---
slug: model_risk
name: Model Risk
one_liner: "A second-order application of risk to itself: the risk of loss attributable not to the world but to the risk model used to measure the world — mis-specification, mis-calibration, or misuse of the measuring tool, an error invisible to the model that produces it."
type: prime
schema_version: 2
status: density_v2_draft
categories: [structural, economic, computational]
origin_domain: quantitative_finance
alternate_origin_domains: [risk_management, machine_learning, engineering, epidemiology]
aliases: [model_error, risk_of_model_misspecification, measurement_tool_risk, risk_from_the_risk_model, valuation_model_risk]
related: []
solution_archetypes: []
review_flags:
- "OPERATOR-DERIVED (op-02 self-application, route R / reflexive) — the SECOND-ORDER prime obtained by applying `risk` to itself: the risk that one's own risk model is wrong. Where base-level `risk` quantifies exposure to a distribution of adverse outcomes, model_risk is the risk arising from the risk MODEL itself being mis-specified, mis-calibrated, or misused — a probability of loss attributable to the measuring tool rather than the world. Discovered in the op-02 sweep 2026-07-03. MODERATE hold (verified absent from corpus; established named construct — Derman 1996 'Model Risk'; Basel/regulatory model-risk management). STATUS: HOLD pending owner keep/cut."
- "CLUSTER-MERGE FLAG (dominant open question): model_risk sits in a tight meta-uncertainty cluster with three sibling op-02 holds — `second_order_uncertainty`, `second_order_probability`, and `model_uncertainty` — all four being 'uncertainty/risk about one's own uncertainty/risk apparatus.' model_risk is the risk-management/finance framing and emphasizes the CONSEQUENCE (loss from a wrong model) rather than the epistemic state, and is the MOST FRAMED of the four (it carries finance vocabulary). Owner must decide at curation whether these are ONE prime, a genus+species, or distinct primes. If the cluster resolves to a single prime it should probably be the more structural `second_order_uncertainty`, with model_risk as its finance-framed instance. RESOLVE THE WHOLE 4-MEMBER CLUSTER TOGETHER."
- "Created 2026-07-03; STUB ONLY (frontmatter + Core Idea + Notes). SI/SF/similarity are PROPOSER ESTIMATES pending triangulation. Body sections (Structural Signature onward) NOT written."
- "Phase-C action: re-similarity-check vs `risk` (the input / parent) AND the cluster siblings `second_order_uncertainty`, `second_order_probability`, `model_uncertainty`; confirm it is the genuine second-order lift (loss attributable to the measurement tool, invisible to that tool) and not merely a finance-framed restatement of a general meta-uncertainty prime; resolve the cluster; draw a subsumption edge risk→model_risk (or fold under the retained cluster prime) at incorporation; write a v1 draft and sync the one_liner across v1/v2 per CLAUDE.md before merge."
substrate_independence:
  composite_substrate_independence: 3
  domain_breadth: 4
  structural_abstraction: 3
  transfer_evidence: 4
  reasoning: 'The bare structure — a probability of loss attributable to the MODEL of risk being wrong rather than to the underlying events, an error the producing model cannot itself price — is substrate-neutral in its "model-of-X-being-wrong" skeleton, and recurs wherever a decision relies on a formal model: VaR/valuation-model failure in finance, safety-factor model error in engineering, distribution-shift and model-misspecification risk in machine learning, and structural model uncertainty in climate/epidemiological projection. That gives real domain breadth (4). But structural_abstraction is held at 3 because the "risk/loss" framing adds substantive weight: the prime is not merely "the meta-model may be wrong" (that is the more structural `second_order_uncertainty`) but specifically "expected LOSS from the wrong model," which imports an evaluative/consequence cast and a strong finance idiom. Transfer evidence is strong (independently named across finance, ML, engineering, and projection sciences), but the framing residue keeps the composite at 3 rather than higher. Note: these estimates are entangled with the cluster-merge question — if model_risk folds into `second_order_uncertainty`, the retained prime would score more structural.'
similarity_to_nearest_existing_prime:
  slug: risk
  similarity: 0.58
structural_framed:
  label: mixed
  aggregate: 0.46
  criteria: {vocab_travels: 0.6, evaluative_weight: 0.5, institutional_origin: 0.5, human_practice_bound: 0.4, import_vs_recognize: 0.5}
  rationale: 'Reads mixed. The "model of X being wrong" structure is genuinely substrate-neutral and recognized across finance, ML, engineering, and projection sciences, which pulls toward structural. But the "risk/loss" framing adds real weight: the prime foregrounds expected LOSS attributable to the measurement tool (an evaluative/consequence cast) and carries a strong finance/regulatory idiom (VaR, model validation, Basel model-risk management), which pulls toward framed. It is the MOST FRAMED member of its meta-uncertainty cluster — the finance-vocabulary carrier — whereas its sibling `second_order_uncertainty` reads more purely structural. Aggregate lands mid-scale; the label should be revisited once the cluster-merge question is resolved, since folding model_risk into a general meta-uncertainty prime would shift the retained node toward structural.'
phase_b_passes_completed: []
---
# Model Risk

## Core Idea

Model risk is a *second-order* application of `risk` to itself: the risk that one's own risk model is wrong. Base-level `risk` quantifies exposure to a distribution of adverse outcomes — it prices the probability and magnitude of loss arising from *the world*. Model risk names a different source of loss entirely: loss attributable not to the world but to the *tool used to measure the world*. When a risk model is mis-specified (wrong functional form, missing factor, false independence assumption), mis-calibrated (parameters fit to a regime that no longer holds), or misused (applied outside its domain of validity), the loss that follows is caused by the model, not by the events the model was meant to track. It is reached from `risk` by applying the risk lens to the risk apparatus: the new meta-level object is *the model/measure of risk*, not the underlying events.

The load-bearing anatomy is a feature present at the meta level but absent at the base: *the error is invisible to the model that produces it*. A model cannot price the risk of its own wrongness — its probability assignments are computed *within* its own specification, so a mis-specification does not appear as high variance inside the model; it appears as false confidence. This produces the signature failure mode: model risk manifests as *correlated, regime-shift* loss precisely when the model is trusted most, because the same hidden mis-specification biases every quantity the model reports in the same direction at once. Base-level `risk` has no vocabulary for this — within a fixed model the model itself is a certain background, not a variable — so lifting risk up one level turns the measuring instrument from a certainty into the thing that is now uncertain, exposed, and potentially loss-bearing.

Read structurally, the same second-order shape appears wherever a decision relies on a formal model, and the siblings are genuinely unlike. In *quantitative finance* it is VaR-model failure or valuation-model error — loss because the pricing/risk model was wrong, formalized in Derman's original treatment and in regulatory model-risk management. In *engineering* it is safety-factor or structural-model error — the design fails because the model of the loads or materials was mis-specified, not because the loads were unusually large. In *machine learning* it is distribution-shift and model-misspecification risk — loss because the learned model's assumptions no longer match deployment. In *climate and epidemiology* it is structural model uncertainty in projection — the possibility that the projection model's structure, not merely its inputs, is wrong. Across these, the shared abstract form is a probability of loss attributable to a wrong model of the risk, invisible to that model; the finance case is the most vocabulary-laden species of the genus.

## Notes

**Provenance.** Produced by operator 02 (self-application), route R (reflexive), as the second-order prime obtained by applying `risk` to itself — the risk that one's own risk model is wrong. Surfaced in the op-02 self-application sweep on 2026-07-03. Established named construct: Derman 1996 ("Model Risk", Goldman Sachs Quantitative Strategies), regulatory model-risk management (Basel / supervisory model-risk frameworks), and the broader "model error" literature. Substrate-neutral across any decision that relies on a formal model — finance (VaR-model failure), engineering (safety-factor model error), ML (distribution-shift / model-misspecification risk), climate/epidemiology (structural model uncertainty in projection). Verified absent from the corpus. Manifest: MODERATE hold.

**Meta-uncertainty cluster (the dominant open question).** model_risk does NOT stand alone: it sits in a tight cluster with three sibling op-02 holds — `second_order_uncertainty`, `second_order_probability`, and `model_uncertainty` — all four being variations on "uncertainty/risk about one's own uncertainty/risk apparatus." model_risk is the risk-management/finance framing and emphasizes the *consequence* (loss from a wrong model) rather than the *epistemic state* (not knowing whether the model is right). Critically, model_risk is the MOST FRAMED of the four — it is the member carrying finance/regulatory vocabulary (VaR, model validation, Basel). The owner must decide at curation whether these are one prime, a genus + species set, or distinct primes; this question should be resolved for the whole 4-member cluster at once, not for model_risk in isolation.

**For owner evaluation (keep/cut).** The case FOR: model_risk is an *established, named construct* (Derman; regulatory model-risk management), verified absent, with a real level-2 structure — loss attributable to the measurement tool and *invisible to that tool* (a model cannot price the risk of its own wrongness), producing correlated regime-shift failure when the model is trusted most. The case AGAINST: (1) the **cluster-merge question is dominant** — model_risk likely folds with `model_uncertainty` and/or `second_order_uncertainty`; (2) it may be the **most framed** (finance) member of the cluster, so if the cluster resolves to a single prime, that prime should probably be the more structural `second_order_uncertainty`, with model_risk retained (if at all) as its finance-framed instance. Sharp keep/cut question: *is model_risk a distinct prime, or is it the finance-framed instance of a general meta-uncertainty prime?* — the proposer's recommendation is that the owner resolve the entire 4-member cluster (`model_risk`, `model_uncertainty`, `second_order_uncertainty`, `second_order_probability`) together rather than adjudicating model_risk on its own.

**At incorporation.** Re-run similarity against `risk` (input / parent) AND the three cluster siblings `second_order_uncertainty`, `second_order_probability`, and `model_uncertainty` to determine the collapse/merge structure before any edge is drawn. If model_risk is kept as a distinct prime: draw a subsumption edge `risk` → `model_risk` and sibling/genus edges among the cluster members (or under whichever cluster prime is retained as genus). If the cluster folds: fold model_risk under the retained prime (most likely `second_order_uncertainty`) as a framed finance instance and record the redirect. Add a missing-edge note flagging the meta-uncertainty cluster relationship explicitly. Write a v1 draft and sync the one_liner across v1 and v2 per CLAUDE.md before merge.
