Abstract

As political polarization in the United States continues to rise1,2,3, the question of whether polarized individuals can fruitfully cooperate becomes pressing. Although diverse perspectives typically lead to superior team performance on complex tasks4,5, strong political perspectives have been associated with conflict, misinformation and a reluctance to engage with people and ideas beyond one’s echo chamber6,7,8. Here, we explore the effect of ideological composition on team performance by analysing millions of edits to Wikipedia’s political, social issues and science articles. We measure editors’ online ideological preferences by how much they contribute to conservative versus liberal articles. Editor surveys suggest that online contributions associate with offline political party affiliation and ideological self-identity. Our analysis reveals that polarized teams consisting of a balanced set of ideologically diverse editors produce articles of a higher quality than homogeneous teams. The effect is most clearly seen in Wikipedia’s political articles, but also in social issues and even science articles. Analysis of article ‘talk pages’ reveals that ideologically polarized teams engage in longer, more constructive, competitive and substantively focused but linguistically diverse debates than teams of ideological moderates. More intense use of Wikipedia policies by ideologically diverse teams suggests institutional design principles to help unleash the power of polarization.

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Code availability

Code used to gather, process and analyse the data is available at https://github.com/KnowledgeLab/wisdom-of-polarized-crowds.

Data availability

Data used in the study are available at https://github.com/KnowledgeLab/wisdom-of-polarized-crowds.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We acknowledge funding from the National Science Foundation SBE-1829366, John Templeton Foundation to the Metaknowledge Network and Air Force Office of Scientific Research FA9550-15-1-0162, and computation support from Cloud Kotta. We also acknowledge support from the Data@Carolina initiative and thank H. Guo for helping with some of the computations. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Feng Shi, Misha Teplitskiy.

Affiliations

  1. Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • Feng Shi
  2. Knowledge Lab, University of Chicago, Chicago, IL, USA

    • Feng Shi
    • , Misha Teplitskiy
    • , Eamon Duede
    •  & James A. Evans
  3. Laboratory for Innovation Science, Harvard University, Boston, MA, USA

    • Misha Teplitskiy
  4. Committee on the Conceptual and Historical Studies of Science, University of Chicago, Chicago, IL, USA

    • Eamon Duede
  5. Department of Sociology, University of Chicago, Chicago, IL, USA

    • James A. Evans
  6. Santa Fe Institute, Santa Fe, NM, USA

    • James A. Evans

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Contributions

All authors designed the research, interpreted the results and drafted the paper. F.S., M.T. and E.D. gathered the data. F.S. designed the code and F.S. and M.T. analysed the data.

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The authors declare no competing interests.

Corresponding authors

Correspondence to Misha Teplitskiy or James A. Evans.

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    Supplementary Figures 1–3, Supplementary Tables 1–8, Supplementary Methods 1–7, Supplementary Discussions 1 and 2, and Supplementary References.

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DOI

https://doi.org/10.1038/s41562-019-0541-6