Computer-aided innovation

Computational explorations of compatibility and innovation IFIP CAI : October 2007 R Sosa and JS Gero

Department of Design, Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), Mexico Krasnow Institute for Advanced Study and Volgenau School of Information Technology and Engineering, George Mason University Ricardo Sosa John S. Gero R Sosa and JS Gero

Compatibility is defined as... R Sosa and JS Gero

Compatibility: coffee pods R Sosa and JS Gero

Key questions How does compatibility determine the success of innovative designs? Can we foresee the diffusion of innovations based on their compatibility? How does complexity and compatibility interact in determining success or failure? Can we find opportunities for innovation based on the compatibility of existing solutions? How to introduce novelty yet be broadly accepted? R Sosa and JS Gero

Compatibility and innovation Is compatibility likely to determine the success or failure of an innovative design? R Sosa and JS Gero

Research approach R Sosa and JS Gero

This research addresses... R Sosa and JS Gero

Computational social simulation refers to... R Sosa and JS Gero

Social simulations To study the match between individual attributes and actions within the appropriate context the relevant macro processes that facilitate diffusion, adoption and advantageous consequences of innovation Social groups whose members interact in order to generate and evaluate new ideas R Sosa and JS Gero

Key references Domain-individual-field interaction Csikszentmihalyi 1988 Design prototypes Gero 1990 Diffusion of innovations Rogers 1995 Computational social simulations Axelrod 1997 Logic, genius, chance and zeitgeist Simonton 2004 R Sosa and JS Gero

Ok, but how? R Sosa and JS Gero

System details: cellular automata (CA) A social group is implemented as a CA where a minority of cells generates numeric values and a majority of cells evaluate them by randomly activating simple rules of influence between adjacent cells in an n -dimensional grid Cycles of global convergence and divergence are generated as an aggregate effect of local influence, replicating sigmoid curves of diffusion R Sosa and JS Gero

System details: multi-agent systems (MABS) Designers in MABS are agents that generate novel solutions to problems shared by social groups Solutions are evaluated by the social group (adopters) Feedback is provided: adoption decisions & satisfaction Designers have a learning mechanism to adjust Adopters: individual perception and preferences Social interaction: agents influence decisions to adopt or reject solutions R Sosa and JS Gero

Types of issues modelled Compatibility and adoption of new ideas generative process manipulated from entirely incompatible to entirely compatible effects on adoption patterns type of innovation: disruptive – transformational Compatibility and design frequency traversing the spaces of compatibility and rate of behaviour by designer agents Compatibility and complexity traversing the compatibility and complexity spaces of new ideas in the generative processes R Sosa and JS Gero

Findings (1) Low levels of compatibility yield divergence, causing information flow to stop and precluding innovation High levels of compatibility may cause total and rapid convergence in a social group If information flow is maintained, a high rate of crossover of ideas occurs R Sosa and JS Gero

Findings (2) Low levels of compatibility yield opportunistic innovations if the rate of idea production is high enough to support a competitive environment A balance between high compatibility and low complexity may be hard to achieve as new ideas with very low levels of complexity, a small attribute variation between two designs can rapidly decrease their compatibility R Sosa and JS Gero

Design for innovation guidelines R Sosa and JS Gero

Some modelling implications Isolated characteristics of designers and their ideas are insufficient Causality in the situational factors Emergence is a key aspect This approach provides insights & another tool to reason about these challenging problems R Sosa and JS Gero

Complementary approaches R Sosa and JS Gero

http://hdl.handle.net/2123/614 Thank you! R Sosa and JS Gero

References R. Sosa, Computational Explorations of Creativity and Innovation in Design, PhD Thesis , Key Centre of Design Computing and Cognition (University of Sydney: Sydney, 2005). DK. Simonton, Creativity in Science: Chance, Logic, Genius, and Zeitgeist (Cambridge University Press: Cambridge, 2004). JM. Epstein, Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton University Press: New Jersey, 2007). EM. Rogers, Diffusion of Innovations (The Free Press: New York, 1995). D. Partridge, and J. Rowe, Computers and Creativity (Intellect: Oxford, 1994). H. Petroski, The Evolution of Useful Things (Knopf: New York, 1992). M. Csikszentmihalyi, in: The Nature of Creativity, Contemporary Psychological Perspectives , edited by RJ Sternberg (Cambridge University Press, 1988), pp. 325-339. DH. Feldman, M. Csikszentmihalyi, and H. Gardner, Changing the World: A Framework for the Study of Creativity (Praeger: Westport, 1994). JS. Gero, Design Prototypes. A Knowledge Representation Schema for Design, AI Magazine . Volume 11, Number 4, pp. 26-36 (1990). R. Sosa, and JS. Gero, in: Computational and Cognitive Models of Creative Design VI , edited by JS. Gero, and ML. Maher (University of Sydney: Sydney, 2005), pp. 19-44. R. Axelrod, The Dissemination of Culture: A Model with Local Convergence and Global Polarization, The Journal of Conflict Resolution . Volume 41, 2, pp. 203-226 (1997). R Sosa and JS Gero

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