“Searching for analogical ideas with crowds” Yu et al.
The authors provide a novel approach for finding analogies and ideas to problems and their solutions using crowdsourcing. The internet is full of solutions to different problems, and one need not reinvent the wheel every time while trying to solve a problem. Thus the authors try to reduce complicated problems from surface similarities and object attributes to structural relations or analogs which can be employed to come up with solutions of problems with different surface attributes.
They conducted an experiment where the participants were guided toward an approach for solving the problem, while the control being no constraints-no guidance. Then, 2 condition-blind judges evaluated the analogical similarities of analogs and quality of solutions. They used Intraclass Correlation Coefficient to calculate inter-rater reliabilities. Using Logistic Regression Analysis, they found that the number of valid analogs generated by Schema Conditions was significantly higher than the Features and Inspiration condition, but the quality of solutions did not differ greatly between the three.
In experiment was designed to test if one set of people could find analogs and the other could come up with solutions using those analogs. Schema-based ideas condition resulted in higher rated solution than the ones obtained as a result of conditions based on Problem Definition or Inspiration.
However, schema-based problems did equally well on the far transfer analogs as well as the source problems.
Used mediation analysis to verify the hypothesis if the solution quality is influenced by the type of idea.
The research outcomes beg the question if the entire problem-solving process can be automated. From the generation of a schema to validation of the schema, to drawing multiple analogs in different domains, to the generation of ideas to final solution generation. Another way to greatly improve problem solving is have iterations as discussed in class, starting from no guidance, to inspiration and then to the schema condition to further refine the solution quality. Maybe even extend it to prototyping and implementation.
The paper doesn’t exactly apply to my project, but for extended research, it sparked some ideas like automated generation and validation of the schema for problem-solving. The participants of the survey were a majority of Native English Speakers, which could be improved to include more culturally diverse mindsets bagging on their different problem-solving approaches. Furthermore, the approach of participants to creativity can be that of a structuralist, or an inspirationalist or a situationist. Assigning them into a condtion of problem solving may not allow their inherent natures to get to work.
[1] Lixiu Yu, Aniket Kittur, and Robert E. Kraut. Encouraging “Outside-the-box” Thinking in Crowd Innovation Through Identifying Domains of Expertise. CSCW ’16, FEBRUARY 27–MARCH2, 2016, SAN FRANCISCO, CA, USA