Research Funding as a Decision Problem Under Heavy-Tailed Uncertainty

TL;DR

This paper models research funding as a decision problem under heavy-tailed uncertainty, proposing biased lottery mechanisms as scalable alternatives to peer review, supported by bibliometric data analysis.

Contribution

It introduces a decision-theoretic biased lottery framework balancing exploration and exploitation for research funding, validated through large-scale bibliometric data.

Findings

Funding allocations tend to favor top researchers under impact-based objectives.

Biased lottery mechanisms outperform traditional peer review in heavy-tailed environments.

A web app implementing the deterministic allocation method is provided.

Abstract

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.