The Limits of AI-Driven Allocation: Optimal Screening under Aleatoric Uncertainty

Bryan Wilder; Carlos Patino; Mateo Dulce Rubio; Santiago Cortes-Gomez

arxiv: 2605.07979 · v1 · submitted 2026-05-08 · 💻 cs.AI

The Limits of AI-Driven Allocation: Optimal Screening under Aleatoric Uncertainty

Santiago Cortes-Gomez , Mateo Dulce Rubio , Carlos Patino , Bryan Wilder This is my paper

Pith reviewed 2026-05-11 03:15 UTC · model grok-4.3

classification 💻 cs.AI

keywords algorithmic allocationaleatoric uncertaintyoptimal screeningresource allocationtwo-stage policysocial protectionhumanitarian demining

0 comments

The pith

The optimal strategy screens units at the margin of algorithmic allocation while directly targeting the highest-risk units.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies how to combine machine learning predictions with direct screening for allocating limited resources when individual outcomes remain uncertain even with perfect risk models. It establishes that the best policy is to screen only those units where the algorithmic prediction sits near the allocation cutoff and to assign the resource immediately to units with the highest predicted risk. This combination reduces overall misallocation compared to using either method alone. Gains from screening become larger when the population exhibits higher levels of aleatoric uncertainty that no algorithm can resolve.

Core claim

In the two-stage allocation model, the optimal policy screens units at the margin of algorithmic allocation and directly targets the highest-risk units. Screening and algorithmic targeting act as complements, with efficiency gains from screening increasing as aleatoric uncertainty rises in the population.

What carries the argument

The two-stage framework separating a screening stage that reveals true vulnerability for selected units from a final allocation stage under a fixed budget constraint.

If this is right

Screening provides greater efficiency improvements when aleatoric uncertainty is higher in the target population.
The strategy applies directly to improving accuracy in income-based social protection programs.
In humanitarian demining operations, it offers a way to balance the cost of screening against reduced allocation errors.
Algorithmic predictions and screening function as complements rather than substitutes under this optimal policy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This framework could inform resource allocation in other high-stakes domains such as medical treatment prioritization where outcomes are uncertain.
If the cost of screening varies across units, the optimal margin for screening would adjust accordingly.
Estimating the degree of aleatoric uncertainty in a given population becomes key for deciding how much screening to incorporate.

Load-bearing premise

The model assumes a fixed coverage budget and that screening reveals the exact true vulnerability status for the chosen units.

What would settle it

An experiment that measures total misallocation when following the marginal-screening policy versus a policy that screens the highest-risk units or random units, under controlled levels of uncertainty.

Figures

Figures reproduced from arXiv: 2605.07979 by Bryan Wilder, Carlos Patino, Mateo Dulce Rubio, Santiago Cortes-Gomez.

**Figure 2.** Figure 2: Allocation precision by screening budget α under optimal screening vs. no-screening baseline across risk distributions (β = 35%). Lines show averages over 10 simulations; shaded regions indicate ± one standard deviation [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 4.** Figure 4: Precision as a function of the screening budget [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗

**Figure 5.** Figure 5: Risk distributions used in simulations. All four share the same mean ( [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗

**Figure 6.** Figure 6: Allocation precision by screening budget [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗

read the original abstract

The rise of machine learning has shifted targeted resource allocation in policy and humanitarian settings toward algorithmic targeting based on predicted risk scores. This approach is typically cheaper and faster than traditional screening procedures that directly observe the latent vulnerability status through physical verification. Yet, even access to the true conditional vulnerability probability cannot eliminate misallocation: aleatoric uncertainty over individual vulnerability status is irreducible, and probabilistic targeting inevitably misallocates some resources. In this work we study how screening and algorithmic targeting should be optimally combined in a two-stage allocation framework where a screening stage observes true outcomes for a subset of units before a final allocation stage assigns the resource under a fixed coverage budget. We show that the optimal strategy screens units at the margin of algorithmic allocation, while directly targeting the highest-risk units. Furthermore, we empirically characterize when screening and algorithmic targeting act as complements or substitutes: efficiency gains from screening grow as the aleatoric uncertainty in the population increases. We illustrate our framework with applications in income-based social protection programs and humanitarian demining in Colombia, where the tension between screening costs and allocation efficiency is operationally consequential.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper cleanly characterizes the optimal mix of screening and algorithmic targeting under perfect information and shows complementarity with high aleatoric uncertainty.

read the letter

The one thing to take away is that this paper gives a precise answer to how screening and ML-based targeting should be combined when you have a limited budget and some units have uncertain vulnerability. Specifically, the optimal thing is to screen units that sit right at the cutoff of the algorithmic score, while assigning the resource directly to those with the highest predicted risk. They also find that the benefit of adding screening grows with the level of aleatoric uncertainty in the population, so the two tools are complements in noisier settings. The paper builds a two-stage model. In the first stage you decide a subset to screen, paying the cost and learning the true binary outcome for them. In the second stage you allocate the fixed number of resources using the mix of screened truths and the ML predictions for the rest. The characterization comes from showing that the value of screening is highest for units where the algorithm is most uncertain, i.e., near the threshold. For the empirical part they illustrate with data or parameters from Colombian social programs and demining, showing how the efficiency gain varies with uncertainty levels. It does a good job of making the abstract idea operational. The result is intuitive once you see it, but the derivation pins it down. The applications section helps ground why this matters beyond theory. The focus on aleatoric uncertainty rather than just epistemic is a useful distinction. Where it is softer is on the assumptions. The model treats screening as revealing the exact status with certainty, which simplifies the analysis but may not match field conditions where verification has its own error rates. If screening is imperfect, the optimal policy could shift away from the margin. It is also a one-period model, so it does not address how you might learn over multiple rounds or update the algorithm. The empirical characterization relies on varying parameters rather than out-of-sample validation with actual allocation outcomes. This paper is for people who work on designing targeting systems for anti-poverty programs or humanitarian aid, or more generally on decision-making under uncertainty with costly information. A reader who wants a clean theoretical handle on the screening-versus-prediction trade-off will get value from it. Because it has a novel characterization and ties it to concrete settings, it deserves to be sent for peer review.

Referee Report

1 major / 0 minor

Summary. The paper develops a two-stage allocation model combining algorithmic risk-score targeting with a screening stage that reveals true binary vulnerability status for a chosen subset of units, subject to a fixed coverage budget. It derives that the optimal policy screens units at the algorithmic allocation margin while directly assigning resources to the highest-risk units, and characterizes screening and algorithmic targeting as complements (with efficiency gains increasing in aleatoric uncertainty) versus substitutes. The framework is illustrated with applications to income-based social protection programs and humanitarian demining in Colombia.

Significance. If the derivations hold, the work supplies a principled, model-based account of when and how to combine imperfect ML predictions with costly but informative screening, directly addressing the irreducible misallocation that persists even with perfect conditional probabilities. The complements/substitutes characterization and the two concrete policy illustrations are useful for practitioners. The paper's explicit focus on aleatoric uncertainty as a fundamental limit of AI-driven allocation is a clear strength.

major comments (1)

[Abstract and model setup] Abstract and model setup: the headline optimality result (screening at the allocation margin while targeting highest-risk units) is derived under the assumption that screening reveals the true latent status with certainty. This assumption is load-bearing for the marginal-screening property and the complements/substitutes distinction; the skeptic's concern is therefore on point. The manuscript should either (a) state the precise conditions under which the result survives noisy screening or (b) add a robustness section showing how the policy changes when revelation is imperfect.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The major comment raises an important point about the scope of our optimality result, which we address directly below by outlining a targeted revision.

read point-by-point responses

Referee: [Abstract and model setup] Abstract and model setup: the headline optimality result (screening at the allocation margin while targeting highest-risk units) is derived under the assumption that screening reveals the true latent status with certainty. This assumption is load-bearing for the marginal-screening property and the complements/substitutes distinction; the skeptic's concern is therefore on point. The manuscript should either (a) state the precise conditions under which the result survives noisy screening or (b) add a robustness section showing how the policy changes when revelation is imperfect.

Authors: We agree that the perfect-revelation assumption is central to the closed-form characterization of the optimal policy and the complements/substitutes result. The model is intentionally constructed as a benchmark that isolates the interaction between algorithmic risk scores and costly but fully informative screening. To address the concern, we will add a new robustness subsection (Section 4.4) that (i) derives the first-order conditions under which the marginal-screening property continues to hold approximately when screening returns a noisy signal with known error rate ε, and (ii) presents numerical experiments across a range of ε values showing that the qualitative finding—screening efficiency gains increasing in aleatoric uncertainty—remains intact for moderate noise levels. For high noise the optimal policy tilts toward greater reliance on the algorithmic score, which we will document explicitly. This implements option (b) suggested by the referee while preserving the core analytic contribution under the perfect-screening benchmark. revision: yes

Circularity Check

0 steps flagged

No significant circularity; result follows from explicit model assumptions

full rationale

The paper derives its central theoretical claim—that optimal screening occurs at the algorithmic allocation margin while highest-risk units are directly targeted—from a two-stage optimization model with fixed coverage budget and perfect revelation of latent status upon screening. This is a standard first-principles derivation within the stated framework rather than a reduction to fitted parameters, self-definitions, or self-citations. No load-bearing self-citations, ansatzes smuggled via prior work, or renaming of known results appear in the abstract or described setup. The empirical characterization of complements/substitutes is presented as a separate analysis of efficiency gains under varying aleatoric uncertainty, without evidence of circularity. The derivation is self-contained against the model's assumptions.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Based solely on the abstract, the framework rests on standard domain assumptions about resource budgets and observability via screening; no free parameters or invented entities are explicitly introduced in the provided text.

axioms (2)

domain assumption Fixed coverage budget for final allocation
Stated as part of the two-stage allocation framework.
domain assumption Screening observes true vulnerability status for selected units
Core premise enabling the distinction between screening and pure algorithmic targeting.

pith-pipeline@v0.9.0 · 5494 in / 1130 out tokens · 42143 ms · 2026-05-11T03:15:21.153803+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Machine learning and phone data can improve targeting of humanitarian aid.Nature, 603(7903):864– 870, 2022

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work page

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What uncertainties do we need in bayesian deep learning for computer vision?Advances in neural information processing systems, 30, 2017

Alex Kendall and Yarin Gal. What uncertainties do we need in bayesian deep learning for computer vision?Advances in neural information processing systems, 30, 2017. 3

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Who Should be Treated? Empirical Welfare Maximiza- tion Methods for Treatment Choice.Econometrica, 86(2):591–616, 2018

Toru Kitagawa and Aleksey Tetenov. Who Should be Treated? Empirical Welfare Maximiza- tion Methods for Treatment Choice.Econometrica, 86(2):591–616, 2018. 6

work page 2018

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A Machine-Learning-Based Approach to Informing Student Admission Decisions.Behavioral Sciences, 15:330, 03 2025

Tuo Liu, Cosima Schenk, Stephan Braun, and Andreas Frey. A Machine-Learning-Based Approach to Informing Student Admission Decisions.Behavioral Sciences, 15:330, 03 2025. 3

work page 2025

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Optimal Individualized Treatments in Resource-Limited Settings.The International Journal of Biostatistics, 12(1):283–303, 2016

Alexander R Luedtke and Mark J Van Der Laan. Optimal Individualized Treatments in Resource-Limited Settings.The International Journal of Biostatistics, 12(1):283–303, 2016. 6

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Regional Office for Europe.Screening Programmes: A Short Guide

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The Application of Artificial Intelligence in Health Care Resource Allocation Before and During the COVID-19 Pandemic: Scoping Review.JMIR AI, 2:e38397, 01 2023

Hao Wu, Xiaoyu Lu, and Hanyu Wang. The Application of Artificial Intelligence in Health Care Resource Allocation Before and During the COVID-19 Pandemic: Scoping Review.JMIR AI, 2:e38397, 01 2023. 3 13 A Notation Symbol Definition X∈ XCovariates and feature space. Y∈ {0,1}Vulnerability status indicating benefit from resource. µ(X) =P(Y= 1|X) Bayes-optimal...

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