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Impact of network structure on collective learning: An experimental study in a data science competition

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  • Devon Brackbill
  • Damon Centola

Abstract

Do efficient communication networks accelerate solution discovery? The most prominent theory of organizational design for collective learning maintains that informationally efficient collaboration networks increase a group’s ability to find innovative solutions to complex problems. We test this idea against a competing theory that argues that communication networks that are less efficient for information transfer will increase the discovery of novel solutions to complex problems. We conducted a series of experimentally designed Data Science Competitions, in which we manipulated the efficiency of the communication networks among distributed groups of data scientists attempting to find better solutions for complex statistical modeling problems. We present findings from 16 independent competitions, where individuals conduct greedy search and only adopt better solutions. We show that groups with inefficient communication networks consistently discovered better solutions. In every experimental trial, groups with inefficient networks outperformed groups with efficient networks, as measured by both the group’s average solution quality and the best solution found by a group member.

Suggested Citation

  • Devon Brackbill & Damon Centola, 2020. "Impact of network structure on collective learning: An experimental study in a data science competition," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-13, September.
  • Handle: RePEc:plo:pone00:0237978
    DOI: 10.1371/journal.pone.0237978
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Naudé, Wim & Bray, Amy & Lee, Celina, 2021. "Crowdsourcing Artificial Intelligence in Africa: Findings from a Machine Learning Contest," IZA Discussion Papers 14545, Institute of Labor Economics (IZA).
    3. Manuel S. Mariani & Federico Battiston & Emőke-Ágnes Horvát & Giacomo Livan & Federico Musciotto & Dashun Wang, 2024. "Collective dynamics behind success," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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