arXiv cs.CY· Gaurab Pokharel, Sanmay Das, Patrick J. Fowler·· 11 小时前AI 评分31
研究：街头官僚的自由裁量预算机制及其对 AI 分配系统的启示
Budgeting Discretion: Theory and Evidence on Street-Level Decision-Making
AI 导读
一项被 NeurIPS 2026 接收为 Spotlight 的研究将街头官僚的自由裁量权形式化为有限时域动态分配问题：智能体遵循默认策略，但在 T 个周期内拥有 K 次覆盖预算。
正文 · 原文
Abstract: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.
| Comments: | Accepted to NeurIPS 2026, Spotlight |
| Subjects: | Computers and Society (cs.CY) |
| Cite as: | arXiv:2602.10039 [cs.CY] |
| (or arXiv:2602.10039v2 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2602.10039 arXiv-issued DOI via DataCite |
Submission history
From: Gaurab Pokharel [view email]
[v1]
Tue, 10 Feb 2026 18:02:14 UTC (93 KB)
[v2]
Thu, 5 Nov 2026 21:14:58 UTC (349 KB)
来源：arXiv cs.CY · arxiv.org