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arXiv Machine Learning By Ahmed Mahrous, Roberto Di Pietro

The Reflexivity Threshold: A Phase Transition for Multi-Agent Performative Prediction

Read the original on arXiv Machine Learning →

The paper introduces the reflexivity matrix Ω to capture how one agent’s deployment influences another’s best response in multi‑agent performative prediction. It identifies a spectral threshold—when the spectral radius of Ω exceeds one—separating stable learning from worst‑case learning obstruction. Below this threshold, repeated retraining converges geometrically to a unique stable equilibrium with bounded excess loss; above it, cumulative excess loss can grow linearly with the horizon.

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