Computer-aided innovation

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Slides presented at the CAI IFIP conference in Detroit 2007 organised by Delphi, Chrysler and Tec de Monterrey ITESM by Ricardo Sosa and John Gero

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  • Computer-aided innovation

    1. 1. Computational explorations of compatibility and innovation IFIP CAI : October 2007 R Sosa and JS Gero
    2. 2. Who? R Sosa and JS Gero
    3. 3. <ul><li>Department of Design, </li></ul><ul><li>Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), </li></ul><ul><li>Mexico </li></ul><ul><li>Krasnow Institute for Advanced Study and Volgenau School of Information Technology and Engineering, </li></ul><ul><li>George Mason University </li></ul><ul><li>Ricardo Sosa </li></ul><ul><li>John S. Gero </li></ul>R Sosa and JS Gero
    4. 4. What? R Sosa and JS Gero
    5. 5. Compatibility is defined as... R Sosa and JS Gero
    6. 6. Compatibility: coffee pods R Sosa and JS Gero
    7. 7. Why? R Sosa and JS Gero
    8. 8. Key questions <ul><li>How does compatibility determine the success of innovative designs? </li></ul><ul><li>Can we foresee the diffusion of innovations based on their compatibility? </li></ul><ul><li>How does complexity and compatibility interact in determining success or failure? </li></ul><ul><li>Can we find opportunities for innovation based on the compatibility of existing solutions? </li></ul><ul><li>How to introduce novelty yet be broadly accepted? </li></ul>R Sosa and JS Gero
    9. 9. Compatibility and innovation Is compatibility likely to determine the success or failure of an innovative design? R Sosa and JS Gero
    10. 10. How? R Sosa and JS Gero
    11. 11. Research approach R Sosa and JS Gero
    12. 12. This research addresses... R Sosa and JS Gero
    13. 13. Computational social simulation refers to... R Sosa and JS Gero
    14. 14. Social simulations <ul><li>To study the match between </li></ul><ul><ul><li>individual attributes and actions within the appropriate context </li></ul></ul><ul><ul><li>the relevant macro processes that facilitate diffusion, adoption and advantageous consequences of innovation </li></ul></ul><ul><li>Social groups whose members interact in order to generate and evaluate new ideas </li></ul>R Sosa and JS Gero
    15. 15. Key references <ul><li>Domain-individual-field interaction </li></ul><ul><ul><li>Csikszentmihalyi 1988 </li></ul></ul><ul><li>Design prototypes </li></ul><ul><ul><li>Gero 1990 </li></ul></ul><ul><li>Diffusion of innovations </li></ul><ul><ul><li>Rogers 1995 </li></ul></ul><ul><li>Computational social simulations </li></ul><ul><ul><li>Axelrod 1997 </li></ul></ul><ul><li>Logic, genius, chance and zeitgeist </li></ul><ul><ul><li>Simonton 2004 </li></ul></ul>R Sosa and JS Gero
    16. 16. Ok, but how? R Sosa and JS Gero
    17. 17. System details: cellular automata (CA) <ul><li>A social group is implemented as a CA where </li></ul><ul><ul><li>a minority of cells generates numeric values and a majority of cells evaluate them </li></ul></ul><ul><ul><li>by randomly activating simple rules of influence between adjacent cells in an n -dimensional grid </li></ul></ul><ul><li>Cycles of global convergence and divergence are generated as an aggregate effect of local influence, replicating sigmoid curves of diffusion </li></ul>R Sosa and JS Gero
    18. 18. R Sosa and JS Gero
    19. 19. System details: multi-agent systems (MABS) <ul><li>Designers in MABS are agents that generate novel solutions to problems shared by social groups </li></ul><ul><li>Solutions are evaluated by the social group (adopters) </li></ul><ul><li>Feedback is provided: adoption decisions & satisfaction </li></ul><ul><li>Designers have a learning mechanism to adjust </li></ul><ul><li>Adopters: individual perception and preferences </li></ul><ul><li>Social interaction: agents influence decisions to adopt or reject solutions </li></ul>R Sosa and JS Gero
    20. 20. Framework R Sosa and JS Gero
    21. 21. R Sosa and JS Gero
    22. 22. Types of issues modelled <ul><li>Compatibility and adoption of new ideas </li></ul><ul><ul><li>generative process manipulated from entirely incompatible to entirely compatible </li></ul></ul><ul><ul><li>effects on adoption patterns </li></ul></ul><ul><ul><li>type of innovation: disruptive – transformational </li></ul></ul><ul><li>Compatibility and design frequency </li></ul><ul><ul><li>traversing the spaces of compatibility and rate of behaviour by designer agents </li></ul></ul><ul><li>Compatibility and complexity </li></ul><ul><ul><li>traversing the compatibility and complexity spaces of new ideas in the generative processes </li></ul></ul>R Sosa and JS Gero
    23. 23. And? R Sosa and JS Gero
    24. 24. Findings (1) <ul><li>Low levels of compatibility yield divergence, causing information flow to stop and precluding innovation </li></ul><ul><li>High levels of compatibility may cause total and rapid convergence in a social group </li></ul><ul><li>If information flow is maintained, a high rate of crossover of ideas occurs </li></ul>R Sosa and JS Gero
    25. 25. Findings (2) <ul><li>Low levels of compatibility yield opportunistic innovations if the rate of idea production is high enough to support a competitive environment </li></ul><ul><li>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 </li></ul>R Sosa and JS Gero
    26. 26. Design for innovation guidelines R Sosa and JS Gero
    27. 27. Some modelling implications <ul><li>Isolated characteristics of designers and their ideas are insufficient </li></ul><ul><li>Causality in the situational factors </li></ul><ul><li>Emergence is a key aspect </li></ul><ul><li>This approach provides insights & another tool to reason about these challenging problems </li></ul>R Sosa and JS Gero
    28. 28. Where to? R Sosa and JS Gero
    29. 29. Complementary approaches R Sosa and JS Gero
    30. 30. <ul><li>http://hdl.handle.net/2123/614 </li></ul>Thank you! R Sosa and JS Gero
    31. 31. References <ul><li>R. Sosa, Computational Explorations of Creativity and Innovation in Design, PhD Thesis , Key Centre of Design Computing and Cognition (University of Sydney: Sydney, 2005). </li></ul><ul><li>DK. Simonton, Creativity in Science: Chance, Logic, Genius, and Zeitgeist (Cambridge University Press: Cambridge, 2004). </li></ul><ul><li>JM. Epstein, Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton University Press: New Jersey, 2007). </li></ul><ul><li>EM. Rogers, Diffusion of Innovations (The Free Press: New York, 1995). </li></ul><ul><li>D. Partridge, and J. Rowe, Computers and Creativity (Intellect: Oxford, 1994). </li></ul><ul><li>H. Petroski, The Evolution of Useful Things (Knopf: New York, 1992). </li></ul><ul><li>M. Csikszentmihalyi, in: The Nature of Creativity, Contemporary Psychological Perspectives , edited by RJ Sternberg (Cambridge University Press, 1988), pp. 325-339. </li></ul><ul><li>DH. Feldman, M. Csikszentmihalyi, and H. Gardner, Changing the World: A Framework for the Study of Creativity (Praeger: Westport, 1994). </li></ul><ul><li>JS. Gero, Design Prototypes. A Knowledge Representation Schema for Design, AI Magazine . Volume 11, Number 4, pp. 26-36 (1990). </li></ul><ul><li>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. </li></ul><ul><li>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). </li></ul>R Sosa and JS Gero
    32. 32. R Sosa and JS Gero

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