# The Hidden Cost of Waiting for Accurate Predictions

arXiv: https://arxiv.org/abs/2503.00650 · HTML: https://mlsanity.com/paper/2503.00650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 56 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [Linear Contextual Bandits with Knapsacks](https://mlsanity.com/paper/1507.06738/) (2015)
- [Rotting Bandits](https://mlsanity.com/paper/1702.07274/) (2017)
- [Decision Trees for Decision-Making under the Predict-then-Optimize Framework](https://mlsanity.com/paper/2003.00360/) (2020)
- [Difficult Lessons on Social Prediction from Wisconsin Public Schools](https://mlsanity.com/paper/2304.06205/) (2023)
- [Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression](https://mlsanity.com/paper/2211.07484/) (2022)
- [Fairness in Ranking under Uncertainty](https://mlsanity.com/paper/2107.06720/) (2021)
- [Algorithms and Decision-Making in the Public Sector](https://mlsanity.com/paper/2106.03673/) (2021)
- [Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment](https://mlsanity.com/paper/1712.08238/) (2017)
- [The Relative Value of Prediction in Algorithmic Decision Making](https://mlsanity.com/paper/2312.08511/) (2023)
- [Allocating Opportunities in a Dynamic Model of Intergenerational Mobility](https://mlsanity.com/paper/2101.08451/) (2021)
- [From the Fair Distribution of Predictions to the Fair Distribution of Social Goods: Evaluating the Impact of Fair Machine Learning on Long-Term Unemployment](https://mlsanity.com/paper/2401.14438/) (2024)
- [Allocation Requires Prediction Only if Inequality Is Low](https://mlsanity.com/paper/2406.13882/) (2024)
- [Learning treatment effects while treating those in need](https://mlsanity.com/paper/2407.07596/) (2024)
- [Is your model predicting the past?](https://mlsanity.com/paper/2206.11673/) (2022)
- [Evaluating the Effectiveness of Index-Based Treatment Allocation](https://mlsanity.com/paper/2402.11771/) (2024)
- [Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation](https://mlsanity.com/paper/2302.02570/) (2023)
- [Sequential Nature of Recommender Systems Disrupts the Evaluation Process](https://mlsanity.com/paper/2205.13681/) (2022)
- [Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action](https://mlsanity.com/paper/2401.16440/) (2024)
- [Wealth Dynamics Over Generations: Analysis and Interventions](https://mlsanity.com/paper/2209.07375/) (2022)
- [Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care](https://mlsanity.com/paper/2306.08044/) (2023)

## Built on by (most cited first, 1 of 1)

- [Good Allocations from Bad Estimates](https://mlsanity.com/paper/2601.05597/) (2026)

## Abstract (arXiv)

Algorithmic predictions are increasingly informing societal resource allocations by identifying individuals for targeting. Policymakers often build these systems with the assumption that by gathering more observations on individuals, they can improve predictive accuracy and, consequently, allocation efficiency. An overlooked yet consequential aspect of prediction-driven allocations is that of timing. The planner has to trade off relying on earlier and potentially noisier predictions to intervene before individuals experience undesirable outcomes, or they may wait to gather more observations to make more precise allocations. We examine this tension using a simple mathematical model, where the planner collects observations on individuals to improve predictions over time. We analyze both the ranking induced by these predictions and optimal resource allocation. We show that though individual prediction accuracy improves over time, counter-intuitively, the average ranking loss can worsen. As a result, the planner's ability to improve social welfare can decline. We identify inequality as a driving factor behind this phenomenon. Our findings provide a nuanced perspective and challenge the conventional wisdom that it is preferable to wait for more accurate predictions to ensure the most efficient allocations.