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In search of diverse and connected teams: A computational approach to assemble diverse teams based on members’ social networks

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  • Diego Gómez-Zará
  • Archan Das
  • Bradley Pawlow
  • Noshir Contractor

Abstract

Previous research shows that teams with diverse backgrounds and skills can outperform homogeneous teams. However, people often prefer to work with others who are similar and familiar to them and fail to assemble teams with high diversity levels. We study the team formation problem by considering a pool of individuals with different skills and characteristics, and a social network that captures the familiarity among these individuals. The goal is to assign all individuals to diverse teams based on their social connections, thereby allowing them to preserve a level of familiarity. We formulate this team formation problem as a multi-objective optimization problem to split members into well-connected and diverse teams within a social network. We implement this problem employing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which finds team combinations with high familiarity and diversity levels in O(n2) time. We tested this algorithm on three empirically collected team formation datasets and against three benchmark algorithms. The experimental results confirm that the proposed algorithm successfully formed teams that have both diversity in member attributes and previous connections between members. We discuss the benefits of using computational approaches to augment team formation and composition.

Suggested Citation

  • Diego Gómez-Zará & Archan Das & Bradley Pawlow & Noshir Contractor, 2022. "In search of diverse and connected teams: A computational approach to assemble diverse teams based on members’ social networks," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-29, November.
    • Handle: RePEc:plo:pone00:0276061
    DOI: 10.1371/journal.pone.0276061
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    References listed on IDEAS

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    2. Satyam Mukherjee & Yun Huang & Julia Neidhardt & Brian Uzzi & Noshir Contractor, 2019. "Prior shared success predicts victory in team competitions," Nature Human Behaviour, Nature, vol. 3(1), pages 74-81, January.
    3. Satyam Mukherjee & Yun Huang & Julia Neidhardt & Brian Uzzi & Noshir Contractor, 2019. "Author Correction: Prior shared success predicts victory in team competitions," Nature Human Behaviour, Nature, vol. 3(4), pages 406-406, April.
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