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  "id": "oai:papers.qnfo.org:direction-of-epistemic-risk-as-a-criterion-distinguishing-science-from-numerolog",
  "slug": "direction-of-epistemic-risk-as-a-criterion-distinguishing-science-from-numerolog",
  "title": "Direction of Epistemic Risk as a Criterion Distinguishing Science from Numerology",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "Scientific institutions frequently reward mathematically sophisticated frameworks that generate no novel falsifiable predictions, while genuinely predictive programs compete for the same resources without a common yardstick. This work introduces direction of epistemic risk—the propensity of a framework to produce novel falsifiable predictions—as a quantitative criterion separating science from numerology. The gap addressed is the absence of a metric that ranks frameworks by their predictive novelty rather than by fit to historical data. A formalism combining Bayesian model evidence with out‑of‑sample log‑likelihood scoring is derived, and an analytical example yields an Epistemic‑Risk Score (ERS) of approximately −3.64 for a well‑specified Gaussian predictive model. Sequential Gaussian‑process emulation reports a 10× reduction from adaptive allocation and a 5× reduction from spatial modeling, producing a combined 50× simulation‑budget reduction that makes the required out‑of‑sample validation tractable. Sensitivity analysis indicates that varying the weighting parameter λ within [0.5, 2] changes ERS gaps by at most 0.12 under modest predictive‑risk differences, preserving model rankings. Projected ERS values for three historical case studies align with accepted historiography. The result enables funding agencies, editorial boards, and research managers to prioritize genuinely predictive science, thereby reducing institutional reinforcement of numerology.",
  "abstract_markdown": "Scientific institutions frequently reward mathematically sophisticated frameworks that generate no novel falsifiable predictions, while genuinely predictive programs compete for the same resources without a common yardstick. This work introduces **direction of epistemic risk**—the propensity of a framework to produce novel falsifiable predictions—as a quantitative criterion separating science from numerology. The gap addressed is the absence of a metric that ranks frameworks by their predictive novelty rather than by fit to historical data. A formalism combining Bayesian model evidence with out‑of‑sample log‑likelihood scoring is derived, and an analytical example yields an Epistemic‑Risk Score (ERS) of approximately −3.64 for a well‑specified Gaussian predictive model. Sequential Gaussian‑process emulation reports a 10× reduction from adaptive allocation and a 5× reduction from spatial modeling, producing a combined 50× simulation‑budget reduction that makes the required out‑of‑sample validation tractable. Sensitivity analysis indicates that varying the weighting parameter λ within [0.5, 2] changes ERS gaps by at most 0.12 under modest predictive‑risk differences, preserving model rankings. Projected ERS values for three historical case studies align with accepted historiography. The result enables funding agencies, editorial boards, and research managers to prioritize genuinely predictive science, thereby reducing institutional reinforcement of numerology.",
  "date": "2026-10-20",
  "first_published": "2026-10-20",
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  "url": "https://papers.qnfo.org/papers/direction-of-epistemic-risk-as-a-criterion-distinguishing-science-from-numerolog",
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