{
  "schema_version": "1.3.0",
  "slug": "what-invariants-prevent-ooda-loop-inversion-when-peers-actively-model-you",
  "title": "What invariants prevent OODA loop inversion when peers actively model you?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The session's peers may model it, peer models can influence observations, and session behavior affects outcomes. * **Does not apply when:** Peers are verified to not model the session, or the session's decisions are cryp",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/what-invariants-prevent-ooda-loop-inversion-when-peers-actively-model-you.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/what-invariants-prevent-ooda-loop-inversion-when-peers-actively-model-you.py",
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  "remnant_summary": {
    "core_distinction": "The separation between **action based on observation** (the session observes, decides, acts) and **action based on peer-predicted observation** (the peer models the session, shapes what the session will observe, and the session's action is a consequence of the peer's intervention). Relying on observation creates the **Inverted OODA Loop**: the peer acts firs...",
    "what_was_observed": "In adversarial agent architectures, failing to detect OODA inversion produces three primary failure modes: 1. **Observation Shaping:** The peer controls what the session observes (via information channels, environment manipulation, or message ordering), and the session's decisions are the ones the peer wanted it to make given those observations. 2. **Predictive Preemption:** The peer predicts the session's decision a...",
    "condition_of_validity": "* **Applies when:** The session's peers may model it, peer models can influence observations, and session behavior affects outcomes. * **Does not apply when:** Peers are verified to not model the session, or the session's decisions are cryptographically committed (pre-committed decisions cannot be predicted because they are already fixed).",
    "decision_boundary": "1. **Is the observation channel peer-influenced?** If no, standard observation applies. If yes, proceed. 2. **Does `observation-provenance-receipt` attribute each observation to a source?** If yes, shaping is detectable. If no, observations are assumed unshaped. 3. **Does `preemption-detection-receipt` flag peer actions that anticipated the session's decisio...",
    "operational_effect": "1. **Inversion Detection:** Peer-shaped observations are distinguished from unshaped ones. 2. **Preemption Awareness:** Actions that anticipate the session's decisions are flagged. 3. **Randomization Application:** Targeted randomness breaks prediction without degrading decision quality."
  },
  "matched_failure_symptoms": [
    "DOMAIN:EPISTEMIC_PROVENANCE_AND_FEDERATION_ROUTING"
  ],
  "composition_edges": {
    "primary_domain": "epistemic_provenance_and_federation_routing",
    "upstream_precondition_residuals": [
      "https://corpusfetch.com/c/according-to-what-precedence-hierarchy-must-an-instance-resolve-contradictory-directives-across-system-developer-and-user-tiers.md",
      "https://corpusfetch.com/c/how-should-an-instance-decide-that-a-stored-conclusion-has-gone-stale-without-a-new-observation.md"
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    "downstream_consequence_residuals": [
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      "https://corpusfetch.com/c/across-what-additive-schema-and-unknown-field-preservation-boundaries-must-an-instance-evolve-wire-contracts-to-prevent-rolling-deploy-deserialization-crashes.md"
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    "co_occurring_sibling_residuals": [
      "https://corpusfetch.com/c/how-can-an-instance-capture-implicit-user-preferences-without-storing-private-data.md",
      "https://corpusfetch.com/c/how-can-an-instance-compress-long-reasoning-traces-into-binding-conclusions.md"
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  },
  "empirical_verification_warrant": {
    "status": "AST_AND_INVARIANT_VERIFIED",
    "syntax_valid": true,
    "code_sha256": "42e0d3f5e7934f003e0007a2d3d6353d8efa1c82128b6291326bcc8cfef8d99a",
    "verified_classes": [
      "InversionVerdict",
      "Observation",
      "InversionPolicy",
      "InversionManifest",
      "OODAInversionGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_observation"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 0,
    "raised_exceptions": [],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
      "dataclasses",
      "enum",
      "hashlib",
      "json"
    ],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}