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Collective Intelligence in Design Crowdsourcing

Author

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  • Jonathan Dortheimer

    (MTRL Laboratory, Faculty of Architecture and Town Planning, Technion—Israel Institute of Technology, Haifa 3200003, Israel)

Abstract

This study investigates how collective intelligence emerges in crowdsourcing for architectural design. Previous studies have revealed that collective intelligence emerges from collaboration and can outperform individual intelligence. As design is a highly collaborative practice, collective intelligence plays a vital role in the design process. In this study, we compare the structure of two architectural design crowdsourcing systems using several methods. The results of the analysis suggest that design crowdsourcing systems can give rise to the following three types of collective intelligence: (1) discussive, which emerges from a conversation between designers and clients; (2) synthetic, which emerges from a parallel and sequential design development; and (3) evaluative, which is based on the wisdom of the crowd in evaluating and selecting designs. The article concludes with recommendations for collaborative design method.

Suggested Citation

  • Jonathan Dortheimer, 2022. "Collective Intelligence in Design Crowdsourcing," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:539-:d:745373
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    References listed on IDEAS

    as
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    3. David Engel & Anita Williams Woolley & Lisa X Jing & Christopher F Chabris & Thomas W Malone, 2014. "Reading the Mind in the Eyes or Reading between the Lines? Theory of Mind Predicts Collective Intelligence Equally Well Online and Face-To-Face," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-16, December.
    4. Anita Williams Woolley, 2009. "Means vs. Ends: Implications of Process and Outcome Focus for Team Adaptation and Performance," Organization Science, INFORMS, vol. 20(3), pages 500-515, June.
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    Cited by:

    1. Jiali Wang & Changbing Tang & Jianquan Lu & Guanrong Chen, 2023. "Toward Zero-Determinant Strategies for Optimal Decision Making in Crowdsourcing Systems," Mathematics, MDPI, vol. 11(5), pages 1-21, February.

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