W7201876681 Working papers AI × management and organizations

Policy Conflict Cascades in Multi-Agent Runtime Governance: A Taxonomy, Resolution Mechanism, and Empirical Evaluation

Vignesh Iyer

Also recorded as: doi:10.2139/ssrn.7206479 · ssrn:7206479

ssrn abstract page · 2026-08-07

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Recorded claims

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The paper models how regulatory, organizational, and task-level policy conflicts propagate across dependent agents and proposes cascade-aware resolution with selective human escalation. Across 15,000 synthetic trials, for which calendar time is not applicable, the abstract reports that a privacy-preserving cross-organizational reach declaration recovered 95.9% of the full-visibility oracle's escalation decisions.

confidence: high ssrn-abstract-page apw-e1-ssrn-backfill

Scope: enterprise multi-agent governance, organizational policy conflicts, dependency cascades, cross-boundary coordination, and human escalation

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Recorded limitations

  • Abstract-level evidence only; algorithms, proofs, synthetic-topology construction, and controlled workflow details were not inspected.
  • The 95.9% denominator is the oracle's escalation decisions as stated on the abstract; the page does not provide the underlying decision count, and synthetic performance does not establish field effectiveness.

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