut, Aniket Kittur · 2016
Previous work has shown the promise of crowdsourcing analogical idea generation, where distributing the stages of analogical processing across many people can reduce fixation, identify inspirations from more diverse domains, and lead to more creative ideas. However, prior work has only considered problems with a single constraint, while many real-world problems involve multiple constraints. This paper contributes a systematic crowdsourcing approach for eliciting multiple constraints inherent in a problem and using those constraints to find inspirations useful in solving it. To do so we identify methods to elicit useful constraints at different levels of abstraction, and empirical results that identify how the level of abstraction influences creative idea generation. Our results show that crowds find the most useful inspirations when the problem domain is represented abstractly and constraints are represented more concretely.