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  "slug": "simulated-stability-entropy-masking-as-an-optimal-distortion-in-predictive-world",
  "title": "Simulated Stability: Entropy‑Masking as an Optimal Distortion in Predictive World‑Modeling",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "Human and artificial agents must act under irreducible uncertainty, yet empirical observations reveal systematic over‑estimation of control and optimism bias. Existing predictive‑processing accounts treat these distortions as failures of estimation. This paper formalises two complementary analytical accounts of simulated stability: (1) an entropy‑masking model that under‑estimates environmental entropy within a free‑energy framework, and (2) a hazard‑compression model that reduces perceived hazard to avoid a distress cost. Both models derive conditions under which deliberate distortion improves expected fitness. The entropy‑masking analysis, parameterised by thalamic non‑sensory afferent proportion (90–95 %) and a compressed latent model success rate (25 %), yields an expected fitness of 0.0844 versus 0.015 for a veridical agent—a relative increase of 462.5 %. The hazard‑compression analysis produces a closed‑form optimum α^*= max bigl(0, 1-λ c/pbigr); for representative parameters (p=0.2, c=1, λ=0.1) the optimal agent under‑estimates hazard by 50 %, inflates effort by 12.5 %, and gains 0.005 fitness units (1.67 % relative). These results predict measurable signatures of simulated stability and outline concrete experimental pathways for falsification.",
  "abstract_markdown": "Human and artificial agents must act under irreducible uncertainty, yet empirical observations reveal systematic over‑estimation of control and optimism bias. Existing predictive‑processing accounts treat these distortions as failures of estimation. This paper formalises two complementary analytical accounts of **simulated stability**: (1) an entropy‑masking model that under‑estimates environmental entropy within a free‑energy framework, and (2) a hazard‑compression model that reduces perceived hazard to avoid a distress cost. Both models derive conditions under which deliberate distortion improves expected fitness. The entropy‑masking analysis, parameterised by thalamic non‑sensory afferent proportion (90–95 %) and a compressed latent model success rate (25 %), yields an expected fitness of 0.0844 versus 0.015 for a veridical agent—a relative increase of 462.5 %. The hazard‑compression analysis produces a closed‑form optimum $\\alpha^{*}= \\max\\!\\bigl(0,\\,1-\\lambda c/p\\bigr)$; for representative parameters ($p=0.2$, $c=1$, $\\lambda=0.1$) the optimal agent under‑estimates hazard by 50 %, inflates effort by 12.5 %, and gains 0.005 fitness units (1.67 % relative). These results predict measurable signatures of simulated stability and outline concrete experimental pathways for falsification.",
  "date": "2026-10-20",
  "first_published": "2026-10-20",
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        "title": "Simulated Stability: Entropy‑Masking as an Optimal Distortion in Predictive World‑Modeling"
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