A Principled Approach to Randomized Selection under Uncertainty: Applications to Peer Review and Grant Funding

TL;DR

This paper introduces MERIT, a principled, optimization-based randomized decision-making framework that improves selection robustness under uncertainty, with applications to peer review and grant funding.

Contribution

The paper proposes MERIT, a novel method for randomized selection based on interval estimates, providing optimal worst-case performance and a polynomial-time algorithm for large-scale problems.

Findings

MERIT scales to over 10,000 items efficiently.

It outperforms existing methods in worst-case scenarios.

It matches existing algorithms in expected utility under probabilistic models.

Abstract

Many decision-making processes involve evaluating and then selecting items; examples include scientific peer review, job hiring, school admissions, and investment decisions. The eventual selection is performed by applying rules or deliberations to the raw evaluations, and then deterministically selecting the items deemed to be the best. These domains feature error-prone evaluations and uncertainty about future outcomes, which undermine the reliability of such deterministic selection rules. As a result, selection mechanisms involving explicit randomization that incorporate the uncertainty are gaining traction in practice. However, current randomization approaches are ad hoc, and as we prove, inappropriate for their purported objectives. In this paper, we propose a principled framework for randomized decision-making based on interval estimates of the quality of each item. We introduce MERIT (Maximin Efficient Randomized Interval Top-k), an optimization-based method that maximizes the worst-case expected number of top candidates selected, under uncertainty represented by overlapping intervals (e.g., confidence intervals or min-max intervals). MERIT provides an optimal resource allocation scheme under an interpretable notion of robustness. We develop a polynomial-time algorithm to solve the optimization problem and demonstrate empirically that the method scales to over 10,000 items. We prove that MERIT satisfies desirable axiomatic properties not guaranteed by existing approaches. Finally, we empirically compare algorithms on synthetic peer review data. Our experiments demonstrate that MERIT matches the performance of existing algorithms in expected utility under fully probabilistic review data models used in previous work, while outperforming previous methods with respect to our novel worst-case formulation.