Lixiu Yu
Jeffrey V. Nickerson
ABSTRACT
A sketch combination system is introduced and tested: a crowd of 1047 participated in an iterative process of design, evaluation and combination. Specifically, participants in a crowdsourcing marketplace sketched chairs for children. One crowd created a first generation of chairs, and then successive crowds created new generations by combining the chairs made by previous crowds. Other participants evaluated the chairs. The crowd judged the chairs from the third generation more creative than those from the first generation. An analysis of the design evolution shows that participants inherited and modified presented features, and also added new features. These findings suggest that crowd based design processes may be effective, and point the way toward computer-human interactions that might further encourage crowd creativity.
- von Ahn, L. and Dabbish, L. Labeling images with a computer game. In Proc. CHI 2004, ACM Press (2004), 319--326. Google Scholar
Digital Library
- Amazon Mechanical Turk. https://www.mturk.com/mturk/welcome.Google Scholar
- Amabile, T. M., R. Conti, H. Coon, J. Lazenby, and M. Herron. Assessing the work environment for creativity. Academy of Management Journal, 39 (1996), 1154--1184.Google ScholarCross Ref
- Benkler, Y. The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press, New Haven, CT, USA, 2006. Google ScholarDigital Library
- Brabham, D. Moving the crowd at Threadless: Motivations for participation in a crowdsourcing application. Information, Communication & Society, 13 (2010), 1122--1145.Google Scholar
- Campbell, D. T. Blind variation and selective retention in creative thought as in other knowledge processes. Psychological Review, 67 (1960), 380--400.Google ScholarCross Ref
- Deb, K. Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester, UK, 2001. Google ScholarDigital Library
- Finke, R., Ward, T., and Smith, S. Creative Cognition: Theory, Research, and Applications. MIT Press Cambridge, MA, USA, 1996.Google ScholarCross Ref
- Fischer, G. Social Creativity: Turning Barriers into Opportunities for Collaborative Design. In Proc. PDC2004, 152--161. Google ScholarDigital Library
- Gero J. S., and Maher M. L. (eds) Modeling creativity and knowledge-based creative design. Lawrence Erlbaum, Hillsdale, NJ, USA, 1993. Google ScholarDigital Library
- Goldberg, D. E. Genetic Algorithms in Search, Optimization and Machine Learning. Kluwer Academic Publishers, Boston, MA, USA, 1989. Google ScholarDigital Library
- Goldschmidt, G, and Litan Sever A. From text to design solution: inspiring design ideas with texts. In Proc. ICED 2009.Google Scholar
- Google Docs Drawing Application. http://docs0.google.com/demo/edit?id=scACRQaIm3t83kVISWPhWfrqx#drawing.Google Scholar
- Heit, E. Influences of prior knowledge on selective weighting of category members. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24 (1998), 712--731.Google Scholar
- Holland. J. H. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975.Google ScholarDigital Library
- Hu, C., Bederson, B. B., and Resnik, P. Translation by iterative collaboration between monolingual users. In Proc. of Graphics Interface 2010, Canadian Information Processing Society (2010), 39--46. Google ScholarDigital Library
- Kittur, A. Crowdsourcing, collaboration and creativity. XRDS: Crossroads, December (2010). Google ScholarDigital Library
- Kittur, A., Chi, E. H., and Suh, B. Crowdsourcing user studies with Mechanical Turk. In Proc. CHI 2008, ACM Press (2008), 453--456. Google ScholarDigital Library
- Kosorukoff, A. Human based genetic algorithm. In Proc. IEEE Conference on Systems, Man, and Cybernetics, 2001, 3464--3469.Google ScholarCross Ref
- Little, G., Chilton, L. B., Goldman, M., and Miller, R. C. Exploring iterative and parallel human computation processes. In Proc. of the ACM SIGKDD Workshop on Human Computation (2010), 68--76. Google ScholarDigital Library
- Lorge, I., Fox, D., Davitz, J., and Brenner, M. A. Survey of Studies Contrasting the Quality of Group Performance and Individual Performance, 1920-1957, Psychological Bulletin 55, 6 (1958), 337--372.Google ScholarCross Ref
- Maher, M. L. Design Creativity Research: From the Individual to the Crowd. Design Creativity 2010.Google Scholar
- McDonald, D. W. Social Computing: A Research Topic in Search of Disciplinarity. In the Research Directions for Social Computing Workshop at the ACM International Conf. on Supporting Group Work (2007).Google Scholar
- Mednick, S. A. The associative basis of the creative process. Psychological Review 69, 1962, 220--232.Google ScholarCross Ref
- Mullen, B., Johnson C. and Salas E. Productivity Loss in Brainstorming Groups: A Meta-Analytic Integration, Basic and Applied Social Psychology 72, 1 (1991), 3--23.Google Scholar
- Nunamaker, J., Dennis, A. R., Valacich, J. S., Vogel, D. R. and George, J. F. Electronic Meeting Systems to Support Group Work, Communications of the ACM 34, 7 (1991), 40--61. Google ScholarDigital Library
- Osborn, A. F. Applied imagination: Principles and procedures of creative problem solving (3rd Edition). Scribners, New York, USA, 1963.Google Scholar
- Palmeri, T. J., and Nosofsky, R. M. Recognition memory for exceptions to the category rule. Journal of Experimental Psychology: Learning, Memory, & Cognition, 21 (1995), 548--568.Google Scholar
- Quinn, A. J., and Bederson, B. B. Human Computation: A Survey and Taxonomy of a Growing Field, CHI 2011, ACM Press (2011). Google ScholarDigital Library
- Raykar, V. C., Yu, S., Zhao, L. H., Valadez, G. H., Florin, C., Bogoni, L., and Moy, L. Learning from crowds. Journal of Machine Learning Research. 11, 7 (2010), 1297--1322. Google ScholarDigital Library
- Rosch, E. H. Principles of categorization. In E. Rosch and B. Lloyd, eds. Cognition and Categorization. Lawrence Erlbaum Associates, 1978, 27--48.Google Scholar
- Ross, J., Irani, L., Silberman, M. S., Zaldivar, A., and Tomlinson, B. Who are the Crowdworkers? Shifting Demographics in Mechanical Turk. Ext. Abstracts CHI 2010, ACM Press (2010). Google ScholarDigital Library
- Sack, W., Détienne, F., Burkhardt, J.-M., Barcellini, F., Ducheneaut, N., and Mahendran, D. A methodological framework for socio-cognitive analyses of collaborative design of Open Source Software. Distributed Collective Practices Workshop at CSCW, (2004).Google Scholar
- Sakamoto, Y., and Love, B. C. Vancouver, Toronto, Montreal, Austin: Enhanced oddball memory through differentiation, not isolation. Psychonomic Bulletin & Review, 13 (2006), 474--479.Google Scholar
- Simonton, D. K. Scientific creativity as constrained stochastic behavior: The integration of product, person, and process perspectives. Psychological Bulletin, 129 (2003), 475--494.Google ScholarCross Ref
- Smith, S. M., Ward, T. B., and Schumacher, J. S. Constraining effects of examples in a creative generation task. Memory & Cognition, 21 (1993), 837--845.Google ScholarCross Ref
- Spears, W. M. Crossover or Mutation? In Foundations of Genetic Algorithms 2. L.D. Whitley (ed). Morgan Kaufmann, CA, USA, 1993.Google Scholar
- Suwa, M. and Tversky, B. What do architects and students perceive in their design sketches? A protocol analysis. Design Studies 18, 4 (1997), 385--403.Google ScholarCross Ref
- Tech Guru, Open Source Cars, http://www.techquark.com/2010/05/open-source-cars.html, May 26, 2010, accessed January 10, 2010.Google Scholar
- Thagard, P. Conceptual revolutions. Princeton University Press, USA, 1992.Google ScholarCross Ref
- Tversky, B. and Chou, J. Creativity: depth and breadth. ICDC 2010.Google Scholar
- Voiklis, J. A Thing Is What We Say It Is: Referential Communication and Indirect Category Learning, Ph.D. dissertation, Columbia University, New York, USA, 2008.Google Scholar
Index Terms
- Cooks or cobblers?: crowd creativity through combination
Index terms have been assigned to the content through auto-classification.
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