Conference proceeding
LEARNING BY MIGRATING: A COMPUTATIONAL STUDY OF DIVERSITY AND TEAM-LEVEL DECISION-MAKING
DS87-8 PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN (ICED 17), VOL 8: HUMAN BEHAVIOUR IN DESIGN, pp 589-598
01 Jan 2017
Abstract
How does previous experience and learning influence a team's ability to successfully agree on a system architecture, team roles and responsibilities, and design method? Migration of team members leads to diversity in past experiences and beliefs, which might have a positive or negative affect on team decision-making. Using computational modeling of self-managed teams across multiple project life cycles, we perform controlled experiments to evaluate performance and decision-making patterns of migrating vs. non-migrating teams. We find that there is no difference in mean performance, indicating that neither approach is intrinsically better. However, statistical tests of paired trials shows a meaningful an advantage for migrating (diverse) teams. Examining patterns of decision-making over time reveal that migrating (diverse) teams explore a wider range of team-level decisions, which makes them more adaptable in specific circumstances.
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Details
- Title
- LEARNING BY MIGRATING: A COMPUTATIONAL STUDY OF DIVERSITY AND TEAM-LEVEL DECISION-MAKING
- Creators
- Russell Thomas - George Mason UniversityJohn Gero - George Mason University
- Contributors
- A Maier (Editor)S Skec (Editor)H Kim (Editor)M Kokkolaras (Editor)J Oehmen (Editor)G Fadel (Editor)F Salustri (Editor)M VanDerLoos (Editor)
- Publication Details
- DS87-8 PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN (ICED 17), VOL 8: HUMAN BEHAVIOUR IN DESIGN, pp 589-598
- Series
- International Conference on Engineering Design
- Publisher
- Design Soc
- Number of pages
- 10
- Grant note
- CMMI-1400466 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000455207100060
- Other Identifier
- 991022202928804721