Delayed Repression and Emergent Instability in Adaptive Multi-Agent Systems
A new research paper explores how delays in regulatory intervention can destabilize adaptive multi-agent systems. The study found that reactive agents, which immediately respond to signals, are highly susceptible to instability when faced with delayed repression, leading to oscillations. In contrast, agents using reinforcement learning (Q-learning) demonstrated greater resilience due to their ability to learn from past punishments, buffering the destabilizing effects of delayed feedback. AI
IMPACT Highlights how system design and reaction delays can lead to emergent instability in AI agents, impacting the design of safe and robust multi-agent systems.