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A computational study of creativity in design: The role of society

Published online by Cambridge University Press:  02 November 2005

RICARDO SOSA  and
JOHN S. GERO
RICARDO SOSA
Affiliation:
Key Centre of Design Computing and Cognition, University of Sydney, Sydney, Australia
JOHN S. GERO
Affiliation:
Key Centre of Design Computing and Cognition, University of Sydney, Sydney, Australia
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Abstract

Studies of creativity have tended to focus on isolated individuals, under the assumption that it can be defined as a characteristic of an extraordinary person, product, or process. Existing computational models of creative behavior have inherited this emphasis on independent generative processes. However, an increasing multidisciplinary consensus regards creativity as a systems property, and extends the focus of inquiry to include the interaction between generative individuals and evaluative social groups. To acknowledge the complementarity of evaluative processes by social groups, experts, and peers, this paper presents experimentation with a framework of design as a social activity. This model is used to inspect phenomena associated with creativity in the interaction between designers and their societies. In particular, this paper describes the strength of social ties as a mechanism of social organization, and explores its potential relation to creativity in a computational social simulation. These experiments illustrate ways in which the role of designers as change agents of their societies can be largely determined by how the evaluating group self-organizes over time. A key potential implication is that the isolated characteristics of designers may be insufficient to formulate conclusions about the nature and effects of their behavior. Instead, causality could be attributed to situational factors that define the relationship between designers and their evaluators.

Keywords

CreativityDesign AgentsInnovation DiffusionSituated BehaviorSocial Simulation
Type
Research Article
Information
AI EDAM , Volume 19 , Issue 4 , November 2005 , pp. 229 - 244
Copyright
© 2005 Cambridge University Press

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References

REFERENCES

Alder, G. (2004). JGraph, open source Java Graph visualization. Accessed on-line September 2004 at http://www.jgraph.com
Amabile, T. & Hennessey, B.A. (1999). Consensual assessment. In Encyclopedia of Creativity (Runco, M.A. & Pritzer, S.R., Eds.), pp. 347–360. San Diego, CA: Academic.
Asch, S.E. (1955). Opinions and social pressure. Scientific American 193(5), 31–35.Google Scholar
Barabasi, A.L., Ravasz, E., & Vicsek, T. (2001). Deterministic scale-free networks. Physica A: Statistical Mechanics and its Applications 299(3–4), 559–564.Google Scholar
Boden, M. (1999). Computer models of creativity. In Handbook of Creativity (Sternberg, R., Ed.), pp. 351–372. Cambridge: Cambridge University Press.
Boden, M.A. (1994). Dimensions of Creativity. Cambridge, MA: MIT Press.
Boyd, R. & Richerson, P.J. (1995). Why does culture increase human adaptability? Ethology and Sociobiology 16(2), 125–143.Google Scholar
Castelfranchi, C. (2001). The theory of social functions: Challenges for computational social science and multi-agent learning. Cognitive Systems Research 2(1), 5–38.Google Scholar
Collier, N. (2004). Repast: An agent based modelling toolkit for Java. Accessed on-line August 2004 at http://repast.sourceforge.net
Dorfman, R. (1979). Formula for Gini coefficient. Review of Economics Statistics 61(2), 146–149.Google Scholar
Feldman, D.H., Csikszentmihalyi, M., & Gardner, H. (1994). Changing the World: A Framework for the Study of Creativity. Westport, CT: Praeger.
Freeman, L.C. (1998). Exploring social structure using dynamic three-dimensional color images. Social Networks 20(2), 109–118.Google Scholar
Gabora, L. (1995). Memes and variations: A computational model of cultural evolution. In 1993 Lectures in Complex Systems (Nadel, N. & Stein, D.L., Eds.), pp. 471–486. Reading, MA: Addison–Wesley.
Gardner, H. (1993). Creating Minds: An Anatomy of Creativity Seen through the Lives of Freud, Einstein, Picasso, Stravinsky, Eliot, Graham, and Gandhi. New York: Basic Books.
Gardner, H. (1994). The creators' patterns. In Changing the World, A Framework for the Study of Creativity (Feldman, D.H., Csikszentmihalyi, M. & Gardner, H., Eds.), pp. 69–85. Westport, CT: Praeger.
Gero, J.S. (2000). Computational models of innovative and creative design processes, Technological Forecasting and Social Change 64(2–3), 183–196.Google Scholar
Gilbert, G.N. & Troitzsch, K.G. (1999). Simulation for the Social Scientist. Buckingham: Open University Press.
Goel, V. (1994). A comparison of design and nondesign problem spaces. Artificial Intelligence in Engineering 9(1), 53–72.Google Scholar
Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology 78(6), 1360–1380.Google Scholar
Heck, P.S. & Ghosh, S. (2000). A study of synthetic creativity: behavior modeling and simulation. IEEE Intelligent Systems and their Applications 15(6), 58–66.Google Scholar
Hoschek, W. (2002). The Colt 1.0.3 distribution: open source libraries for high performance scientific and technical computing in Java. Accessed on-line August 2002 at http://cern.ch/hoschek/colt
Howe, M.J.A., Davidson, J.W., & Sloboda, J.A. (1999). Innate traits: reality or myth? In The Nature–Nurture Debate, The Essential Readings (Ceci, S.J. & Williams, W.M., Eds.), pp. 258–289. Malden: Blackwell.
Khan, A. (2004). JExcel: A Java library for reading/writing Excel. Accessed on-line August 2004 at http://jexcelapi.sourceforge.net
Marsden, P.V. & Campbell, K. (1984). Measuring tie strength. Social Forces 63(2), 482–501.Google Scholar
Nattermann, P.M. (2000). Best practice does not equal best strategy. The McKinsey Quarterly 2000(2), 38–45.Google Scholar
Purcell, A.T. & Gero, J.S. (1996). Design and other types of fixation. Design Studies 17(4), 363–383.Google Scholar
Rogers, E.M. (1995). Diffusion of Innovations. New York: Free Press.
Ross, L. & Nisbett, R.E. (1991). The Person and the Situation: Perspectives of Social Psychology. Philadelphia, PA: Temple University Press.
Rudowicz, E. (2003). Creativity and culture: a two way interaction. Scandinavian Journal of Educational Research 47(3), 273–290.Google Scholar
Runco, M.A. & Pritzker, S. (1999). Encyclopedia of Creativity. San Diego, CA: Academic.
Simonton, K. (2000). Creativity. American Psychologist 55(1), 151–172.Google Scholar
Sosa, R. & Gero, J.S. (2003). Social change: exploring design influence. In Multi-Agent-Based Simulation III (Hales, D., Edmonds, B., Norling, E., & Rouchier, J., Eds.), pp. 106–110. Berlin: Springer.
Sternberg, R.J. (1999). Handbook of Creativity. Cambridge: Cambridge University Press.
Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. New York: Cambridge University Press.
Wooldridge, M.J. (2000). Reasoning about Rational Agents. Cambridge, MA: MIT Press.