Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-19T12:18:54.271Z Has data issue: false hasContentIssue false

COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK

Published online by Cambridge University Press:  27 July 2021

Marija Majda Perisic*
Affiliation:
University of Zagreb, FSB;
Mario Štorga
Affiliation:
University of Zagreb, FSB; Luleå University of Technology;
John S. Gero
Affiliation:
UNC Charlotte
*
Perisic, Marija Majda, University of Zagreb, FSB, Department of Design, Croatia, [email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

When observing a design space expansion during teamwork, several studies found that cumulative solution-related issues' occurrence follows a linear trend. Such findings contradict the hypothesis of solution-related issues being characteristic for the later design stages. This work relies on agent-based simulations to explore the emerging patterns in design solution space expansion during teamwork. The results demonstrate trends that accord with the empirical findings, suggesting that a cognitive effort in solution space expansion remains constant throughout a design session. The collected data on agents' cognitive processes and solution space properties enabled additional insights, which led to the detection of four distinct regimes of design solution space expansion.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Alzayed, Alsager, McComb, M., Hunter, C., Miller, S.T., S.R. (2019), “Expanding the Solution Space in Engineering Design Education: A Simulation-Based Investigation of Product Dissection”, Journal of Mechanical Design, Vol. 141 No. 3. doi.org/10.1115/1.4042426.Google Scholar
Asimov, M. (1962), Introduction to Design, Prentice-Hall, Englewood Cliffs (New Jersey).Google Scholar
Daly, S.R., Christian, J.L., Yilmaz, S., Seifert, C.M. and Gonzalez, R. (2012), “Assessing Design Heuristics for Idea Generation in an Introductory Engineering Course”, International Journal of Engineering Education, Vol. 28 No. 2, pp. 463473.Google Scholar
Dorst, K. (2015), Frame Innovation: Create New Thinking by Design, The MIT Press. doi.org/10.7551/mitpress/10096.001.0001.CrossRefGoogle Scholar
Dorst, K. and Cross, N. (2001), “Creativity in the design process: co-evolution of problem–solution”, Design Studies, Vol. 22 No. 5, pp. 425437.CrossRefGoogle Scholar
Gero, J.S. (1990), “Design Prototypes: A Knowledge Representation Schema for Design”, AI Magazine, Vol. 11 No. 4, pp. 2636.Google Scholar
Gero, J.S. and Kan, J.W.T. (2016), “Empirical results from measuring design creativity: Use of an augmented coding scheme in protocol analysis”, Proceedings of ICDC 2016, Atlanta, Georgia, USA.Google Scholar
Gero, J.S. and Kannengiesser, U. (2004), “The situated function–behaviour–structure framework”, Design Studies, Vol. 25 No. 4, pp. 373391.CrossRefGoogle Scholar
Gero, J.S., Kannengiesser, U. and Pourmohamadi, M. (2014), “Commonalities Across Designing: Empirical Results”, Design Computing and Cognition ’12, Springer, pp. 265281.Google Scholar
Gero, J.S., Kannengiesser, U. and Williams, C.B. (2014), “Does designing have a common cognitive behavior independent of domain and task: A meta-analysis of design protocols”, Proceedings of HBiD 2014, Ascona, Switzerland, p. 123.Google Scholar
Gero, J.S. and Kazakov, V. (1998), “Adapting evolutionary computing for exploration in creative designing”, Proceedings of the 4th International Round-Table Conference on Computational Models of Creative Design, Key Centre of Design Computing and Cognition, University of Sydney, Heron Island.Google Scholar
Gero, J.S. and Kumar, B. (1993), “Expanding design spaces through new design variables”, Design Studies, Vol. 14 No. 2, pp. 210221.CrossRefGoogle Scholar
Han, J., Shi, F., Chen, L. and Childs, P.R.N. (2018), “A computational tool for creative idea generation based on analogical reasoning and ontology”, AIEDAM, Vol. 32 No. 4, pp. 462477.CrossRefGoogle Scholar
Kahneman, D. (2011), Thinking, Fast and Slow, Farrar, Straus and Giroux.Google Scholar
Kan, J.W. and Gero, J.S. (2017), Quantitative Methods for Studying Design Protocols, Springer. doi.org/10.1007/978-94-024-0984-0.CrossRefGoogle Scholar
Kan, J.W.T. and Gero, J.S. (2018), “Characterizing innovative processes in design spaces through measuring the information entropy of empirical data from protocol studies”, AIEDAM, Vol. 32 No. 1, pp. 3243.CrossRefGoogle Scholar
Kannengiesser, U. and Gero, J.S. (2012), “A Process Framework of Affordances in Design”, Design Issues, Vol. 28 No. 1, pp. 5062.CrossRefGoogle Scholar
Kannengiesser, U., Gero, J.S., Wells, J. and Lammi, M. (2015), “Do high school students benefit from pre-engineering education?”, Proceedings of ICED 15, Vol. 11, The Design Society, Milan, Italy, pp. 267276.Google Scholar
Maher, M.L. (2000), “A Model of Co-evolutionary Design”, Engineering with Computers, Vol. 16, pp. 195208.CrossRefGoogle Scholar
Maher, M.L. and Tang, H.-H. (2003), “Co-evolution as a computational and cognitive model of design”, Research in Engineering Design, Vol. 14 No. 1, pp. 4764.CrossRefGoogle Scholar
Maher, M.L., Poon, J. and Boulanger, S. (1996), “Formalising Design Exploration as Co-Evolution”, Advances in Formal Design Methods for CAD, Springer US, Boston, MA, pp. 330.CrossRefGoogle Scholar
Martinec, T., Škec, S., Perišić, M.M. and Štorga, M. (2020), “Revisiting Problem-Solution Co-Evolution in the Context of Team Conceptual Design Activity”, Applied Sciences, Vol. 10 No. 18, p. 6303.CrossRefGoogle Scholar
Miyake, A. and Shah, P. (1999), Models of Working Memory, Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Pahl, G. and Beitz, W. (2007), Engineering Design: A Systematic Approach, Springer. doi.org/10.1007/978-1-84628-319-2.CrossRefGoogle Scholar
Perišić, M.M. (2020), Multi-Agent System for Simulation of Team Behaviour in Product Development, University of Zagreb.Google Scholar
Perišić, M.M., Martinec, T., Štorga, M. and Gero, J.S. (2019a), “A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams”, Proceedings of ICED, Vol. 1, pp. 1120.CrossRefGoogle Scholar
Perišić, M.M., Štorga, M. and Gero, J.S. (2019b), “Exploring the Effect of Experience on Team Behavior: A Computational Approach”, Design Computing and Cognition ’18, Springer, pp. 595612.CrossRefGoogle Scholar
Shah, J.J., Kulkarni, S. V. and Vargas-Hernandez, N. (2000), “Evaluation of Idea Generation Methods for Conceptual Design: Effectiveness Metrics and Design of Experiments”, Journal of Mechanical Design, Vol. 122 No. 4, pp. 377384.CrossRefGoogle Scholar
Yu, R., Gu, N., Ostwald, M. and Gero, J.S. (2015), “Empirical support for problem–solution coevolution in a parametric design environment”, AIEDAM, Vol. 29 No. 1, pp. 3344.CrossRefGoogle Scholar