Diversity, Network Structure, and the Effectiveness of Collective Design and Innovation
This project is currently supported by the NSF Science of Organizations (SoO) and Systems Science (SYS) Programs (Award #: NSF SES-1734147).
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
About the Project
As the complexity of products and services has skyrocketed over the last several decades, collective design and innovation processes have become a necessity for the development of successful solutions for real-world problems. Such large-scale design processes typically involve individuals with diverse knowledge, expertise and behaviors, and the organizational structures under which the collective design takes place are often complex and dynamic with temporally changing non-trivial network properties.
This project aims to investigate, both theoretically and experimentally, how the diversity of knowledge, expertise, and behaviors of individual members and the topological properties of organizational network structures will affect the effectiveness of design and innovation processes at collective levels. This will be accomplished through (a) theoretical agent-based network modeling and simulation, and (b) model evaluation through online laboratory experiments involving student participants with diverse majors. Modeling and simulation will investigate potential interactions between task-related diversity and large-scale organizational network structures. The predictions produced by the simulations will be evaluated through online laboratory experiments, in which participants will collaborate on several open-ended collective design tasks through a computer-mediated collaboration platform. In these experiments, the participants' task-related diversity will be measured and its distribution in the organizational network will be manipulated. The topologies of the network will also be monitored and manipulated dynamically.
The successful outcome of the project will be a new "guiding principle" for configuring and manipulating task-related diversity and organizational network structures to promote effective collective design and innovation. This will bear multidisciplinary impacts on organizational science, management science, systems science, systems engineering, operations research, and network science.
- Yiding Cao (PhD candidate in Industrial and Systems Engineering)
- Minjun Kim (PhD candidate in Systems Science)
- Yingjun Dong (PhD candidate in Systems Science)
- Neil MacLaren (PhD candidate in Management)
- Ankita Kulkarni (PhD candidate in Management)
- Shelley D. Dionne, Hiroki Sayama, and Francis J. Yammarino, Diversity and social network structure in collective decision making: Evolutionary perspectives with agent-based simulations, Complexity, in press.
- Hiroki Sayama, Shelley Dionne, Francis Yammarino, Yiding Cao, Minjun Kim, Neil MacLaren, and Ankita Kulkarni, Effects of organizational network structure and task-related diversity on collective design and innovation: An agent-based modeling study, accepted for presentation at the 2018 Conference on Complex Systems (CCS 2018), September 23-28, 2018, Thessaloniki, Greece.
- Hiroki Sayama, Evolutionary perspectives on collective design and innovation: Task diversity, behavioral diversity, and network structure, an invited talk at the Advanced Institute for Complex Systems, Waseda University, Tokyo, Japan, June 28, 2018.
- Neil Maclaren, Yiding Cao, Ankita Kulkarni, Francis Yammarino, Michael Mumford, Shelley Dionne, Hiroki Sayama, Shane Connelly, Tyler Mulhearn, Robert Martin, Erin Todd, and Frank Bosco, Agent-based model parameter estimation and variable reduction using metaBUS: An application to a collective leadership model, presented as a poster at NERCCS 2018: First Northeast Regional Conference on Complex Systems, April 12-13, 2018, Binghamton, NY.
- [NetSci High] Ewa Sulicz, Juwairiyah Shaikh, Carol Reynolds, and Hiroki Sayama, Does it really matter where you go to college? A network-based analysis of educational outcomes of universities in the United States, presented as a NetSci High poster at CompleNet 2018: Ninth International Conference on Complex Networks, March 6, 2018, Boston, MA.
- [NetSci High] Chris Vincens, Danyal Shah, Carol Reynolds, and Hiroki Sayama, How will the transfer of using alternatives to fossil fuels affect trade networks and economic interconnectedness?, presented as a NetSci High poster at CompleNet 2018: Ninth International Conference on Complex Networks, March 6, 2018, Boston, MA.
- [NetSci High] Harrison Barnes, Joyce Zhu, Carol Reynolds, and Hiroki Sayama, Music intervals connecting music of different culture, presented as a NetSci High poster at CompleNet 2018: Ninth International Conference on Complex Networks, March 6, 2018, Boston, MA.
- Hiroki Sayama, Mechanistic modeling of social systems, an invited talk at the Satellite Symposium on Understanding Our Complex World Using Data Analytics and Models at the Conference on Complex Systems 2017 (CCS 2017), September 20, 2017, Cancun, Mexico.
Please address any inquiries about this project to:
Hiroki Sayama, D.Sc.
Director, Center for Collective Dynamics of Complex Systems
Professor, Department of Systems Science and Industrial Engineering
Binghamton University, State University of New York
P.O. Box 6000, Binghamton, NY 13902-6000
Tel: (607) 777-3566
© Copyright 2017-2019 Center for Collective Dynamics of
Complex Systems, Binghamton University