发表机构
Snap Inc.（斯奈普公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究探讨对称双边A/B测试中孤立设计的生态成本，发现重尾匹配质量下该成本不会随平台规模增长消失，还给出预启动估算及流量调整的备用设计方案。
AI 中文摘要
在双边内容平台上，对称双边孤立（将创作者和观众的匹配比例分配至孤立的实验组和对照组子市场）因能消除组间市场干扰，被广泛用于创作者侧和冷启动实验。然而，孤立会缩减每个观众的候选目录，直觉上认为由此产生的参与成本应随平台规模增长而消退：庞大目录的一小部分依然庞大。我们在参与度的顺序统计模型中表明，该直觉是否成立取决于匹配质量的上尾。极值理论产生了具有明显二分性的尾类损失定律：对于轻尾或有界尾，损失随候选池规模增长而消失；而在重尾情况下，损失收敛至与规模无关的常数，因此即使将候选池扩大数个数量级，也无法渐近消除该成本。来自拥有数百万活跃创作者的平台上的两项生产实验的证据与这一情况一致：纯A/A流量扫描显示出可测量的、按深度分级的参与成本；单侧目录消融实验独立表明，每个观众的候选目录缩减是该损失的成因；且在小型探索池上校准的尾指数预测的效应，与在大得多的全目录消融实验中观察到的效应一致。因此，孤立会产生实验者应预留预算的成本，如同其他任何成本一样。我们为从业者提供了一种预启动程序，可在上线前估算该成本、相应确定流量规模，并在预测成本超过选定容忍度时推荐备用设计。
英文摘要
On two-sided content platforms, symmetric two-sided isolation (assigning matched fractions of creators and viewers to isolated treatment and control submarkets) is widely used for creator-side and cold-start experiments because it removes cross-arm marketplace interference. Isolation, however, thins each viewer's candidate catalog, and intuition suggests the resulting engagement cost should fade as the platform grows: a small fraction of a vast catalog is still vast. We show that, in an order-statistics model of engagement, whether this intuition holds depends on the upper tail of match quality. Extreme-value theory yields tail-class loss laws with a sharp dichotomy: for light or bounded tails the loss vanishes as the candidate pool grows, whereas under heavy tails it converges to a size-independent constant, so expanding the candidate pool, even by orders of magnitude, does not asymptotically eliminate the cost. Evidence from two production experiments on a platform with millions of active creators is consistent with this picture: a pure A/A traffic sweep reveals a measurable, depth-graded engagement cost; a one-sided catalog ablation independently shows that per-viewer thinning contributes to the loss; and a tail index calibrated on the small exploration pool predicts an effect consistent with the one observed in the far larger full-catalog ablation. Isolation thus carries a price that experimenters should budget for, like any other cost. We give practitioners a preflight procedure that estimates it before launch, sizes traffic accordingly, and recommends a fallback design when the predicted cost exceeds a chosen tolerance.
