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Limited diffusion of scientific knowledge forecasts collapse

Nature Human Behaviour volume 9, pages 268–276 (2025)Cite this article

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Abstract

Market bubbles emerge when asset prices are driven unsustainably higher than asset values, and shifts in belief burst them. We demonstrate an analogous phenomenon in the case of biomedical knowledge, when promising research receives inflated attention. We introduce a diffusion index that quantifies whether research areas have been amplified within social and scientific bubbles, or have diffused and become evaluated more broadly. We illustrate the utility of our diffusion approach in tracking the trajectories of cardiac stem cell research (a bubble that collapsed) and cancer immunotherapy (which showed sustained growth). We then trace the diffusion of 28,504 subfields in biomedicine comprising nearly 1.9 M papers and more than 80 M citations to demonstrate that limited diffusion of biomedical knowledge anticipates abrupt decreases in popularity. Our analysis emphasizes that restricted diffusion, implying a socio-epistemic bubble, leads to dramatic collapses in relevance and attention accorded to scientific knowledge.

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Fig. 1: Representation of different diffusion levels and contrasting diffusion trajectories.
Fig. 2: Survival probability against bubble bursting as a function of knowledge diffusion in social space.
Fig. 3: Comparison of author productivity in collapsed subfields 5 and 10 years post collapse.
Fig. 4: The average number of new grants acknowledged in collapsed subfields by years relative to burst.

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Data availability

This work uses the PubMed Knowledge Graph26 (http://er.tacc.utexas.edu/datasets/ped) and the replication data from ref. 31 (https://www.openicpsr.org/openicpsr/project/116188/version/V1/view;jsessionid=EA1E1E5A6DAB42737EE54A5F5DD4B069). Source data are provided with this paper.

Code availability

The data and code used for the figures and tables are available in GitHub55.

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Acknowledgements

We acknowledge funding from the Fetzer Franklin Fund in association with the 2019 MetaScience Symposium (R.S.D., J.A.E., D.K.), the Air Force Office of Scientific Research (AFOSR: FA9550-19-1-0354 and FA9550-15-1-0162) (J.A.E., D.K.) and the National Science Foundation (NSF: 1829366 and 1800956) (J.A.E., D.K.). The funders have/had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This work was completed in part with resources provided by the University of Chicago’s Research Computing Center. We also appreciate the support from J. Xu and Y. Ding in facilitating access to PubMed Knowledge Graph.

Author information

Authors and Affiliations

  1. Department of Sociology, University of Chicago, Chicago, IL, USA

    Donghyun Kang & James A. Evans

  2. Knowledge Lab, University of Chicago, Chicago, IL, USA

    Donghyun Kang & James A. Evans

  3. Division of Cardiology, Department of Medicine, University of Illinois College of Medicine, Chicago, IL, USA

    Robert S. Danziger & Jalees Rehman

  4. Department of Pharmacology, University of Illinois at Chicago, Chicago, IL, USA

    Robert S. Danziger

  5. Department of Physiology and Biophysics, University of Illinois at Chicago, Chicago, IL, USA

    Robert S. Danziger

  6. Department of Biochemistry and Molecular Genetics, University of Illinois, College of Medicine, Chicago, IL, USA

    Jalees Rehman

  7. University of Illinois Cancer Center, Chicago, IL, USA

    Jalees Rehman

  8. Santa Fe Institute, Santa Fe, NM, USA

    James A. Evans

  9. Paradigms of Intelligence Team, Google, Mountain View, CA, USA

    James A. Evans

Authors
  1. Donghyun Kang
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Contributions

D.K., R.S.D., J.R. and J.A.E. conceptualized the project. D.K. and J.A.E. developed the methodology. D.K. performed visualization. R.S.D. and J.A.E. acquired funding. D.K. and J.A.E. wrote the original draft. D.K., R.S.D., J.R. and J.A.E. reviewed and edited the manuscript.

Corresponding author

Correspondence to James A. Evans.

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Nature Human Behaviour thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Complementary 2D heatmaps for the upper panels of Fig. 1.

Values are derived from the kernel density estimations graphed in Fig. 1 for the distribution of diffusion indices in scientific (panels a and b) and social space (panels c and d), respectively.

Source data

Extended Data Fig. 2 Distribution of zi,t from 28,504 subfields.

We set the cutoff value for bubble bursting to −2.64, the bottom 0.5% percentile. The range of \({z}_{i,t}\) is [−5.2, 5.52].

Source data

Extended Data Fig. 3 Six examples of subfields.

Annual citation counts aggregated at the subfield level, using forward citations to related publications. Top panels (a, b, c): Subfields represented by three PMIDs, illustrating cases without bubble bursting events. Bottom panels (d, e, f): Subfields that experienced bubble bursts, corresponding to cutoffs closest to the 0.5%, 0.25%, and 0.1% thresholds of the \({z}_{i,t}\) value.

Source data

Extended Data Table 1 Pairwise t-test comparing average productivity differences between near-collapse active scientists (≤2 years before collapse) and early entrants
Full size table
Extended Data Table 2 Proportion of subfields with newly acknowledged grants after collapse, and the Mean, 1st Quantile, Median, and 3rd Quartile of the number of new grants post-collapse
Full size table

Supplementary information

Supplementary Information

Supplementary Tables 1.1–1.3, 3.1–3.11 and 4.1–4.4; Figs. 2.1, 2.2, 3.1–3.3 and 4.1–4.4; and Discussion.

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Peer Review File

Source data

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Kang, D., Danziger, R.S., Rehman, J. et al. Limited diffusion of scientific knowledge forecasts collapse. Nat Hum Behav 9, 268–276 (2025). https://doi.org/10.1038/s41562-024-02041-0

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