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Funding agencies rely on peer review and expert panels to select the researchdeserving funding. Peer review has limitations, including bias against riskyproposals or interdisciplinary research. The inter-rater reliability betweenreviewers and panels is low, particularly for proposals near the funding line.Funding agencies are also increasingly acknowledging the role of chance. TheSwiss National Science Foundation (SNSF) introduced a lottery for proposals inthe middle group of good but not excellent proposals. In this article, weintroduce a Bayesian hierarchical model for the evaluation process. To rank theproposals, we estimate their expected ranks (ER), which incorporates both themagnitude and uncertainty of the estimated differences between proposals. Aprovisional funding line is defined based on ER and budget. The ER and itscredible interval are used to identify proposals with similar quality andcredible intervals that overlap with the funding line. These proposals areentered into a lottery. We illustrate the approach for two SNSF grant schemesin career and project funding. We argue that the method could reduce bias inthe evaluation process. R code, data and other materials for this article areavailable online.

Rethinking the Funding Line at the Swiss National Science Foundation: Bayesian Ranking and Lottery