Improving Selection of Analogical Inspirations with Chunking and Recombination

Abstract

Innovation is vital in various fields, and analogical thinking is a powerful tool for gen- erating creative solutions to complex problems. However, recognizing analogies can be time- consuming, and successful recognition doesn’t guarantee their adoption in innovation. In this thesis, A novel computational support system for analogical innovation is proposed that employs the cognitive mechanisms for chunking and recombination as mediums of interaction. Chunking involves identifying and extracting meaningful chunks or segments from a design problem into interactive tiles called magnets while recombination involves combining these magnets to gener- ate insightful questions that elicit divergent thinking. In this way, the proposed system aims to streamline the process of recognizing and selecting analogical inspirations for innovation while avoiding premature rejection and design fixation.To evaluate the effectiveness of the system, a within-subjects study involving 23 participants was conducted, comparing the proposed interface with a baseline. The study found that using chunking and recombination as interactive mechanisms helped prevent premature rejection of useful analogical leads, resulting in 4 times fewer ignored analogical leads. Participants were also found to make 12 times fewer changes to their decisions, given a minor increment in processing time in the order of 1.5 minutes. Overall, these results suggest that our proposed intervention is an effective tool for facilitating the selection of beneficial analogies, fostering analogical innovation through computational support.