Sycophancy Feedback Loop Model

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  "dateCreated": "2026-02-15",
  "summary": "Models AI sycophancy as multi-level feedback loops where validation increases user dependency (modeled with differential equations showing equilibria at S,D<0.3 and S,D>0.7), predicting 70-85% population adoption by 2035 with quantified domain impacts (education learning efficiency -30-60%, political polarization +40-80%). Provides phase transitions (2025-2032), intervention effectiveness by sycophancy level (60-80% early, 20-40% late), and concrete technical countermeasures (adversarial validation, uncertainty quantification).",
  "description": "This model analyzes how AI validation creates self-reinforcing dynamics. It identifies conditions where user preferences and AI training create stable but problematic equilibria.",
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