Group Size and Group Performance in Small Collaborative Team Settings: An Agent-Based Simulation Model of Collaborative Decision-Making Dynamics

The relationship between size and performance of collaborative human small groups has been studied broadly across management, psychology, economics, sociology, and engineering disciplines. However, empirical research findings on this question remain equivocal. Many of the earlier studies centered on...

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Vydané v:	Complexity Ročník 2022; číslo 1
Hlavní autori:	Cao, Shun, MacLaren, Neil G., Cao, Yiding, Marshall, Jason, Dong, Yingjun, Yammarino, Francis J., Dionne, Shelley D., Mumford, Michael D., Connelly, Shane, Martin, Robert W., Standish, Colleen J., Newbold, Tanner R., England, Samantha, Sayama, Hiroki, Ruark, Gregory A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken Hindawi 2022 Wiley John Wiley & Sons, Inc
Predmet:	Agent-based models Analysis Collaboration Computer simulation Computer-generated environments Decision making Electronic computers. Computer science Experiments Group dynamics Human performance Information sharing Memory Problem solving Psychology QA75.5-76.95 Simulation models Social networks Sociology Task complexity Utility functions Variables New York United States
ISSN:	1076-2787, 1099-0526
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Abstract	The relationship between size and performance of collaborative human small groups has been studied broadly across management, psychology, economics, sociology, and engineering disciplines. However, empirical research findings on this question remain equivocal. Many of the earlier studies centered on empirical human-subject experiments, which inevitably involved many confounding factors. To obtain more theory-driven mechanistic explanations of the linkage between group size and performance, we developed an agent-based simulation model that describes the complex process of collaborative group decision-making on problem-solving tasks. To find better solutions to a problem with given complexity, these agents repeatedly explore and share solution candidates, evaluate and respond to the solutions proposed by others, and update their understanding of the problem by conducting individual local search and incorporating others’ proposals. Our results showed that under a condition of ineffective information sharing, group size was negatively related to group performance at the beginning of discussion across each level of problem complexity (i.e., low, medium, and high). However, in the long run, larger groups outperformed smaller groups for the problem with medium complexity and equally well for the problem with low complexity because larger groups developed higher solution diversity. For the problem with high complexity, the higher solution diversity led to more disagreements which in turn hindered larger groups’ collaborative problem-solving ability. Our results also suggested that, in small collaborative team settings, effective information sharing can significantly improve group performance for groups of any size, especially for larger groups. This model provides a unified, mechanistic explanation of the conflicting observations reported in the existing empirical literature.
AbstractList	The relationship between size and performance of collaborative human small groups has been studied broadly across management, psychology, economics, sociology, and engineering disciplines. However, empirical research findings on this question remain equivocal. Many of the earlier studies centered on empirical human-subject experiments, which inevitably involved many confounding factors. To obtain more theory-driven mechanistic explanations of the linkage between group size and performance, we developed an agent-based simulation model that describes the complex process of collaborative group decision-making on problem-solving tasks. To find better solutions to a problem with given complexity, these agents repeatedly explore and share solution candidates, evaluate and respond to the solutions proposed by others, and update their understanding of the problem by conducting individual local search and incorporating others’ proposals. Our results showed that under a condition of ineffective information sharing, group size was negatively related to group performance at the beginning of discussion across each level of problem complexity (i.e., low, medium, and high). However, in the long run, larger groups outperformed smaller groups for the problem with medium complexity and equally well for the problem with low complexity because larger groups developed higher solution diversity. For the problem with high complexity, the higher solution diversity led to more disagreements which in turn hindered larger groups’ collaborative problem-solving ability. Our results also suggested that, in small collaborative team settings, effective information sharing can significantly improve group performance for groups of any size, especially for larger groups. This model provides a unified, mechanistic explanation of the conflicting observations reported in the existing empirical literature.
Audience	Academic
Author	Martin, Robert W. Dionne, Shelley D. Connelly, Shane Dong, Yingjun Mumford, Michael D. Newbold, Tanner R. England, Samantha Marshall, Jason Sayama, Hiroki MacLaren, Neil G. Cao, Yiding Ruark, Gregory A. Yammarino, Francis J. Standish, Colleen J. Cao, Shun
Author_xml	– sequence: 1 givenname: Shun orcidid: 0000-0001-7014-0728 surname: Cao fullname: Cao, Shun organization: Department of Information and Logistics TechnologyUniversity of HoustonHoustonTX 77204USAuh.edu – sequence: 2 givenname: Neil G. surname: MacLaren fullname: MacLaren, Neil G. organization: Department of MathematicsUniversity at BuffaloState University of New YorkBuffaloNY 14260-2900USAbuffalo.edu – sequence: 3 givenname: Yiding orcidid: 0000-0002-2392-3700 surname: Cao fullname: Cao, Yiding organization: Center for Collective Dynamics of Complex SystemsBinghamton UniversityState University of New YorkBinghamtonNY 13902-6000USAbuffalo.edu – sequence: 4 givenname: Jason surname: Marshall fullname: Marshall, Jason organization: Heider College of BusinessCreighton UniversityOmahaNE 68178USAcreighton.edu – sequence: 5 givenname: Yingjun orcidid: 0000-0002-1935-1105 surname: Dong fullname: Dong, Yingjun organization: Center for Collective Dynamics of Complex SystemsBinghamton UniversityState University of New YorkBinghamtonNY 13902-6000USAbuffalo.edu – sequence: 6 givenname: Francis J. surname: Yammarino fullname: Yammarino, Francis J. organization: Center for Collective Dynamics of Complex SystemsBinghamton UniversityState University of New YorkBinghamtonNY 13902-6000USAbuffalo.edu – sequence: 7 givenname: Shelley D. surname: Dionne fullname: Dionne, Shelley D. organization: Center for Collective Dynamics of Complex SystemsBinghamton UniversityState University of New YorkBinghamtonNY 13902-6000USAbuffalo.edu – sequence: 8 givenname: Michael D. surname: Mumford fullname: Mumford, Michael D. organization: Department of PsychologyUniversity of OklahomaNormanOKUSAou.edu – sequence: 9 givenname: Shane surname: Connelly fullname: Connelly, Shane organization: Department of PsychologyUniversity of OklahomaNormanOKUSAou.edu – sequence: 10 givenname: Robert W. surname: Martin fullname: Martin, Robert W. organization: Department of PsychologyUniversity of OklahomaNormanOKUSAou.edu – sequence: 11 givenname: Colleen J. surname: Standish fullname: Standish, Colleen J. organization: Department of PsychologyUniversity of OklahomaNormanOKUSAou.edu – sequence: 12 givenname: Tanner R. surname: Newbold fullname: Newbold, Tanner R. organization: Department of PsychologyUniversity of OklahomaNormanOKUSAou.edu – sequence: 13 givenname: Samantha surname: England fullname: England, Samantha organization: Department of PsychologyUniversity of OklahomaNormanOKUSAou.edu – sequence: 14 givenname: Hiroki orcidid: 0000-0002-2670-5864 surname: Sayama fullname: Sayama, Hiroki organization: Department of MathematicsUniversity at BuffaloState University of New YorkBuffaloNY 14260-2900USAbuffalo.edu – sequence: 15 givenname: Gregory A. surname: Ruark fullname: Ruark, Gregory A. organization: U.S. Army Research Institute for the Behavioral and Social SciencesFort BelvoirVAUSA
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ContentType	Journal Article
Copyright	Copyright © 2022 Shun Cao et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Shun Cao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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SubjectTerms	Agent-based models Analysis Collaboration Computer simulation Computer-generated environments Decision making Electronic computers. Computer science Experiments Group dynamics Human performance Information sharing Memory Problem solving Psychology QA75.5-76.95 Simulation models Social networks Sociology Task complexity Utility functions Variables
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Title	Group Size and Group Performance in Small Collaborative Team Settings: An Agent-Based Simulation Model of Collaborative Decision-Making Dynamics
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