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

Author:

Cao Shun1ORCID,MacLaren Neil G.2,Cao Yiding345ORCID,Marshall Jason6,Dong Yingjun345ORCID,Yammarino Francis J.35,Dionne Shelley D.35,Mumford Michael D.7,Connelly Shane7,Martin Robert W.7,Standish Colleen J.7,Newbold Tanner R.7,England Samantha7,Sayama Hiroki2358ORCID,Ruark Gregory A.9

Affiliation:

1. Department of Information and Logistics Technology, University of Houston, Houston, TX 77204, USA

2. Department of Mathematics, University at Buffalo, State University of New York, Buffalo, NY 14260-2900, USA

3. Center for Collective Dynamics of Complex Systems, Binghamton University, State University of New York, Binghamton, NY 13902-6000, USA

4. Department of Systems Science and Industrial Engineering, Binghamton University, State University of New York, Binghamton, NY 13902-6000, USA

5. Bernard M. & Ruth R. Bass Center for Leadership Studies, School of Management, Binghamton University, State University of New York, Binghamton, NY, USA

6. Heider College of Business, Creighton University, Omaha, NE 68178, USA

7. Department of Psychology, University of Oklahoma, Norman, OK, USA

8. Waseda Innovation Lab, Waseda University, Shinjuku Tokyo 169-8050, Japan

9. U.S. Army Research Institute for the Behavioral and Social Sciences, Fort Belvoir, VA, USA

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.

Funder

U.S. Army Research Institute

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

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