From Risk Scoring to Risk Allocation: A Density-Driven Framework for Diverse Monitoring in Multi-Agent Systems
Risk monitoring in multi-agent systems is commonly built on a per-state primitive that scores each state independently and selects the top K. Under crowding, where many agents share the same fragility, this approach picks redundant alerts whose risks are jointly correlated, a pattern we describe as ``herding in monitoring.'' We propose a paradigm shift from risk scoring to risk allocation, supported by two contributions. First, we identify the Crowding Paradox, namely that P(risk | x) $\propto$ p(x) rather than 1/p(x), so density rather than anomaly score is the operative risk signal; on financial data, density-based scoring reaches AUROC $\geq$ 0.94 at 5d/10d/20d crash horizons, while fi...
| Authors | Zhaohui Wang |
|---|---|
| Primary category | cs.LG |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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