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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.22793 |
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| _version_ | 1866917434098712576 |
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| author | Sorzano, Carlos Oscar S. Pueche-Granados, B. |
| author_facet | Sorzano, Carlos Oscar S. Pueche-Granados, B. |
| contents | Heavy-tailed impact distributions, intrinsic uncertainty, and the high costs of proposal-based peer review increasingly challenge research funding decisions. Using large-scale bibliometric data, we show that past scientific performance provides statistically meaningful, though imperfect, information about future productivity and impact across multiple dimensions. An aggregated, percentile-normalised proxy signal captures this predictive structure robustly across research domains.
We analyse deterministic and stochastic funding allocation mechanisms under impact-based objectives and find that both converge to highly concentrated allocations that favour a small number of top-performing researchers. To address the limitations of pure exploitation, we introduce a biased lottery framework based on a regularised decision-theoretic objective that explicitly balances exploration and exploitation while accounting for practical funding constraints. Our results suggest that biased lottery mechanisms offer a transparent, efficient, and scalable alternative to conventional peer review in environments characterised by heavy-tailed scientific returns. Additionally, we provide a web application, available at http://scilottery.biocomputingunit.es, that implements the deterministic allocation method presented in this work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22793 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Research Funding as a Decision Problem Under Heavy-Tailed Uncertainty Sorzano, Carlos Oscar S. Pueche-Granados, B. Applications Heavy-tailed impact distributions, intrinsic uncertainty, and the high costs of proposal-based peer review increasingly challenge research funding decisions. Using large-scale bibliometric data, we show that past scientific performance provides statistically meaningful, though imperfect, information about future productivity and impact across multiple dimensions. An aggregated, percentile-normalised proxy signal captures this predictive structure robustly across research domains. We analyse deterministic and stochastic funding allocation mechanisms under impact-based objectives and find that both converge to highly concentrated allocations that favour a small number of top-performing researchers. To address the limitations of pure exploitation, we introduce a biased lottery framework based on a regularised decision-theoretic objective that explicitly balances exploration and exploitation while accounting for practical funding constraints. Our results suggest that biased lottery mechanisms offer a transparent, efficient, and scalable alternative to conventional peer review in environments characterised by heavy-tailed scientific returns. Additionally, we provide a web application, available at http://scilottery.biocomputingunit.es, that implements the deterministic allocation method presented in this work. |
| title | Research Funding as a Decision Problem Under Heavy-Tailed Uncertainty |
| topic | Applications |
| url | https://arxiv.org/abs/2604.22793 |