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.
International Atomic Energy Agency
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
Limited legal personhood for AI, implemented via purpose-bound operating companies within human-controlled holding structures, serves as a precautionary governance instrument that enables transparency and accountability without resolving questions of artificial consciousness.
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
Proposes eight criteria for escalating AI incidents internationally, derived from regulations like the EU AI Act, tested on ten incidents, and identifies three patterns of under-detection in current designs.
citing papers explorer
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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.
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Precautionary Governance of Autonomous AI: Legal Personhood as Functional Instrument
Limited legal personhood for AI, implemented via purpose-bound operating companies within human-controlled holding structures, serves as a precautionary governance instrument that enables transparency and accountability without resolving questions of artificial consciousness.
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Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
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Designing escalation criteria for international AI incident response: criteria, triggers, and thresholds
Proposes eight criteria for escalating AI incidents internationally, derived from regulations like the EU AI Act, tested on ten incidents, and identifies three patterns of under-detection in current designs.