Skip to main content
SpringerLink
Log in

Possible of Design

  • Reference work entry
  • First Online:
The Palgrave Encyclopedia of the Possible

  • 36 Accesses

Abstract

Design means conceiving of objects or processes for accomplishing goals and it is a way to intentionally changing part of the world, as it is the case in prospective ergonomics. The design process usually requires creativity since designers have to propose objects or processes that are both new/novel – and possibly unexpected – and adapted to the design situation (e.g., useful and usable by future end-users). The goals of a particular design task and the intended functions of the design to be produced are not fully available at the outset. Thus, part of designing consists in constructing those goals and functions. It occurs as designing proceeds and it has the potential to alter what has been decided before, which makes designing non-monotonic across time. The cognitive processes and mechanisms involved in the creative design activities are of particular interest and are described in this entry as well as potential ways to favor creative design.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 849.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 899.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Bonnardel, N. (2000). Towards understanding and supporting creativity in design: Analogies in a constrained cognitive environment. Knowledge-Based Systems, 13, 505–513.

    Article  Google Scholar 

  • Bonnardel, N. (2002). Entrée : Créativité [Entry: Creativity]. In G. Tiberghien (Ed.), Dictionnaire des Sciences Cognitives [Cognitive sciences dictionary] (pp. 95–97). Armand Colin/VUEF.

    Google Scholar 

  • Bonnardel, N. (2006/2012a). Créativité et Conception : Approches Cognitives et Ergonomiques [Creativity and Design: Cognitive and Ergonomics Approaches]. De Boeck (réédition).

    Google Scholar 

  • Bonnardel, N. (2012b). Designing future products: What difficulties do designers encounter and how can their creative process be supported? Work, A Journal of Prevention, Assessment & Rehabilitation, 41, 5296–5303.

    Google Scholar 

  • Bonnardel, N. (2021). Entrée : Créativité et conception [Entry : Creativity and design]. In E. Brangier & G. Vallery (Eds.), Dictionnaire Encyclopédique de l’Ergonomie : 150 notions clés [Encyclopedic Dictionary of Ergonomics: 150 key concepts]. Paris: Dunod.

    Google Scholar 

  • Bonnardel, N., & Bouchard, C. (2011). Towards supporting creative design: Analysis of the use of the TRENDS system according to designers’ expertise. In Proceedings of the 8thACM Conference on Creativity and Cognition – C&C 2011 (pp. 315–316), Atlanta. New York: ACM Press.

    Google Scholar 

  • Bonnardel, N., & Bouchard, C. (2017). Creativity in design. In J. C. Kaufman, V. P. Glaveanu, & J. Baer (Eds.), Cambridge handbook of creativity across different domains (pp. 403–427). New York: Cambridge University.

    Chapter  Google Scholar 

  • Bonnardel, N., & Didier, J. (2016). Enhancing creativity in the educational design context: An exploration of the effects of design project-oriented methods on students’ evocation processes and creative output. Journal of Cognitive Education and Psychology, 15(1), 80–101. https://doi.org/10.1891/1945-8959.15.1.80.

    Article  Google Scholar 

  • Bonnardel, N. & Didier, J. (2020). Brainstorming variants to favor creative design. Applied Ergonomics. 83. 102987 [doi.org/https://doi.org/10.1016/j.apergo.2019.102987].

  • Bonnardel, N., & Lubart, T. (2019). La créativité : approches et méthodes en psychologie et en ergonomie. RIMHE – Revue Interdisciplinaire, Management, Homme & Entreprise, 37, 79–98.

    Article  Google Scholar 

  • Bonnardel, N., & Pichot, N. (2020). Enhancing collaborative creativity with virtual dynamic personas. Applied Ergonomics, 82, 102949. https://doi.org/10.1016/j.apergo.2019.102949.

    Article  Google Scholar 

  • Bonnardel, N., & Zenasni, F. (2010). The impact of technology on creativity in design: An enhancement? Creativity and Innovation Management, 19(2), 180–191.

    Article  Google Scholar 

  • Bonnardel, N., Forens, M., & Lefevre, M. (2016). Enhancing collective creative design: An exploratory study on the influence of static and dynamic personas in a virtual environment. Design Journal, 19(2), 221–235. https://doi.org/10.1080/14606925.2016.112914.

    Article  Google Scholar 

  • Bonnardel, N., Wojtczuk, A., Gilles, P.-Y., & Mazon, S. (2018). The creative process in design. In T. Lubart (Ed.), The creative process: Perspectives from multiple domains (pp. 229–254). New York: Palgrave Macmillan.

    Chapter  Google Scholar 

  • Bouchard, C., Camous, R., & Aoussat, A. (2005). Nature and role of intermediate representation (IR) in the design process: Case studies in car design. International Journal of Vehicle Design, 38(1), 1–25.

    Article  Google Scholar 

  • Bouchard, C., Omhover, J.-F., Mougenot, C., Aoussat, A., & Westerman S. (2008). Trends: A content-based information retrieval system for designers. In Third international conference on design computing and cognition (DCC’08). Atlanta: Georgia Institute of Technology.

    Google Scholar 

  • Christiaans, H. (2002). Creativity as a design criterion. Creativity Research Journal, 14, 41–45.

    Article  Google Scholar 

  • Chrysikou, E., & Gero, J. S. (2020). Using neuroscience techniques to understand and improve design cognition. AIMS Neuroscience, 7(3), 319–326. https://doi.org/10.3934/Neuroscience.2020018.

    Article  Google Scholar 

  • Chrysikou, E., Berryhill, M. E., Bikson, M., et al. (2017). Editorial: Revisiting the effectiveness of transcranial direct current brain stimulation for cognition: Evidence, challenges, and open questions. Frontiers in Human Neuroscience, 11, Article 448. https://doi.org/10.3389/fnhum.2017.00448.

    Article  Google Scholar 

  • Clancey, W. J. (1997). Situated Cognition: On Human Knowledge and Computer Representations, Cambridge University Press.

    Google Scholar 

  • Didier, J., & Bonnardel, N. (Eds., 2020). Didactique de la Conception [The Didactics of Design]. Belfort : Presses de l’UTBM.

    Google Scholar 

  • Eastman, C.M. (1969). Cognitive processes and ill-defined problems: A case study from design. In Proceedings of the 1stinternational joint conference on artificial intelligence (pp. 669–690). Washington, DC.

    Google Scholar 

  • Gero, J. S. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine, 11(4), 26–36.

    Google Scholar 

  • Gero, J. S. (2000). Computational models of innovative and creative design processes. Technological Forecasting and Social Change, 64, 183–196.

    Article  Google Scholar 

  • Gero, J. S. (2020). Nascent directions for design creativity research. International Journal of Design Creativity and Innovation, 8(3), 144–146. https://doi.org/10.1080/21650349.2020.1767885.

    Article  Google Scholar 

  • Gero, J. S., & Kannengiesser, U. (2004). The situated function-behaviour-structure framework. Design Studies, 25(4), 373–391.

    Article  Google Scholar 

  • Gero, J. S. & Maher, M. L. (Eds, 1993). Modeling Creativity and Knowledge-Based Creative Design, Lawrence Erlbaum.

    Google Scholar 

  • Goldschmidt, G. (1991). The dialectics of sketching. Creativity Research Journal, 4, 123–143.

    Article  Google Scholar 

  • Jiménez-Narvàez, L. M., & Gardoni, M. (2014). Developing design concepts in a cloud computing environment: Creative interactions and brainstorming modalities. Digit. Creativ., 25(4), 295–312. https://doi.org/10.1080/14626268.2013.858750.

    Article  Google Scholar 

  • Karimi, P., Maher, M. L., Davis, N., & Grace, K. (2019). Deep learning in a computational model for conceptual shifts in a co-creative design system. arXiv preprint arXiv:1906.10188.

    Google Scholar 

  • Karimi, K., Rezwana, J., Siddiqui, S., Maher, M. L., & Dehbozorgi, N. (2020). Creative sketching partner: An analysis of human-AI co-creativity. In Proceedings of the 25th international conference on intelligent user interfaces (IUI ‘20) (pp. 221–230). Association for Computing Machinery. https://doi.org/10.1145/3377325.3377522.

  • Lubart, T. (2005). How can computers be partners in the creative process? International Journal of Human Computer Studies, 63, 365–369.

    Article  Google Scholar 

  • Mayseless, N., & Shamay-Tsoory, S. G. (2015). Enhancing verbal creativity: Modulating creativity by altering the balance between right and left inferior frontal gyrus with tDCS. Neuroscience, 291, 167–176.

    Article  Google Scholar 

  • Osborn, A. F. (1963). Applied imagination: Principles and procedures for problem-solving (3rd ed.). New York: Charles Scribner’s Sons.

    Google Scholar 

  • Quanz, B., Sun, W., Deshpande, A., Shah, D. & Park, J. (2019). Machine learning based co-creative design framework. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver.

    Google Scholar 

  • Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf.

    Google Scholar 

  • Reitman, W. R. (1964). Heuristic decision procedures, open constraints and the structure of ill-defined problems. In M. W. Shelly & G. L. Bryan (Eds.), Human judgments and optimality (pp. 282–315). New York: Wiley & Sons.

    Google Scholar 

  • Rittel, H., & Webber, M. M. (1984). Planning problems are wicked problems. In N. Cross (Ed.), Developments in design methodology (pp. 135–144). New York: John Wiley & Sons.

    Google Scholar 

  • Robert, J. M., & Brangier, E. (2012). Prospective ergonomics: Origin, goal, and prospects. Work, 41, 5235–5242.

    Article  Google Scholar 

  • Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96.

    Article  Google Scholar 

  • Schön, D. A. (1983). The reflective practitioner: How professionals think in action. New York: Basic Books.

    Google Scholar 

  • Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. Harper.

    Google Scholar 

  • Shealy, T., Gero, J. S., Milovanovic, J. & Hu, M. (2020). Sustaining creativity with neuro-cognitive feedback: A preliminary study, In Boujut, J-F., Gaetano, C., Ahmed-Kristensen, S., Georgiev, G. & Iivari, N. (Eds), Proceedings of the sixth international conference on design creativity (ICDC 2020), Design Society, pp. 84.91. https://doi.org/10.35199/ICDC.2020.11.

  • Simon, H. A. (1973). The structure of ill structured problems. Artificial Intelligence, 4, 181–201.

    Article  Google Scholar 

  • Simon, H. A. (1995). Problem forming, problem finding and problem solving in design. In A. Collen & W. Gasparski (Eds.), Design & Systems (pp. 245–257). New Brunswick: Transaction Publishers.

    Google Scholar 

  • Suwa, M., Gero, J. S., & Purcell, T. (2000). Unexpected discoveries and s-inventions of design requirements: Important vehicles for a design process. Design Studies, 21(6), 539–567.

    Article  Google Scholar 

  • Visser, W. (1994). Organisation of design activities: Opportunistic, with hierarchical episodes. Interacting with Computers, 6, 235–238.

    Article  Google Scholar 

  • Weisberg, R., Pichot, N., Bonetto, E., Pavani, J.-B., Arciszewski, T., & Bonnardel, N. (2021). From explicit to implicit theories of creativity and back: The relevance of naive criteria in defining creativity. The Journal of Creative Behavior. https://doi.org/10.1002/jocb.492.

  • Zhao, T., Yang, J., Zhang, H., & Siu, K. W. M. (2019). Creative idea generation method based on deep learning technology. International Journal of Technology and Design Education, 1–20. https://doi.org/10.1007/s10798-019-09556-y.

Download references

Author information

Authors and Affiliations

  1. Aix-Marseille Univ., PSYCLE (UR 3273) & InCIAM, Aix-en-Provence, France

    Nathalie Bonnardel

  2. University of North Carolina, Charlotte, NC, USA

    John Gero

Authors
  1. Nathalie Bonnardel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Gero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nathalie Bonnardel .

Editor information

Editors and Affiliations

  1. Dublin City University, Dublin, Ireland

    Vlad Petre Glăveanu

Section Editor information

  1. Paris Descartes University, Paris, France

    Samira Bourgeois-Bougrine

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Bonnardel, N., Gero, J. (2022). Possible of Design. In: Glăveanu, V.P. (eds) The Palgrave Encyclopedia of the Possible. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-90913-0_22

Download citation

Publish with us

Policies and ethics

Search

Navigation