Lixiu Yu
Robert E. Kraut
Aniket Kittur
ABSTRACT
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.
- Teresa Amabile. 1996. Creativity in Context. Westview Press.Google Scholar
- Richard Buchanan. 1992. Wicked problems in design thinking. Design issues, 5-21.Google Scholar
- Balakrishnan Chandrasekaran. 1990. Design problem solving: A task analysis. AI magazine 11, 4: 59. Google ScholarDigital Library
- Joel Chan, Susannah B.F. Paletz, and Christian D. Schunn. 2012. Analogy as a strategy for supporting complex problem solving under uncertainty. Memory & cognition 40, 8: 1352–1365.Google Scholar
- Darren W. Dahl and Page Moreau. 2002. The Influence and Value of Analogical Thinking during New Product Ideation. Journal of Marketing Research 39, 1: 47–60.Google ScholarCross Ref
- Kevin Dunbar and Isabelle Blanchette. 2001. The in vivo/in vitro approach to cognition: The case of analogy. Trends in cognitive sciences 5, 8: 334–339.Google Scholar
- Karl Duncker and Lynne S. Lees. 1945. On problem-solving. Psychological monographs 58, 5: i-113.Google Scholar
- Ronald A. Finke, Thomas B. Ward, and Steven M. Smith. 1992. Creative cognition: Theory, research, and applications. The MIT Press.Google Scholar
- R.A. Fisher. 1954. Statistical methods for research workers. Oliver and Boyd.Google Scholar
- Dedre Gentner. 2002. Analogy in scientific discovery: The case of Johannes Kepler. In Model-Based Reasoning. Springer, 21–39.Google Scholar
- Dedre Gentner, Sarah Brem, Ron Ferguson, Philip Wolff, Arthur B. Markman, and K. D. Forbus. 1997. Analogy and creativity in the works of Johannes Kepler. Creative thought: An investigation of conceptual structures and processes, 403–459.Google Scholar
- Mary L. Gick and Keith J. Holyoak. 1980. Analogical problem solving. Cognitive psychology 12, 3: 306–355.Google Scholar
- Mary L. Gick and Keith J. Holyoak. 1983. Schema induction and analogical transfer. Cognitive psychology 15, 1: 1–38.Google Scholar
- Ashok K. Goel and Sambasiva R. Bhatta. 2004. Use of design patterns in analogy-based design. Advanced Engineering Informatics 18, 2: 85–94. Google ScholarDigital Library
- Andrew Hargadon and Robert I. Sutton. 1997. Technology brokering and innovation in a product development firm. Administrative science quarterly, 716–749.Google Scholar
- Mary B. Hesse. 1966. Models and analogies in science. University of Notre Dame Press, Notre Dame, IN.Google Scholar
- J. S. Linsey, A. B. Markman, and K. L. Wood. 2012. Design by analogy: A study of the WordTree method for problem re-representation. Journal of Mechanical Design 134, 4: 041009.Google ScholarCross Ref
- Winter Mason and Siddharth Suri. 2012. Conducting behavioral research on Amazon's Mechanical Turk. Behavior research methods 44, 1, 1–23.Google Scholar
- Scarlett R Miller, Brian P Bailey. 2014. Searching for inspiration: an in-depth look at designers' example finding practices. ASME 2014 International Design Engineering Technical Conference and Computers and Information in Engineering Conference.Google ScholarCross Ref
- Meg Monk. (2013, December). BYU engineers use origami to make more space in space. The Digital Universe, Retrieved from http://universe.byu.edu/2013/12/12/byu-engineers-use-origami-to-make-more-space-in-space/Google Scholar
- Donald A. Norman. 2002. The design of everyday things. Basic books. Google ScholarDigital Library
- Tony Poze. 1983. Analogical connections—The essence of creativity. The Journal of creative behavior 17, 4, 240–258.Google ScholarCross Ref
- Tom Ritchey. 2006. Problem Structuring Using Computer-Aided Morphological Analysis. The Journal of the Operational Research Society 57, 7, 792–801.Google ScholarCross Ref
- David Serrano. 1987. Constraint management in conceptual design. Retrieved January 13, 2015 from http://dspace.mit.edu/handle/1721.1/14689Google Scholar
- Jami J. Shah, Steve M. Smith, and Noe Vargas-Hernande. Metrics for measuring ideation effectiveness. Design studies 24, 2 (2003), 111–134.Google Scholar
- Patrick C. Shih, Gina Venolia, and Gary M. Olson. 2011. Brainstorming under constraints: why software developers brainstorm in groups. In Proceedings of the 25th BCS Conference on Human-Computer Interaction, British Computer Society, 74–83. Retrieved January 13, 2015 from http://dl.acm.org/citation.cfm?id=2305331 Google ScholarDigital Library
- Robert J. Sternberg. 1998. Handbook of creativity. Cambridge University Press.Google Scholar
- Lixiu Yu, Aniket Kittur, and Robert E. Kraut. 2014. Searching for analogical ideas with crowds. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems (CHI'14), ACM, 1225–1234. http://dl.acm.org/citation.cfm?id=2557378 Google ScholarDigital Library
- Lixiu Yu, Robert E. Kraut, and Aniket Kittur. 2014. Distributed analogical idea generation: innovating with crowds. In Proceedings of the ACM conference on human factors in computing systems (CHI'14), ACM, 1245-1254. http://dx.doi.org/10.1145/2556288.25573781 Google ScholarDigital Library
- Lixiu Yu. 2012. Crowd Idea Generation. Ph.D Dissertation. Stevens Institute of Technology, Hoboken, NJ.Google Scholar
Recommendations
Distributed analogical idea generation: inventing with crowds
CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsHarnessing crowds can be a powerful mechanism for increasing innovation. However, current approaches to crowd innovation rely on large numbers of contributors generating ideas independently in an unstructured way. We introduce a new approach called ...
Idea Generation and the Quality of the Best Idea
In a wide variety of settings, organizations generate a number of possible solutions to a problem---ideas---and then select a few for further development. We examine the effectiveness of two group structures for such tasks---the team structure, in which ...
Brainstorming under constraints: why software developers brainstorm in groups
BCS-HCI '11: Proceedings of the 25th BCS Conference on Human-Computer InteractionGroup brainstorming is widely adopted as a design method in the domain of software development. However, existing brainstorming literature has consistently proven group brainstorming to be ineffective under the controlled laboratory settings. Yet, ...
Comments