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Social learning strategies modify the effect of network structure on group performance

Author

Listed:
  • Daniel Barkoczi

    (Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development)

  • Mirta Galesic

    (Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development
    Santa Fe Institute)

Abstract

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.

Suggested Citation

  • Daniel Barkoczi & Mirta Galesic, 2016. "Social learning strategies modify the effect of network structure on group performance," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13109
    DOI: 10.1038/ncomms13109
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    Citations

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    Cited by:

    1. Darío Blanco-Fernández & Stephan Leitner & Alexandra Rausch, 2023. "Interactions between the individual and the group level in organizations: The case of learning and group turnover," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(4), pages 1087-1128, December.
    2. Khraisha, Tamer, 2020. "Complex economic problems and fitness landscapes: Assessment and methodological perspectives," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 390-407.
    3. Tang, Ming & Liao, Huchang, 2024. "Group efficiency and individual fairness tradeoff in making wise decisions," Omega, Elsevier, vol. 124(C).
    4. Massari, Giovanni F. & Giannoccaro, Ilaria & Carbone, Giuseppe, 2019. "Are distrust relationships beneficial for group performance? The influence of the scope of distrust on the emergence of collective intelligence," International Journal of Production Economics, Elsevier, vol. 208(C), pages 343-355.
    5. Abigail N. Devereaux & Richard E. Wagner, 2020. "Contrasting Visions for Macroeconomic Theory: DSGE and OEE," The American Economist, Sage Publications, vol. 65(1), pages 28-50, March.
    6. Niccolo Pescetelli, 2021. "A Brief Taxonomy of Hybrid Intelligence," Forecasting, MDPI, vol. 3(3), pages 1-11, September.
    7. Theiss Bendixen, 2020. "How cultural evolution can inform the science of science communication—and vice versa," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-10, December.
    8. Kim, Dennie & Funk, Russell & Zaheer, Aks, 2020. "Structure in Context: A Morphological View of Whole Network Performance," SocArXiv x6q7g, Center for Open Science.
    9. Atsushi Ueshima & Matthew I. Jones & Nicholas A. Christakis, 2024. "Simple autonomous agents can enhance creative semantic discovery by human groups," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    10. Gengjun Yao & Jingwei Wang & Baoguo Cui & Yunlong Ma, 2022. "Quantifying effects of tasks on group performance in social learning," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    11. Lei Xu & Ronggui Ding & Lei Wang, 2022. "How to facilitate knowledge diffusion in collaborative innovation projects by adjusting network density and project roles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1353-1379, March.

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