Adaptive Accountability in Networked MAS: Tracing and Mitigating Emergent Norms at Scale
Saad Alqithami

TL;DR
This paper introduces the Adaptive Accountability Framework (AAF), a runtime system for large-scale multi-agent systems that detects and mitigates undesirable emergent norms, ensuring system compliance and stability.
Contribution
The paper presents AAF, a novel framework that records interaction provenance, detects norm violations, attributes responsibility, and applies interventions to maintain system integrity.
Findings
AAF reduces norm violations in simulations by 11.9% median
Detects violations with median delay of 71 steps
Achieves 97% attribution accuracy at 10% Byzantine rate
Abstract
Large-scale networked multi-agent systems increasingly underpin critical infrastructure, yet their collective behavior can drift toward undesirable emergent norms such as collusion, resource hoarding, and implicit unfairness. We present the Adaptive Accountability Framework (AAF), an end-to-end runtime layer that (i) records cryptographically verifiable interaction provenance, (ii) detects distributional change points in streaming traces, (iii) attributes responsibility via a causal influence graph, and (iv) applies cost-bounded interventions-reward shaping and targeted policy patching-to steer the system back toward compliant behavior. We establish a bounded-compromise guarantee: if the expected cost of intervention exceeds an adversary's expected payoff, the long-run fraction of compromised interactions converges to a value strictly below one. We evaluate AAF in a large-scale…
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Taxonomy
TopicsReinforcement Learning in Robotics · Distributed Control Multi-Agent Systems · Infrastructure Resilience and Vulnerability Analysis
