← 研踪 PaperTrailarXiv:2602.10039 · v2
Budgeting Discretion: Theory and Evidence on Street-Level Decision-Making
首次提交 2026-02-11 02:02
版本更新 2026-11-06 05:14
雷达观测 2026-11-09 10:16
摘要
Street-level bureaucrats, such as caseworkers, triage nurses, and school administrators, operate under a fundamental tension. Official policy on who to prioritize may conflict with bureaucrats' estimation of who would benefit most. The theory of street-level bureaucracy argues that frontline agents bridge this gap, overriding policy when professional judgment identifies opportunities to improve outcomes. As algorithmic recommendations enter allocation settings, understanding this behavior is key to designing tools that account for professional judgment. We formalize discretion as a finite-horizon dynamic allocation problem. An agent follows a default policy but has a budget of $K$ overrides across $T$ periods. Each period, the agent observes the welfare gain from deviating and decides whether to spend an override now or save it for later. We show that the optimal policy is a state-dependent threshold rule and prove a behavioral invariance: for location-scale families of improvement distributions, the rate at which an optimal agent exercises discretion is independent of welfare-gain magnitude and depends only on the distribution's shape. Fat-tailed gains induce patience (conserving overrides for rare, high-stakes cases); thin-tailed gains induce routine, front-loaded spending. We also compare the model's implications with data from a homelessness service system. In the model, a capacity opening makes an override cheaper to spend, lowering the threshold for using one. In the data, override rates shift with short-run capacity openings in scarce housing, rising when exits free slots and falling when interventions are congested. Descriptively, spending is front-loaded: override rates peak on Mondays and early in the fiscal year. Together, the model and data point to systematic structure in discretion, informing the design of more efficient and equitable human-AI allocation systems.
作者备注：Accepted to NeurIPS 2026, Spotlight
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v2 · 2026-11-06 05:14 · 最新观测版本
Budgeting Discretion: Theory and Evidence on Street-Level Decision-Making
Street-level bureaucrats, such as caseworkers, triage nurses, and school administrators, operate under a fundamental tension. Official policy on who to prioritize may conflict with bureaucrats' estimation of who would benefit most. The theory of street-level bureaucracy argues that frontline agents bridge this gap, overriding policy when professional judgment identifies opportunities to improve outcomes. As algorithmic recommendations enter allocation settings, understanding this behavior is key to designing tools that account for professional judgment. We formalize discretion as a finite-horizon dynamic allocation problem. An agent follows a default policy but has a budget of $K$ overrides across $T$ periods. Each period, the agent observes the welfare gain from deviating and decides whether to spend an override now or save it for later. We show that the optimal policy is a state-dependent threshold rule and prove a behavioral invariance: for location-scale families of improvement distributions, the rate at which an optimal agent exercises discretion is independent of welfare-gain magnitude and depends only on the distribution's shape. Fat-tailed gains induce patience (conserving overrides for rare, high-stakes cases); thin-tailed gains induce routine, front-loaded spending. We also compare the model's implications with data from a homelessness service system. In the model, a capacity opening makes an override cheaper to spend, lowering the threshold for using one. In the data, override rates shift with short-run capacity openings in scarce housing, rising when exits free slots and falling when interventions are congested. Descriptively, spending is front-loaded: override rates peak on Mondays and early in the fiscal year. Together, the model and data point to systematic structure in discretion, informing the design of more efficient and equitable human-AI allocation systems.
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