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Structural Diversity Drives Disruptive Scientific Innovation

Daniel Dajun Zeng, Hao Peng, Kang Zhao, Ning Zhang, Peijie Zhang, Qingpeng Zhang, Saike He, Yichun Peng, Yi Yang

Structural diversity in teams predicts and drives disruptive scientific innovation.

arxiv:2605.12514 v1 · 2026-04-02 · cs.SI · cs.CV · cs.CY · cs.DL · stat.AP

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Claims

C1strongest claim

SD is a powerful and robust predictor of disruptive innovation, outperforming traditional team novelty indicators such as team freshness and edge density. Moreover, SD positively interacts with team size and is able to mitigate the well-known 'curse of scale' by transforming scale from a liability into a resource for creative synthesis.

C2weakest assumption

The quasi-natural experiment based on the 2012 NSF policy change validly isolates the causal effect of structural diversity without residual confounding from unobserved team or field characteristics, and that the operational definition of 'distinct knowledge communities' from the collaboration network is stable and not sensitive to clustering choices.

C3one line summary

Structural diversity in a team's prior collaboration network predicts disruptive scientific innovation more strongly than team freshness or edge density and turns large team size from a liability into an advantage via better disciplinary integration.

References

30 extracted · 30 resolved · 1 Pith anchors

[1] Are ideas getting harder to find?American Economic Review, 110(4):1104–1144, 2020 2020
[2] Team assembly mechanisms determine collaboration network structure and team performance.Science, 308(5722):697–702, 2005 2005
[3] The increasing dominance of teams in production of knowledge.Science, 316(5827):1036–1039, 2007 2007
[4] Large teams develop and small teams disrupt science and technology.Nature, 566(7744):378–382, 2019 2019
[5] death of the renaissance man 2009

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Receipt and verification
First computed 2026-05-18T03:10:02.936150Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

5aac20098459b417354dae90d1bf2049c0f5c622c2cf1aa00901375090ce4021

Aliases

arxiv: 2605.12514 · arxiv_version: 2605.12514v1 · doi: 10.48550/arxiv.2605.12514 · pith_short_12: LKWCACMELG2B · pith_short_16: LKWCACMELG2BONKN · pith_short_8: LKWCACME
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/LKWCACMELG2BONKNV2INDPZAJH \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5aac20098459b417354dae90d1bf2049c0f5c622c2cf1aa00901375090ce4021
Canonical record JSON
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    "submitted_at": "2026-04-02T02:15:38Z",
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