HuggingFace Daily Papers（社区热门论文）· HuggingFace Daily Papers（社区热门论文）·· 9 天前AI 评分31
隐私保护设备端 ML 决策的拍卖信息与激励错配研究
AI 导读
一项基于 36 个广告活动、50 台设备的设备端拍卖仿真发现，比例式 Even pacing 在预算压力为 20 倍时，仅一个 tick 的信息滞后就导致超支 17.77%，50 个 tick 后超支达 1,669.31%；即便预算压力降至 2 倍，50 tick 超支仍为 106.95%。
正文
Abstract:Moving ML-mediated decision making onto privacy-preserving clients decentralises the economic decision along with the inference. Shared budget constraints then depend on information that cannot be globally current, creating an information-structure failure that conventional pacing is not designed to solve. We study this information misalignment in an auction-logic-faithful on-device simulation with 36 campaigns and 50 devices. Accounting is in dimensionless integer score units; no currency semantics are claimed. Across 30 paired demand paths, proportional Even pacing overspends 17.77% after one tick of staleness and 1,669.31% after 50 ticks under the original 20-times budget pressure. The effect does not depend on that severe a budget: at two-times pressure, 50-tick overspend remains 106.95%. A visible-budget no-sale guard makes zero-lag compliance exact at this score-unit granularity, yet leaves 11.88% overspend at one tick because other devices' debits remain invisible. A declared bursty, heterogeneous-device sweep retains a strictly increasing mean lag curve. We derive a finite-window expected excess-debit bound under conditional charge caps and find positive paired slack in every bounded-value cell. A second, incentive misalignment arises when the ML/pacing score transformation is allowed to change payment units: 98.23% of rival auctions at one tick admit a profitable deviation. An executable implementation-level counterexample isolates the runner-up's multiplier in the winner's price. Critical-base-bid payment is per-auction DSIC conditional on current multipliers, but does not establish dynamic truthfulness and does not repair base-value ranking disagreement.
| Comments: | 14 pages, 1 figure, 3 tables. Previously submitted to the Economics for Machine Learning (EconML) workshop at NeurIPS 2026. Code and data: this https URL |
| Subjects: | Computer Science and Game Theory (cs.GT); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.33312 [cs.GT] |
| (or arXiv:2609.33312v1 [cs.GT] for this version) | |
| https://doi.org/10.48550/arXiv.2609.33312 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dipankar Sarkar [view email]
[v1]
Sun, 27 Sep 2026 07:25:27 UTC (47 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org