提出可验证的分摊机制，让高风险者负担更轻且不伤害他人。
Conformal Risk Sharing: Certified Cost Allocation with Participation Guarantees
- 用可解释的分配策略结合分割校准，实现无分布假设下的风险分摊。
- 在真实降水与能源数据上，高风险参与者最大责任降低超70%。
- 适合需要公平分担极端风险的金融、保险与能源合作场景。
通过群体分摊罕见负面事件的财务影响，可减轻个体极端负担，但若某参与者因安排而受损，则有退出动机。因此，可信机制必须为每位参与者提供未来责任的可靠上限，并仅在总体损害有限时部署。我们将其形式化为认证分配问题：仅基于有限数据，无需分布假设，寻找再分配规则，生成每位参与者的责任上限，并验证无人遭受实质性损失。本文提出共形风险分摊（Conformal Risk Sharing），通过将可解释的分配策略与分割共形校准相结合解决该问题。分配强度在训练数据上调优，而保留的校准数据生成无需分布假设的个体保障（在可交换性下成立）。在合成数据和真实世界数据（包括降水与能源合作数据）上的实验表明，该框架可显著降低高风险参与者的极端责任，同时控制对其他人的损害。
原文摘要 · Abstract (English)
Sharing the financial impact of rare adverse events across a group can soften extreme individual burdens, but any participant made worse off by the arrangement has reason to leave. A credible mechanism must therefore provide each agent with a trustworthy cap on their future obligation and should be deployed only if the aggregate harm across participants is bounded. We formalise this as the Certified Allocation Problem: from finite data and without distributional assumptions, find a redistribution rule, produce obligation caps for every participant, and verify that no participant is made materially worse off. We propose Conformal Risk Sharing, which solves this problem by pairing an interpretable sharing policy with split conformal calibration. The sharing intensity is tuned on training data, while held-out calibration data produces distribution-free per-agent guarantees (valid under exchangeability). Experiments on synthetic and real-world data, including precipitation and energy-cooperative data, confirm that the framework can substantially reduce extreme obligations for high-risk agents while controlling harm to others.
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