A scalable Aumann-Shapley attribution method for million-agent systems reveals that small-scale samples structurally misattribute emergence under nonlinear macro indicators, as shown by the Attribution Scaling Bias theorem.
Mert Cemri, Melissa Z
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A new model derives a convex systemic risk coupling r(φ) that grows superlinearly with AI adoption share, producing a saddle-node bifurcation to algorithmic monoculture and 18-54% tail-loss amplification, validated on SEC 13F holdings data.
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Attributing Emergence in Million-Agent Systems
A scalable Aumann-Shapley attribution method for million-agent systems reveals that small-scale samples structurally misattribute emergence under nonlinear macro indicators, as shown by the Attribution Scaling Bias theorem.
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Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets
A new model derives a convex systemic risk coupling r(φ) that grows superlinearly with AI adoption share, producing a saddle-node bifurcation to algorithmic monoculture and 18-54% tail-loss amplification, validated on SEC 13F holdings data.