Allocation Requires Prediction Only if Inequality Is Low

TL;DR

This paper investigates when algorithmic predictions improve resource allocation, finding they are most effective in low-inequality, high-budget settings, thus highlighting limits of prediction-based interventions.

Contribution

It introduces a mathematical framework to evaluate the effectiveness of prediction-based allocations in societal resource distribution settings.

Findings

Prediction outperforms baseline only with low between-unit inequality.

High intervention budgets enhance the efficacy of prediction-based allocations.

Effectiveness depends on prediction accuracy, heterogeneity, and learnability of unit statistics.

Abstract

Algorithmic predictions are emerging as a promising solution concept for efficiently allocating societal resources. Fueling their use is an underlying assumption that such systems are necessary to identify individuals for interventions. We propose a principled framework for assessing this assumption: Using a simple mathematical model, we evaluate the efficacy of prediction-based allocations in settings where individuals belong to larger units such as hospitals, neighborhoods, or schools. We find that prediction-based allocations outperform baseline methods using aggregate unit-level statistics only when between-unit inequality is low and the intervention budget is high. Our results hold for a wide range of settings for the price of prediction, treatment effect heterogeneity, and unit-level statistics' learnability. Combined, we highlight the potential limits to improving the efficacy of interventions through prediction.