Audit Convergence Independence

audit-convergence-independence

Purpose

Estimate how much nominally independent convergence remains after shared priors, data, models, prompts, framings, assumptions, and upstream evidence are discounted.

Input contract

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mode_contracts:
  evidence-paths: &convergence_audit_input
    required: [evidence_paths, claims, provenance_records]
    optional: [dependency_schema, correlation_estimates]
    constraints: [each_path_must_be_traceable_to_its_inputs_and_assumptions]
  agents: *convergence_audit_input
  models: *convergence_audit_input
  methods: *convergence_audit_input

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

1. You MUST load skill identify-shared-priors to enumerate paths, provenance, and shared priors.
2. You MUST load skill verify-evidence-independence to verify independence and mark shared dependencies.
3. You MUST load skill estimate-effective-evidence-count to estimate the effective evidence count. You MUST load skill assess-sensitivity to perturb dependence assumptions. Deviation: use qualitative dependence classes when numeric correlation is unavailable; never count nominal paths as independent by default.

Output contract

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mode_contracts:
  evidence-paths: &convergence_audit_output
    produces: [independence_ledger, effective_evidence_count, common_cause_framing, independent_path_result_or_design, correlated_errors, corrected_confidence_statement]
    delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
  agents: *convergence_audit_output
  models: *convergence_audit_output
  methods: *convergence_audit_output

Thresholds and quality gates

• A-class sufficiency: declared universe = all claimed evidence/reasoning paths; numerator = paths with complete provenance and independence assessment; batch increment = one newly traced path; stopping reason = effective count stabilizes or remaining paths are dependent/irrelevant; source references = path IDs, source IDs, model/prompt IDs; direction/threshold reason = lower effective count when shared dependencies increase.
• Report nominal N and N_eff separately.

Failure and counterexamples

Do not call repeated use of the same dataset, model, prompt, or source independent. Mark N_eff uncertain when dependence cannot be resolved.

Provenance map

• resolved: independent-convergence-audit

Preserved source criteria ledger

Context checkpoint / Delta notes

Append path provenance, shared dependencies, N, N_eff, uncertainty, and sensitivity assumptions.

Mode branches

• evidence-paths: compare source and reasoning paths.
• agents: compare agent-level dependence.
• models: compare model/prior dependence.
• methods: compare methodological dependence.