Correlation Structure Characterization

batch: Batch 04

canonical_family: Correlation, Coherence & Joint States

aliases:
Statistical Association Profiling
Dependence Structure Characterization
Co-Variation Mapping
Correlation Analysis
Correlation Structure Analysis

slug: correlation

slug: relation

slug: stationarity

slug: confounding

slug: stratification

slug: scale

slug: time

slug: bias

slug: coupling

slug: equivariance

slug: observability

slug: validation

slug: monitoring

slug: nonlinearity

components: Missingness and Measurement-Error Record

mechanisms: Outlier, Range, and Transformation Sensitivity Review

essence_one_line: Characterize how variables move together—by sign, strength, form, lag, condition, uncertainty, and stability—then explicitly constrain what that association may be used to claim or decide.

compression_statement: When variables are treated as independent or a single raw coefficient is overinterpreted, define a legitimate paired observation frame, select dependence measures suited to the data and use, inspect joint form, estimate uncertainty, test segments and lags, stress the relationship across time and preprocessing, label its causal status, and translate only the validated portion into bounded action or further study.

core_intuition: Co-variation is information about a relationship, not a complete explanation of it. A trustworthy correlation result preserves the conditions under which observations were paired, the shape and uncertainty of the dependence, the contexts where it changes, and the limits of inference. That makes correlation useful without allowing it to masquerade as causation, independence, or permanence.

canonical_formula: define use and scope → align paired observations → inspect joint form → select dependence measure → estimate uncertainty → condition, segment, and lag → test stability and artifacts → label causal status → permit bounded use or escalate

structural_problem: A decision system either assumes variables are independent when they systematically co-vary, or it notices an association and compresses it into an unqualified coefficient. The result can double-count duplicated evidence, overstate diversification, miss indirect signals, hide subgroup reversals, chase chance patterns, or turn descriptive association into an unsupported causal intervention.

trigger_conditions: Several measured variables may carry overlapping or linked information.

trigger_conditions: A prediction, monitoring, diversification, allocation, or diagnostic decision depends on whether observations move together.

trigger_conditions: A relationship appears strong but its form, lag, subgroup dependence, uncertainty, or stability is unknown.

trigger_conditions: A zero linear coefficient conflicts with visible nonlinear or segmented structure.

trigger_conditions: A proxy or early indicator is being considered for operational use.

trigger_conditions: A large search over variables has produced apparently interesting associations.

trigger_conditions: The environment or data pipeline may change the relationship after deployment.

symptoms: Supposedly independent signals fail together or add little information when combined.

symptoms: A pooled correlation disappears or reverses within subgroups.

symptoms: A strong coefficient is driven by one outlier, shared trend, narrow range, or common measurement source.

symptoms: Teams infer causal leverage from association alone.

symptoms: A model or dashboard keeps using a relationship after its sign or magnitude changes.

symptoms: Different dependence measures tell materially different stories with no selection rationale.

symptoms: Many correlations are reported without uncertainty, replication, or a search-space record.

symptoms: Operational thresholds are built from a relationship that was only exploratory.

root_tension: A useful summary must compress joint behavior enough to guide reasoning, yet preserve enough context, form, uncertainty, and instability to prevent the summary from becoming a false universal law or causal story.

anti_signatures: The relation is deterministic and already defined by a known rule rather than estimated from observations.

anti_signatures: The primary need is to define entities and relation types in an ontology; use Relation Mapping or Ontology Clarification.

anti_signatures: The decision requires a causal effect of intervention; use an experimental, quasi-experimental, confounder-control, or causal-mechanism archetype.

anti_signatures: Only the marginal distribution of one variable matters; use Variability Characterization.

anti_signatures: The task is specifically to evaluate risk-pooling benefit or diversification under dependence; use the accepted pooling specialization.

anti_signatures: There are no legitimately paired or comparable observations.

anti_signatures: The sample is too small or selected to support any defensible association claim.

structural_intervention: Build a governed association profile around the decision use: make the observation frame and pairing rule explicit, choose measures that fit the variables and plausible structure, quantify and stress-test the dependence, separate conditional from pooled patterns, attach uncertainty and temporal validity, enforce a causal-claim boundary, and monitor any relationship that becomes operational.

action_logic: State the intended use and the claim strength required for that use.

action_logic: Define variables, units, population, sampling frame, time horizon, and data-generation process.

action_logic: Specify how observations are paired or aligned across entities, times, and locations.

action_logic: Inspect joint distributions before selecting a summary statistic.

action_logic: Choose one or more dependence measures suited to scale, form, robustness, and interpretability.

action_logic: Estimate magnitude and uncertainty while documenting missingness, measurement error, range restriction, and transformations.

action_logic: Compare pooled, subgroup, conditional, and relevant lead–lag relationships.

action_logic: Test sensitivity to outliers, preprocessing, alternative measures, windows, and plausible null structures.

action_logic: Record the search space, prespecification status, multiplicity policy, and replication evidence.

action_logic: Assess stability across time, data sources, regimes, and operational changes.

action_logic: Label the result as descriptive, predictive, diagnostic, hypothesis-generating, or causally identified.

action_logic: Translate the validated result into a bounded use or route it to causal study, redesign, additional measurement, or non-use.

action_logic: Monitor drift and revoke operational use when the validity window or stability threshold is crossed.

decision_rules: Do not summarize a relationship with one coefficient until joint form and data quality have been inspected.

decision_rules: Prefer the simplest interpretable dependence measure that answers the declared use; add complexity only when it changes decisions.

decision_rules: Treat sign, magnitude, and uncertainty as context-bound properties, not universal constants.

decision_rules: Do not infer causation from correlation, temporal precedence, prediction accuracy, or partial correlation alone.

decision_rules: When pooled and segmented results disagree, investigate composition and aggregation before selecting one story.

decision_rules: When many relationships were searched, require correction, replication, or explicit exploratory labeling.

decision_rules: Do not operationalize a proxy correlation without drift monitoring and a fallback measurement path.

decision_rules: Use independent or external replication for high-consequence association claims.

decision_rules: If the relationship is unstable, use it only as a monitored hypothesis or local rule within a narrow validity window.

decision_rules: Escalate to causal design when the contemplated action depends on changing one variable to affect another.

target_invariants: Observation pairing remains legitimate and reconstructable.

target_invariants: Variable definitions, units, transformations, and scope remain visible.

target_invariants: Association is not silently upgraded into causal authority.

target_invariants: Uncertainty, missingness, measurement error, and multiplicity are not hidden.

target_invariants: Subgroup and temporal instability remain available to downstream users.

target_invariants: A relationship used operationally has a validity window, owner, drift monitor, and revocation path.

target_invariants: Protected or sensitive attributes are not exploited merely because they improve prediction.

target_invariants: The decision use remains within the evidence actually validated.

expected_outcomes: More accurate understanding of redundant, coupled, and informative variables.

expected_outcomes: Fewer false claims of independence, diversification, or causation.

expected_outcomes: Earlier detection of subgroup reversals, nonlinear form, lead–lag structure, and relationship drift.

expected_outcomes: Better proxy, monitoring, feature, sensor, and portfolio decisions.

expected_outcomes: Reduced double-counting of correlated evidence and common-source signals.

expected_outcomes: Clearer escalation from exploratory association to confirmatory or causal investigation.

expected_outcomes: More reproducible and auditable statistical reasoning across domains.

prose_essence:
Correlation Structure Characterization turns a loose observation that “these things move together” into a bounded, auditable statement about **how**, **where**, **when**, and **with what uncertainty** they co-vary. The archetype is useful because dependence changes what evidence means. Two indicators that rise together may not be two independent confirmations. Two assets that fail together may not provide diversification. A proxy may support early warning for a time, then become unreliable when the environment or measurement process changes.

The archetype deliberately separates three questions:

1. **Is there systematic association?**
2. **What is the form and validity of that association?**
3. **What may the association legitimately be used to do?**

It does not answer a fourth question—whether changing one variable will cause another to change—unless a separate causal design supplies that evidence.

prose_neighbor_distinctions:
**Relation Mapping** is the nearest semantic neighbor. It makes relations explicit, including type, direction, cardinality, evidence, scope, and ownership. This archetype differs by treating the relation as an empirically estimated joint distribution with uncertainty, functional form, conditionality, lag, stability, and statistical-use governance.

**Correlation Structure Analysis for Pooling Effectiveness** is a specialized accepted child-like pattern. It asks whether pooled risks are sufficiently independent and prescribes pool redesign. This archetype supplies the generic characterization layer that can support that specialization and many other uses.

**Correlated Proxy Monitoring** starts after a candidate correlation has been validated and uses it for indirect observation. This archetype decides whether the relationship is credible, bounded, and stable enough for that use.

**Variability Characterization** examines marginal spread, tails, subgroups, and noise. Correlation Structure Characterization examines joint dependence.

**Confounder Control** and **Causal Mechanism Mapping** protect or explain causal claims. This archetype can reveal why those are needed but is not a replacement for them.

**Cross-Impact Interaction Mapping** represents influence among scenario drivers. Correlation may inform it, but co-variation alone does not establish influence.

prose_cross_domain_examples:
### Finance

A portfolio contains many instruments, but several share the same macroeconomic and liquidity drivers. The team profiles ordinary, rolling, regime-specific, and tail dependence. It discovers that apparent diversification disappears under stress and routes the result to the specialized pooling/diversification design patterns.

### Public services

A city uses weather, events, transit, and call-volume indicators to forecast service demand. Relationships are estimated by neighborhood and season, validated on held-out periods, labeled predictive rather than causal, and monitored after policy or reporting changes.

### Physics and instrumentation

A laboratory sees several channels rise together. Joint diagnostics reveal that some co-variation comes from a shared electronics board while another pattern tracks a physical mode. The team redesigns sensing and preserves independent validation channels.

### Software operations

A service has dozens of telemetry streams. Correlation characterization helps identify duplicated signals and lead–lag indicators, but the team avoids declaring a causal dependency graph. Rolling monitors detect when a deployment changes the relationships.

### Health research

An observational dataset shows association between exposure and outcome. Analysts publish pooled and stratified results, uncertainty, missingness sensitivity, and a causal-claim boundary. The association becomes a hypothesis and design input for a subsequent study, not a treatment recommendation.

prose_non_examples:
- A typed ontology relation with no empirical co-variation estimate.
- A single coefficient copied from software output.
- A causal effect estimate from a randomized trial.
- A histogram or variance summary of one variable.
- A heatmap interpreted as a causal network.
- A proxy score deployed without validity, drift, fairness, or fallback controls.

prose_tradeoffs:
### Interpretability versus sensitivity

Simple coefficients are easy to communicate but can miss important structure. Flexible dependence measures detect more patterns but can be opaque, sample-hungry, and easy to overfit.

### Global summary versus local truth

A global coefficient is compact, but subgroup and regime detail may be the real decision-relevant structure. Too much segmentation, however, can reduce power and create a large search space.

### Exploratory breadth versus false discovery

Broad screening can reveal unknown relationships. Without search records, correction, replication, or explicit exploratory labels, it also manufactures convincing chance results.

### Stability versus adaptation

A fixed operational relationship is easy to govern, but the environment may change. Continuous adaptation can follow drift, yet can also chase noise or obscure accountability.

### Predictive usefulness versus legitimacy

A correlation can improve prediction while being unfair, privacy-invasive, manipulable, or causally irrelevant. Usefulness is one criterion, not the only one.

prose_invariants_to_preserve:
The most important invariant is **claim proportionality**: the authority of the conclusion must not exceed the evidence. Other invariants include legitimate pairing, visible scope, traceable transformations, uncertainty, subgroup visibility, temporal validity, and revocability.

When a relationship becomes part of a system, preserve a direct measurement fallback where feasible. A proxy that starts as a useful early indicator can become a manipulated target or drift away from the state it once represented.

prose_target_outcomes:
A successful application produces more than a number. It produces a reusable association record that tells downstream users:

- what was compared;
- how observations were aligned;
- which measures were used and why;
- what the joint form looks like;
- how uncertain and stable the result is;
- where it changes by segment or lag;
- whether it has replicated;
- what it may and may not be used to claim;
- who monitors it and when it expires.

short_definition: Characterizes how association changes after conditioning on a declared set of variables or within specified contexts.

distinctive_feature: The primary object is association conditional on an explicit information set rather than a global marginal relationship.

distinct_from_parent: Adds a governed conditioning set, collider/mediator warning, and interpretation of within-context association.

still_same_parent_because: It preserves the parent sequence of aligning observations, selecting a dependence measure, estimating uncertainty, testing stability, and bounding claims.

use_when: A pooled association may reflect composition or confounding.

use_when: The decision depends on association within rather than across groups.

use_when: Residual dependence after a model fit is operationally meaningful.

example_domains:
epidemiology
public policy
quality engineering
finance

example: Estimate the association between service access and outcomes within comparable regions rather than across all regions.

failure_modes:
conditioning on a collider
post-treatment adjustment
unstated model dependence

Lagged Correlation Characterization

short_definition: Characterizes co-variation across specified leads and lags for forecasting, delay diagnosis, or hypothesis generation.

distinctive_feature: The alignment dimension is an explicit lead–lag window rather than only same-time pairing.

distinct_from_parent: Adds temporal ordering, autocorrelation controls, and lag-selection safeguards.

still_same_parent_because: The result remains an association profile with uncertainty, stability, and causal limits.

source_primes: correlation time stationarity

use_when: A response may follow a driver with delay.

use_when: Contemporaneous correlation could hide lead–lag structure.

example_domains:
operations
economics
ecology
software telemetry

example: Test whether support-ticket volume leads infrastructure saturation by hours or days.

failure_modes:
shared trend
seasonality artifact
multiple-lag searching
reverse direction

Nonlinear Dependence Characterization

short_definition: Characterizes systematic dependence that is weak or invisible under a linear coefficient.

distinctive_feature: Functional form discovery and validation precede any single summary coefficient.

distinct_from_parent: Uses nonlinear or information-based measures and stronger overfitting controls.

still_same_parent_because: It serves the same general purpose of quantifying bounded co-variation without converting association into causation.

use_when: Scatterplots or domain theory suggest thresholds, curves, cycles, clusters, or other non-monotonic structure.

use_when: A zero linear correlation could coexist with strong dependence.

example_domains:
physics
biology
machine learning
public systems

example: Detect a U-shaped relationship between load and error rate that Pearson correlation averages toward zero.

failure_modes:
overfitting flexible measures
opaque dependence score
small-sample instability

Correlation Network Characterization

short_definition: Represents a multivariate dependence structure as a thresholded, weighted, or factor-adjusted network for exploration and monitoring.

distinctive_feature: The object is the global multivariate pattern rather than one variable pair.

distinct_from_parent: Adds graph construction, threshold governance, multiplicity control, and network-level interpretation.

still_same_parent_because: Edges still summarize empirically estimated association with uncertainty and causal limits.

source_primes: correlation network representation

use_when: Many variables co-vary and pairwise review is insufficient.

use_when: Clusters, hubs, redundancy, or regime shifts are decision-relevant.

example_domains:
sensor systems
finance
omics
organizational analytics

example: Group telemetry channels that share stable co-movement so alerts do not count duplicated signals as independent evidence.

failure_modes:
threshold arbitrariness
dense false-positive networks
visual reification
common-source duplication


Correspondence Validation

type: archetype

source: generated

batch: First Iteration/Batch 26

related_primes: continuity interoperability data_integrity invariance boundary relation schema ontology stationarity

slug: versioning

slug: continuity

slug: interoperability

slug: invariance

slug: boundary

slug: schema

slug: ontology

components:
Old Domain Boundary
Legacy Behavior Baseline
New Candidate Representation
Overlap Domain
Correspondence Criterion
Comparison Case Set
Behavior Comparison
Divergence Classification
Acceptance or Limitation Decision
Transition Record
Tolerance Band
Golden Case Library
Domain of Validity Statement
Exception Register
Migration Guardrail

mechanisms:
Backward Compatibility Test
Regression Test Suite
Golden Case Benchmark
Shadow Run or Parallel Run
Protocol Conformance Test
Model-Limit Validation
Migration Acceptance Test
Divergence Review Workflow

essence_one_line: Ensure a new model, theory, version, or system matches the old one within the old one’s valid domain before replacing it.

compression_statement: When a new system supersedes an old one, Correspondence Validation defines the old domain of validity, compares new and old behavior in the overlap domain, classifies divergences, and uses the result to accept, revise, limit, or reject replacement.

core_intuition: Improvement should not require forgetting what already worked. A replacement earns trust by extending or changing the system while still reproducing known-valid behavior where the old system had authority, or by explicitly documenting why a divergence is intentional and acceptable.

canonical_formula: old_domain_boundary + legacy_behavior_baseline + new_candidate_representation + overlap_domain + correspondence_criterion + comparison_case_set + behavior_comparison + divergence_classification + acceptance_or_limitation_decision -> safe_replacement_or_scoped_transition

structural_problem: A newer model, rule, version, workflow, policy, or system is proposed as a replacement for an older one, but it may fail in the very cases where the older system was already trusted.

Replacement or Upgrade Claim

description: A new version, theory, model, process, interface, or rule set is being introduced as an improvement over an existing one.

Old System Has Valid Domain

description: The predecessor is not perfect, but it has a known range of cases, users, outputs, or invariants where it was accepted as reliable enough.

Overlap Domain Exists

description: There are cases where both old and new systems can be meaningfully compared before full replacement or scope expansion.

Regression Cost Is Material

description: Losing old-domain behavior would create safety, trust, continuity, compliance, user, or operational costs.

Improvement Claim May Mask Breakage

description: The new system’s advantages in new cases may distract from failures in old cases that users still depend on.

name: New System Passes New Cases but Fails Old Ones

description: A replacement looks impressive on new capabilities but produces regressions in familiar or historically trusted scenarios.

Legacy Users Report Broken Expectations

description: People or systems that depended on old behavior experience surprises, broken workflows, changed semantics, or lost guarantees after the transition.

Migration Results Do Not Reconcile

description: Old and new reports, records, outputs, or decisions disagree without a clear explanation of which divergences are expected.

Known Cases Become Orphans

description: Canonical examples, edge cases, or old-domain reference cases are no longer handled well by the new representation.

Compatibility Claims Are Underspecified

description: Stakeholders say the new system is compatible, equivalent, improved, or migrated, but do not specify the domain, criterion, or accepted differences.

name: Replacement Debate Collapses into All-or-Nothing

description: Teams argue whether the new system is good or bad overall instead of distinguishing old-domain correspondence, new-domain extension, and intentional divergence.

root_tension: The system needs to change, extend, or modernize, but users also need continuity with behavior that was already known, trusted, or contractually promised. Correspondence Validation resolves this tension by separating legitimate extension from unacceptable regression inside the old domain of validity.

No Old Valid Domain

description: If the predecessor had no trusted domain and is being discarded entirely, the main work may be greenfield validation rather than correspondence validation.

Pure Source-of-Truth Conflict

description: If multiple current representations conflict and the problem is choosing authority, Source-of-Truth Assignment may be primary.

Data Corruption Without Replacement

description: If records are corrupted, stale, or inconsistent without an old-new transition, Data Integrity Preservation is usually more central.

Generalization Without Predecessor

description: If the question is whether a model works outside its training or origin setting but there is no predecessor to match, Generalization Validation or Stationarity Validation may be closer.

name: Continuous Evolution Without Acceptance Gate

description: If the work is managing gradual version history rather than checking a replacement against old-domain behavior, Versioned Evolution may be the better neighbor.

structural_intervention: Turn replacement into an old-new overlap test: state the predecessor’s valid domain, compare the candidate replacement inside that domain, classify divergences, and tie the result to adoption, revision, scoping, or rejection.

State the predecessor’s valid domain

description: Name where the old system, model, policy, interface, or rule was considered reliable, useful, or contractually promised. Separate known-valid behavior from known limitations.

name: Define the new candidate and intended extension

description: Clarify what the new system changes, improves, automates, generalizes, replaces, or newly supports so comparison does not become a vague preference contest.

Identify the overlap domain

description: Select cases where both old and new systems can be evaluated under comparable conditions, including ordinary cases, edge cases, exceptions, and high-stakes legacy expectations.

Set the correspondence criterion

description: Decide what agreement means: exact match, preserved invariant, bounded error, equivalent outcome, compatible interface, expected approximation, or documented intentional divergence.

Compare behavior, not labels

description: Run, simulate, review, or reason through old and new behavior on the same cases and compare outputs, semantics, side effects, failure behavior, user impact, and preserved constraints.

Classify divergences

description: Treat differences as acceptable extension, intended change, tolerable approximation, harmless presentation change, unresolved anomaly, unacceptable regression, or safety violation.

name: Bind validation to a transition decision

description: Use the divergence classification to accept, revise, limit, stage, run in parallel, preserve the old system for certain cases, or reject the replacement.

Record scope and exceptions

description: Document where correspondence was demonstrated, what divergences remain, and where the new system should not be treated as equivalent to the old one.

rule: Do not retire old behavior until old-domain correspondence is established or intentionally waived by accountable review.

rationale: Replacement without overlap validation creates hidden regressions and breaks dependencies that the old system still served.

rule: Treat exact matching as required for safety, identity, rights, contractual, or interoperability invariants unless an explicit exception is approved.

rationale: Some old-domain properties are obligations rather than preferences.

rule: Treat bounded divergence as acceptable only when the tolerance band, use case, and affected stakeholders are explicit.

rationale: A new system may legitimately approximate, improve, or change behavior, but the difference must be bounded and explainable.

rule: Passing old-domain tests permits replacement only inside the validated domain, not unlimited expansion into new domains.

rationale: Correspondence proves continuity where tested; it does not prove universal validity.

rule: When divergence classification is disputed, preserve parallel operation, fallback, or limited rollout until the dispute is resolved.

rationale: A contested mismatch is a governance problem, not a reason to silently proceed.

Old-Domain Safety Preserved

description: The new system must not violate safety, rights, compliance, or harm-prevention constraints that the old system protected within its valid domain.

Known-Valid Behavior Recovered

description: Within the overlap domain, the new system recovers, approximates, or intentionally modifies old behavior according to the correspondence criterion.

Compatibility Promises Honored

description: Existing users, dependencies, interfaces, contracts, or downstream systems receive the continuity that was promised or are given documented migration limits.

Scope of Equivalence Declared

description: Stakeholders know where the new system is equivalent, where it is better, where it differs, and where it is unvalidated.

Divergence Accountability Preserved

description: Differences between old and new behavior are traceable to a classification and accountable decision rather than hidden as implementation detail.

Safer Replacement Decisions

description: Upgrades, migrations, model changes, and policy transitions become less likely to produce hidden regressions.

name: Trusted Continuity with Room for Extension

description: Stakeholders can accept useful change because old-domain behavior has been protected or divergences have been explained.

Clearer Migration Scope

description: The organization knows where replacement is validated, where old systems must remain available, and where additional testing is needed.

Better Regression Detection

description: Loss of previously working behavior becomes visible before it reaches users or downstream systems.

Documented Intentional Change

description: Legitimate improvements that differ from the old system are recorded as intentional changes rather than unexplained inconsistencies.

prose_essence:
Correspondence Validation is the intervention for replacing something without accidentally discarding what already worked. It asks: before a new model, system, rule, version, or theory replaces an old one, can the new candidate recover the old system's valid behavior in the domain where the old system was trusted?

The archetype is not nostalgia for legacy behavior. It allows real improvement, reform, and extension. Its key discipline is to separate three things that are often mixed together: behavior the new system must preserve, behavior it may intentionally change, and behavior that has not yet been validated. A replacement becomes safer when these distinctions are explicit before cutover.

prose_when_to_use:
Use Correspondence Validation when a new version, model, process, policy, interface, or system is being introduced as a replacement or successor, and the older version still has a known valid domain. It is especially useful when existing users, downstream systems, legal obligations, safety guarantees, reports, or institutional expectations depend on old behavior.

It is also useful when a broader model claims to generalize an older one. The new model should be checked in the old model's limiting cases or known-success cases before its new reach is trusted. In operational settings, this often appears as parallel runs, regression suites, compatibility tests, migration acceptance tests, and exception reviews.

Do not use this archetype as a generic validation label. The defining feature is old-new overlap: there is a predecessor, a candidate replacement, and a domain in which both can be compared.

prose_structural_problem:
The structural problem is replacement under incomplete continuity. A new system may be better overall, more modern, more general, or more efficient, yet still fail in cases where the old system was reliable. Because attention is often drawn toward the new capability, old-domain regressions may remain hidden until users, dependent systems, or high-stakes cases encounter them.

This problem is common when old systems are messy but valuable. They may contain tacit knowledge, edge-case handling, compatibility promises, historical conventions, or user expectations that were never fully documented. When the replacement is evaluated only on new goals, the hidden value of old behavior can disappear.

prose_intervention_logic:
The intervention turns replacement into an overlap-domain test. First, state where the old system was valid. Then define the new candidate and identify cases where both old and new can be compared. Next, set a correspondence criterion: exact equality, bounded difference, preserved safety property, compatible interface, equivalent outcome, accepted approximation, or documented intentional divergence.

The comparison should examine behavior rather than labels. It may compare outputs, decisions, side effects, semantics, failure behavior, user consequences, preserved constraints, or downstream compatibility. Mismatches are then classified. Some are improvements, some are harmless changes, some are expected differences, some are unacceptable regressions, and some require more review.

The final step is to bind the evidence to a transition decision. The new system may be accepted, revised, limited to a validated scope, staged, run in parallel, or rejected. Correspondence Validation fails when it notices differences but does not govern adoption.

prose_key_components:
- **Old Domain Boundary** (`old_domain_boundary`): defines where the predecessor was considered valid. This prevents teams from comparing against an undefined past or claiming old-domain success after the fact.
- **Legacy Behavior Baseline** (`legacy_behavior_baseline`): captures what the old system actually did well enough to preserve, such as outputs, invariants, service guarantees, policy outcomes, or interface behavior.
- **New Candidate Representation** (`new_candidate_representation`): names the replacement being evaluated. The archetype applies to a specific successor, not to an abstract desire for improvement.
- **Overlap Domain** (`overlap_domain`): identifies the cases where old and new systems can be meaningfully compared. This is the testing ground for continuity.
- **Correspondence Criterion** (`correspondence_criterion`): states what counts as agreement. Some domains require exact preservation; others allow bounded or intentional difference.
- **Comparison Case Set** (`comparison_case_set`): supplies ordinary, canonical, edge, historical, adversarial, and high-stakes cases for comparison. A weak case set produces false confidence.
- **Behavior Comparison** (`behavior_comparison`): compares old and new behavior under the same cases. It should include semantics and consequences, not only numerical output.
- **Divergence Classification** (`divergence_classification`): classifies differences as acceptable, intentional, harmless, unresolved, or unacceptable. This keeps improvement and regression from being confused.
- **Acceptance or Limitation Decision** (`acceptance_or_limitation_decision`): determines whether replacement proceeds, pauses, narrows, or is rejected. This component turns validation into governance.
- **Transition Record** (`transition_record`): records evidence, exceptions, limits, and accepted divergences so later users know where equivalence was demonstrated.

Optional components include a **Tolerance Band** for acceptable approximate agreement, a **Golden Case Library** for canonical reference cases, a **Domain of Validity Statement** for declaring tested scope, an **Exception Register** for known divergences, and a **Migration Guardrail** for constraining rollout until correspondence evidence is adequate.

prose_common_mechanisms:
**Backward compatibility tests** implement the archetype in versioned systems. They check whether existing clients, workflows, contracts, and interfaces still behave as promised. They are mechanisms because they test one compatibility surface; they do not by themselves define the old domain, classify divergences, or decide migration scope.

**Regression test suites** encode previously working behavior as repeatable checks. They are excellent for executable systems, but they can become too narrow if they only preserve easy old cases. In this archetype, regression tests should be tied to the old-domain boundary and correspondence criterion.

**Golden case benchmarks** use trusted examples as reference anchors. They are useful when the old behavior cannot be fully formalized. Their risk is that they may preserve a small symbolic sample while missing the broader old-domain obligation.

**Shadow runs or parallel runs** operate old and new systems side by side before full replacement. They reveal differences under realistic conditions and are especially valuable for migrations, policy changes, data pipelines, and operational systems.

**Protocol conformance tests** verify that new implementations still satisfy old interface or format obligations. These implement the interoperability side of Correspondence Validation but should not be confused with Data Integrity Preservation or Source-of-Truth Assignment.

**Model-limit validation** checks whether a broader model recovers an older model under the older model's validity conditions. This is the closest transferable form of the correspondence-principle inspiration, but the archetype should remain an intervention pattern rather than a physics metaphor.

**Migration acceptance tests** and **divergence review workflows** connect comparison evidence to decisions. They keep validation from becoming ceremonial by deciding whether to proceed, limit, revise, or preserve a fallback.

prose_failure_modes:
A common failure mode is narrow happy-path validation. The new system passes easy cases while failing boundary cases, legacy exceptions, or high-stakes scenarios. The mitigation is to include canonical, edge, adversarial, historical, and stakeholder-surfaced cases.

Another failure mode is compatibility theater. Tests exist, but the results do not control rollout. Correspondence Validation requires a decision rule: proceed, limit, revise, pause, or reject.

A third failure mode is frozen legacy behavior. The team preserves old behavior even when it was harmful or obsolete. The mitigation is to classify intentional reform separately from accidental regression.

A fourth failure mode is overgeneralized success. Passing old-domain tests is treated as proof that the new system works in new domains. The transition record should state the validated scope and require separate validation for extension.

prose_neighbor_distinctions:
Correspondence Validation is distinct from **Versioned Evolution**: Versioned Evolution manages change across versions over time, while Correspondence Validation checks whether a particular replacement preserves old-domain behavior before adoption.

It is distinct from **Compatibility Management**: Compatibility Management may govern ongoing dependency and interoperability promises, while Correspondence Validation is the old-new overlap validation step. Because `compatibility_management` is not a canonical prime in the current prime list, this draft treats it as a neighbor and promotion candidate, not as a source prime.

It is distinct from **Source-of-Truth Assignment**: Source-of-Truth Assignment chooses authority among conflicting representations. Correspondence Validation compares predecessor and replacement behavior.

It is distinct from **Data Integrity Preservation**: Data integrity may be one invariant during migration, but Correspondence Validation asks whether the new system corresponds to the old system in the old valid domain.

It is distinct from **Stationarity Validation**: Stationarity Validation checks whether past patterns remain predictive. Correspondence Validation checks whether a new system recovers old valid behavior.

prose_cross_domain_examples:
In software, a new API version is validated against old clients and promised response semantics before the old API is retired. In data migration, old and new reporting systems run in parallel until key counts, definitions, cohorts, and exception cases correspond.

In policy, a new eligibility rule is run against historical cases so policymakers can distinguish intended reform from accidental exclusion. In scientific or analytic modeling, a new model is required to reproduce the older model's trusted results under the older model's limiting assumptions.

In product redesign, a new user flow is checked against previously supported accessibility, disclosure, payment, or safety behaviors. In operations, a new triage process is compared against past incidents to ensure that high-risk cases still escalate reliably.

prose_non_examples:
A brand-new system with no predecessor is not primarily a correspondence-validation case. It may need initial validation, risk assessment, usability testing, or safety review, but there is no old domain to match.

A corrupted database is not correspondence validation unless the corruption arises during old-new replacement. The primary pattern is Data Integrity Preservation or reconciliation.

A model that no longer works because the world changed is usually Stationarity Validation unless the problem is comparison with a replacement model.

A deliberately discontinuous reform may use correspondence validation to document what is being broken, but the target is not preservation of old-domain behavior.

prose_tradeoffs:
The central tradeoff is continuity versus speed. Strong correspondence validation can slow upgrades, migrations, and model replacement. But skipping it can cause broken dependencies, lost rights, incorrect reports, unsafe decisions, or distrust.

A second tradeoff is exact matching versus improvement. If every divergence is treated as a failure, obsolete legacy behavior becomes frozen. If too many divergences are waved through as improvement, the replacement may destroy old-domain value. The solution is explicit divergence classification.

A third tradeoff is coverage versus cost. More cases, longer parallel runs, and deeper semantic checks provide better assurance but require more time and expertise.

prose_invariants_to_preserve:
The primary invariant is that known-valid old-domain behavior should not disappear unnoticed. In high-stakes settings, safety constraints, rights, compatibility promises, access guarantees, identity semantics, data definitions, appeal paths, and compliance obligations may need exact preservation unless a responsible process explicitly changes them.

Another invariant is scope honesty. Passing correspondence tests in the overlap domain does not prove the new system is valid everywhere. The draft should preserve the distinction between validated equivalence, intentional divergence, and untested extension.

prose_target_outcomes:
A good Correspondence Validation intervention produces safer replacement decisions, fewer hidden regressions, clearer migration scope, stronger trust in upgrades, and better documentation of intentional changes. It allows useful innovation without forcing users to rediscover every old dependency after the cutover.

The target is not perfect sameness. The target is accountable continuity: the new system preserves what must be preserved, changes what is intentionally changed, and clearly limits what has not yet been validated.

prose_parameter_dimensions:
Important tuning dimensions include how broadly the old domain is defined, how strict the correspondence criterion is, how much tolerance is allowed, how representative the comparison case set is, and how much divergence can be accepted before rollout is limited.

Other parameters include the length of the parallel-run window, the severity classes for divergences, the retirement schedule for old behavior, the threshold for human review, the level of stakeholder involvement, and whether validation must cover semantics, downstream effects, and failure behavior in addition to direct outputs.

The stricter the tuning, the more continuity and safety the intervention preserves. The looser the tuning, the faster replacement can proceed but the more hidden regression risk remains.

prose_variants_and_near_names:
**Backward Compatibility Validation** is the versioned-systems variant. It protects existing clients, contracts, protocols, and workflows during upgrades.

**Model-Limit Correspondence** is the model-transition variant. It checks whether a newer or broader model recovers the older model under limiting conditions or within the old domain of validity.

**Migration Correspondence Validation** is the cutover variant. It uses parallel runs, acceptance gates, and exception registers to protect continuity during data, process, policy, or platform migration.

Near names include correspondence check, old-new overlap validation, known-domain validation, replacement continuity validation, backward compatibility, regression testing, and compatibility testing. Regression tests and compatibility tests should remain mechanisms unless the surrounding governance pattern is explicitly drafted.

Backward Compatibility Validation

short_definition: Validate that a new version preserves existing behavior, interfaces, or user expectations where old compatibility is still promised.

distinctive_feature: The correspondence criterion is framed around continuity for existing dependents rather than broad theory or model replacement.

distinct_from_parent: The parent applies to any old-new replacement in an overlap domain; this variant specializes it for versioned continuity and backward compatibility obligations.

still_same_parent_because: It still requires old-domain boundaries, overlap cases, behavior comparison, divergence classification, and a migration or limitation decision.

condition: A software, protocol, process, policy, or contract version changes while existing users or dependent systems must keep working.

condition: The old behavior remains valid for some legacy cases even though the new version adds features or changes internals.

condition: Breaking compatibility would impose hidden migration costs or safety risks.

role: Names supported legacy cases and dependents.

role: Defines acceptable compatibility.

role: Blocks rollout when compatibility fails.

role: Checks promised legacy behavior.

role: Checks interface or protocol obligations.

role: Detects unintended loss of working behavior.

domain: software

example: A new API version is tested against legacy clients before the old version is retired.

domain: operations

example: A revised workflow must still satisfy existing compliance checkpoints during migration.

failure_mode: Compatibility theater

description: Tests cover only easy cases while real dependent workflows break.

failure_mode: Frozen legacy burden

description: Compatibility obligations are never retired, preventing useful change.

Model-Limit Correspondence

variant_type: subtype

short_definition: Validate that a newer or broader model reproduces the older model under the older model’s domain of validity or limiting conditions.

distinctive_feature: The overlap domain is expressed as a limit condition or domain of validity rather than as user-facing version compatibility.

distinct_from_parent: The parent includes many replacement settings; this variant emphasizes model generalization, limiting cases, approximation, and theory-transition style continuity.

still_same_parent_because: It still compares old and new behavior within an overlap domain and treats divergence as evidence for acceptance, revision, or scope limitation.

condition: A new analytic, scientific, simulation, financial, policy, or operational model claims to generalize an older trusted model.

condition: Stakeholders need confidence that expanded capability did not discard known-valid behavior.

condition: The old model is still practically useful within a known range even if it is not universally correct.

role: Names old model validity conditions.

role: Defines acceptable approximation.

role: Records where correspondence was demonstrated.

role: Tests limiting-case agreement.

role: Anchors comparison in canonical cases.

domain: simulation

example: A detailed simulator is checked against a simpler trusted calculation in simple cases where the old calculation should still hold.

domain: policy

example: A new allocation model is checked against known historical cases before it is used for broader optimization.

failure_mode: Limit condition ambiguity

description: The old domain is vague, so failures can be dismissed as outside scope after the fact.

failure_mode: Overgeneralized success

description: Passing old-domain tests is misread as proof that the new model works in new domains.

Migration Correspondence Validation

short_definition: Validate old-new continuity during a migration so users, data, obligations, and operational behavior survive the transition.

distinctive_feature: The main risk is transition loss during cutover, not merely whether a new model is theoretically compatible.

distinct_from_parent: The parent covers replacement validation generally; this variant focuses on migration timing, cutover decisions, staged exposure, and continuity of service or records.

still_same_parent_because: It still validates new behavior against old-domain expectations in the overlap window and uses divergence findings to govern replacement.

source_primes: versioning continuity data_integrity

condition: A process, platform, dataset, policy, or organizational system is being moved from an old representation to a new one.

condition: The migration may create data loss, broken workflows, changed semantics, or unanticipated regressions.

condition: Parallel operation, staged rollout, or acceptance gates are feasible before full cutover.

role: Covers migrated records or workflows.

role: Documents cutover evidence and exceptions.

role: Determines proceed, pause, revise, or roll back.

role: Compares old and new behavior before cutover.

role: Gates migration on accepted correspondence.

role: Classifies mismatches before final adoption.

domain: data migration

example: Old and new reporting systems run in parallel until record counts, definitions, and key outputs correspond.

domain: public policy

example: A replacement eligibility rule is tested against prior cases before citizens are moved to the new rule set.

failure_mode: Parallel-run complacency

description: Teams compare only high-level aggregates while individual cases break.

failure_mode: Cutover pressure

description: Schedule pressure overrides unresolved divergence review.

near_names:
Correspondence Check
Old-New Model Matching
Known-Case Validation
Backward Compatibility
Regression Test
Compatibility Test


name: Correspondence Violation Detection and Theory Refinement

batch: Second Iteration/Batch 04

slug: refinement

slug: uncertainty

slug: approximation

slug: robustness

slug: calibration

slug: feedback

slug: iteration

slug: commensurability

components:
Expected Correspondence Map
Overlap or Limit Regime Set
Measurement Translation Bridge
Violation Detection Protocol
Violation Triage Filter
Divergence Signature
Boundary Condition Extractor
Refinement Hypothesis Generator
Regression-Guarded Update
Canonical Case Library
Uncertainty and Error Budget
Assumption Ledger
Versioned Theory Change Log
Adjudication Panel or Review Role

mechanisms:
Limiting-Case Test Suite
Overlap-Regime Benchmark Table
Residual Divergence Map
Assumption Audit Worksheet
Parameter Sweep Matrix
Anomaly Triage Board
Boundary Condition Memo
Regression Correspondence Harness
Failure Mode Annotation Card
Expert Adjudication Panel

essence_one_line: Use failures of expected correspondence as high-value signals for refining theory rather than as noise, embarrassment, or simple rejection.

compression_statement: A failure-driven refinement pattern that defines where a new or revised theory should agree with an older, simpler, benchmark, or known-valid formulation; detects and localizes violations of that expected agreement; triages whether the break is caused by measurement, translation, implementation, regime, or assumption failure; and converts the resulting diagnosis into revised boundary conditions, mechanisms, parameters, or theory structure while preserving known valid regimes.

core_intuition: A theory is most informative where it almost works but not quite. Expected correspondence gives a disciplined baseline for what should be recovered. When that recovery fails, the failure is not automatically a defect to hide or a reason to discard the theory. It is a clue about missing assumptions, shifted regimes, boundary conditions, or deeper structure. Refinement works when the clue is diagnosed carefully and the repair is checked against both the old success cases and the new violation.

canonical_formula: Refinement signal = expected correspondence − observed agreement; useful refinement = localized violation + error triage + boundary/mechanism hypothesis + regression-guarded repair.

structural_problem: A new theory, model, method, instrument, process, classification, or explanatory frame is expected to recover an older or simpler formulation in known regimes, but discrepancies appear. Without a disciplined process, the discrepancy may be ignored as noise, misread as total refutation, overfit away, or repaired in a way that breaks cases that were already understood. The system lacks a bridge from correspondence failure to precise refinement.

trigger_conditions: A new model or theory should reproduce a trusted older theory in a limiting case, overlap domain, benchmark case, or known-valid regime.

trigger_conditions: Two versions, instruments, taxonomies, simulations, or operational processes disagree where continuity was expected.

trigger_conditions: A model works broadly but fails repeatedly for a specific population, parameter range, scale, context, or boundary condition.

trigger_conditions: A benchmark, regression test, field observation, or implementation produces a result that violates expected agreement.

trigger_conditions: Decision-makers are tempted either to dismiss the anomaly as noise or to abandon the theory without diagnosing why it failed.

trigger_conditions: A revision must preserve known successes while addressing the violation.

trigger_conditions: The practical value lies in identifying where and why correspondence breaks, not merely declaring pass or fail.

symptoms: Apparent anomalies are logged but never converted into theory changes.

symptoms: A new model passes aggregate metrics while failing in known simple cases.

symptoms: A repair fixes one anomaly but breaks old benchmark cases.

symptoms: Disagreements are blamed on implementation, data, or domain experts without a triage protocol.

symptoms: Different teams argue over whether a discrepancy is measurement error, translation mismatch, boundary condition, or theoretical failure.

symptoms: A theory keeps expanding through ad hoc exceptions rather than explicit scope refinement.

symptoms: Known valid regimes disappear from memory when a new formulation is adopted.

symptoms: A model is rejected too early because a correspondence expectation was stated too loosely or tested with mismatched definitions.

root_tension: The correspondence principle demands continuity with known valid regimes, while refinement requires taking failures seriously enough to change the theory. The danger is either over-preserving old theory by explaining away genuine violations or over-revising the new theory by reacting to noise, translation error, or implementation defects.

anti_signatures: The task is generic benchmark comparison with no expectation that one formulation should recover another in an overlap or limit regime.

anti_signatures: The problem is open-ended counterexample search against a universal claim, rather than diagnosing expected-correspondence failure.

anti_signatures: The discrepancy is already known to be a simple data-entry, unit-conversion, or implementation defect and no theory or boundary refinement is needed.

anti_signatures: The old theory is not valid in any specified regime, so there is no meaningful correspondence expectation.

anti_signatures: The desired action is to optimize performance only, not to refine explanatory scope, assumptions, mechanisms, or model structure.

anti_signatures: A one-off anomaly has no pattern, no decision consequence, and no plausible link to a missing assumption or boundary condition.

structural_intervention: Install a correspondence-violation refinement loop: state expected correspondence regimes, build measurement and translation bridges, detect and localize violations, triage error versus genuine boundary failure, generate refinement hypotheses, update scope or theory structure, and regression-test the repair against both known valid cases and the new violation.

action_logic: Name the old, simpler, benchmark, or trusted formulation and the new or revised formulation being compared.

action_logic: Define the exact regimes where correspondence should hold: limits, overlap domains, canonical cases, shared populations, parameter ranges, or operational equivalences.

action_logic: Specify the measurement, translation, and equivalence criteria that make agreement meaningful.

action_logic: Run correspondence checks and record observed divergences with tolerances and uncertainty bounds.

action_logic: Map the divergence pattern across variables, scale, time, population, context, or parameter space.

action_logic: Triage the violation: noise, finite-sample error, instrument mismatch, unit or schema mismatch, implementation defect, regime change, hidden variable, boundary condition, or theory failure.

action_logic: Convert stable or meaningful violations into candidate refinements: changed assumptions, added mechanism, revised parameterization, narrower scope, new boundary condition, or replacement theory segment.

action_logic: Test candidate refinements against both the failing cases and known-valid correspondence cases.

action_logic: Record the refined theory, its domain of validity, unresolved violations, and conditions that should trigger renewed review.

decision_rules: Do not treat correspondence failure as theory failure until translation, measurement, implementation, and tolerance issues have been checked.

decision_rules: Do not dismiss a repeated or structured violation as noise when it clusters around a regime, subgroup, scale, or parameter range.

decision_rules: A valid refinement should explain the violation while preserving correspondence in regimes where the prior theory is known to work.

decision_rules: Prefer boundary-condition refinement when the old theory still works in a narrowed domain.

decision_rules: Prefer mechanism or structure revision when violations reveal a missing causal, functional, or interaction process.

decision_rules: Require explicit uncertainty and tolerance rules before classifying small divergences as failures.

decision_rules: Use benchmark and limiting cases as regression guards after every major refinement.

decision_rules: Escalate to expert adjudication when violation interpretation has safety, regulatory, ethical, or high-cost consequences.

target_invariants: Known valid regimes remain protected unless evidence shows they were mischaracterized.

target_invariants: Expected correspondence is stated before interpreting failure.

target_invariants: Measurement and translation assumptions are auditable.

target_invariants: Violations are localized rather than averaged away.

target_invariants: Refinements are traceable to diagnosed failure patterns.

target_invariants: Ad hoc patches do not accumulate without scope or mechanism explanation.

target_invariants: Unresolved violations remain visible rather than being hidden in aggregate performance.

expected_outcomes: Clearer theory boundaries and domain-of-validity statements.

expected_outcomes: More reliable model replacement and version transitions.

expected_outcomes: Earlier discovery of hidden assumptions, missing variables, or regime changes.

expected_outcomes: Reduced false confidence from aggregate agreement.

expected_outcomes: Reduced false rejection from poorly specified correspondence expectations.

expected_outcomes: A reusable record of why a theory, model, or process was refined.

expected_outcomes: Stronger continuity between old valid knowledge and new explanatory power.

prose_tradeoffs:
The main tradeoff is between sensitivity and false alarm. Tight thresholds detect subtle failures but can overreact to noise. Loose thresholds avoid false alarms but may hide boundary failures. Another tradeoff is continuity versus novelty: too much attachment to the old formulation can suppress genuine improvement, while too little continuity can discard hard-won knowledge.

Benchmark discipline also has a cost. Benchmark cases protect correspondence, but models can overfit to them. A healthy implementation uses benchmarks as regression guards while continuing to search for new violation patterns.

Limiting-Case Violation Review

short_definition: A correspondence-violation variant that tests whether a new model reduces to the accepted older model in a limiting regime where that older model is known to work.

distinctive_feature: The violation is detected through an expected mathematical, physical, operational, or conceptual limit relation.

distinct_from_parent: The parent covers all expected-correspondence failures; this variant specifically checks limiting reductions.

still_same_parent_because: A limiting-case failure is still used as a disciplined signal for theory or model refinement.

use_when: A new theory, simulation, metric, or model should recover a trusted approximation under low-speed, low-energy, small-scale, equilibrium, or otherwise simple conditions.

use_when: Failure in the limit could mean implementation error, missing term, domain mismatch, or incorrect theory.

use_when: The older model is not globally sufficient but remains valid in a specified regime.

example_domains:
physics
simulation validation
machine learning model compression
policy model comparison

domain: Physics

example: Check whether a relativistic model recovers classical mechanics at low velocity.

domain: Simulation

example: Verify that a high-fidelity simulator reproduces a closed-form approximation when assumptions match.

failure_modes: Treating any limit failure as proof the new theory is wrong before ruling out translation or implementation error.

failure_modes: Forcing correspondence in a regime where the old model was never valid.

Overlap-Regime Divergence Mapping

short_definition: A variant that maps where two theories, models, instruments, processes, or classifications disagree inside a shared domain of expected agreement.

distinctive_feature: It converts scattered disagreements into a structured map of where correspondence fails.

distinct_from_parent: The parent includes diagnosis and repair; this variant emphasizes spatial, parametric, temporal, or categorical localization of divergence.

still_same_parent_because: The divergence map feeds refinement of theory, assumptions, or boundaries.

use_when: Two systems claim to describe or handle the same cases for at least part of their domain.

use_when: The disagreement surface is more informative than a single pass/fail result.

use_when: The goal is to locate boundary conditions, missing variables, or mismatched definitions.

example_domains:
forecasting
diagnostics
classification systems
operations research

domain: Forecasting

example: Map where a new demand model diverges from a trusted historical baseline across season, geography, and product class.

domain: Classification

example: Find category edges where old and new taxonomies assign the same cases differently.

failure_modes: Averaging away local disagreement and missing the regime where refinement is needed.

Benchmark-Failure-Driven Refinement

short_definition: A variant that uses trusted benchmark or canonical cases where correspondence is expected to fail-fast, diagnose assumptions, and refine the theory or model.

distinctive_feature: The failure signal comes from curated benchmark cases rather than open-ended observation alone.

distinct_from_parent: The parent can use any correspondence expectation; this variant relies on a maintained test suite or benchmark set.

still_same_parent_because: Benchmark failure is still interpreted as correspondence violation and used to refine assumptions or boundaries.

source_primes: refinement validation

use_when: There is a benchmark library of cases with known expected behavior.

use_when: A new model must pass canonical checks before being trusted in expanded domains.

use_when: Unexpected benchmark failure is treated as a source of refinement rather than only a rejection.

example_domains:
software testing
clinical model validation
AI evaluation
engineering simulation

domain: AI evaluation

example: Use canonical cases where a model should match expert labels, then inspect systematic failures to refine data or model assumptions.

domain: Engineering

example: Run a finite-element model on benchmark problems with known analytical results before trusting novel configurations.

failure_modes: Overfitting to benchmarks while missing unrepresented regimes.

failure_modes: Treating benchmark pass rates as sufficient refinement without diagnosing failure patterns.

short_definition: A variant that treats repeated correspondence failures not as noise to discard but as clues for specifying where a theory stops applying.

distinctive_feature: The refinement outcome is an explicit boundary condition rather than a wholly new model.

distinct_from_parent: The parent covers all repair responses; this variant specializes in domain-boundary refinement.

still_same_parent_because: Boundary extraction follows from correspondence-violation diagnosis.

use_when: Observed violations cluster around a parameter range, population, context, instrument, or scale.

use_when: The original theory works in many cases but fails systematically in a subset.

use_when: The desired output is a clearer boundary condition or corrected domain statement.

example_domains:
medicine
organizational practice
education research
materials engineering

domain: Medicine

example: A treatment model works for most patients but fails for a subgroup, leading to a boundary condition around comorbidity.

domain: Education

example: A learning intervention matches expected gains except for multilingual learners, requiring a scope refinement.

failure_modes: Explaining away violations as boundary conditions too quickly instead of investigating whether the core theory is wrong.

Translation-Bridge Error Detection

short_definition: A variant that checks whether apparent correspondence violation is caused by mismatched units, definitions, instruments, encodings, or transformation layers rather than true theory failure.

distinctive_feature: It first audits the bridge between representations before modifying the theory.

distinct_from_parent: The parent includes triage; this variant emphasizes representation and measurement translation as the suspected failure source.

still_same_parent_because: Translation errors are part of disciplined correspondence-violation diagnosis.

use_when: Two regimes use different units, terms, data schemas, instruments, taxonomies, or operational definitions.

use_when: A violation appears after migration, model replacement, cross-disciplinary translation, or scale change.

use_when: Premature theory revision would be risky if the mismatch is only representational.

example_domains:
data migration
interdisciplinary modeling
measurement systems
regulatory reporting

domain: Data migration

example: A new reporting system appears to disagree with old reports until a currency, time-zone, or denominator mismatch is found.

domain: Measurement

example: A device comparison fails because one instrument measures peak value while another measures average value.

failure_modes: Correctly identifying a translation error but failing to record the bridge assumption for future use.

near_names:
Correspondence Failure Analysis
Model Limit Failure Refinement
Theory Boundary Anomaly Analysis
Correspondence Breakpoint Mapping


name: Cost-Asymmetric Preference Revelation Design

batch: Batch 11

canonical_family: Public-Private Belief Divergence

aliases: Expression-Cost Calibration for Preferences

aliases: Public-Private Preference Divergence Correction

alias_records: Expression-Cost Calibration for Preferences

alias_records: Public-Private Preference Divergence Correction

slug: conformity

slug: signaling

slug: observability

slug: accountability

slug: transparency

slug: trust

slug: threshold

slug: groupthink

slug: legitimacy

components:
Expression Cost Map
Private Preference Snapshot
Public Expression Trace
Reprisal and Reward Boundary
Protected Revelation Channel
Truthful Expression Incentive Alignment
Aggregate Divergence Report
Public Interpretation Guardrail
Recalibrated Expression Path
Retaliation Monitor and Reopening Rule
Segmented Risk Model
Counter-Signal Validation Layer
Non-Disclosure Legitimacy Exception

essence_one_line: Reveal held preferences without forcing dangerous exposure by mapping expression costs, protecting low-risk signal channels, and translating aggregate private information into safer public choice and norm updates.

compression_statement: Preference falsification occurs when the preference that is cheapest to express publicly differs from the preference an actor privately holds. Cost-Asymmetric Preference Revelation Design treats that divergence as a system-design problem: it maps the costs and rewards attached to expression, elicits held preferences through protected or incentive-compatible channels, compares them with visible expression, and updates procedures, norms, or decisions so public signal no longer systematically overrepresents the safest-to-voice position.

core_intuition: People often do not lie because they love deception; they adapt to the payoff surface around speech. If one view brings safety, status, access, employment, belonging, or avoidance of harassment while another brings punishment, then public expression becomes a distorted measurement instrument. The remedy is not simply exhorting courage. The remedy is to redesign the measurement and expression environment so truthful signal can surface without exposing people to disproportionate cost.

canonical_formula: expression_cost_asymmetry + protected_private_preference_snapshot + public_expression_trace + divergence_report + incentive_and_reprisals_redesign + recalibrated_expression_path -> lower_preference_falsification_pressure

structural_problem: A group, market, organization, public, platform, polity, or community treats visible expressions of preference as though they faithfully represent held preferences even though expressing some preferences is much more costly than expressing others. Actors therefore voice, endorse, repeat, vote for, or remain silent around the lower-cost position, causing public signal, perceived norms, and downstream decisions to diverge from the private distribution.

condition: Asymmetric expression costs exist

description: One preference carries higher risks of punishment, exclusion, reputational damage, harassment, legal exposure, career harm, identity threat, or opportunity loss.

condition: Visible expression is used as evidence of preference

description: Leaders, observers, markets, algorithms, publics, or institutions infer demand, consent, legitimacy, or consensus from what people publicly say or do.

condition: Private preference can plausibly diverge from public signal

description: There are signs that people speak differently in trusted settings, anonymous contexts, closed groups, exit behavior, private choices, or confidential interviews.

condition: Retaliation or reward structures are credible

description: Actors have reason to believe that expressing the costly preference will change their standing, safety, access, income, relationships, or future options.

condition: The distorted public distribution affects decisions or norms

description: Public expression is feeding policy, resource allocation, community norms, strategic plans, platform moderation, political legitimacy, product choice, or collective action.

condition: Direct demand for honesty would be unsafe or unreliable

description: Simply asking people to speak up would either increase risk, produce performative bravery, or deepen falsification because costs remain unchanged.

symptom: Private-public split

signal: Anonymous, confidential, exit, or informal signals differ sharply from meeting-room, public, survey-with-identity, or performative declarations.

symptom: Preference monoculture in visible settings

signal: Public agreement looks unusually uniform even when stakes, histories, or private conversations suggest heterogeneous preferences.

symptom: Fear-conditioned silence

signal: People avoid topics, use euphemisms, wait for high-status actors to speak first, or express support only after a safe majority is visible.

symptom: Cheap-talk conformity

signal: Actors make public endorsements that are weakly predictive of later behavior, exit, noncompliance, anonymous choice, or revealed preference.

symptom: Norm overestimation

signal: Members overestimate support for the publicly dominant position because dissenting preferences are not visible.

symptom: Sudden cascade after protection changes

signal: Once anonymity, protection, leadership turnover, legal change, or coalition size changes, many preferences surface at once.

symptom: Retaliation anecdotes govern speech

signal: A few punished examples, even if old or informal, define what people believe is safe to say.

symptom: Moralized reporting environment

signal: Expressed preference becomes a loyalty test, purity marker, or belonging signal rather than a decision input.

root_tension: Public decision systems need visible expression to coordinate, but visible expression becomes unreliable when the cost of truth varies by preference, actor, and context. The archetype resolves this by separating measurement from exposure, then redesigning the expression environment so public signal becomes less distorted.

case: Ordinary privacy

reason: People may legitimately keep preferences private without falsifying them; use this archetype only when public signal is being interpreted as preference evidence under asymmetric expression cost.

case: Simple ignorance or indecision

reason: If actors do not yet know what they prefer, use sensemaking, deliberation, or preference formation patterns rather than preference-falsification mitigation.

case: Pure revealed-preference inference

reason: If choices under constraints are the only available signal and expression cost is not the main issue, use revealed preference validation or incentive-compatible rule design.

case: Generic groupthink

reason: If the problem is premature consensus, loyalty pressure, or lack of dissent but not specifically private-public preference misreporting, use dissent protection or false convergence prevention.

case: Coerced disclosure demand

reason: If the proposed intervention would force vulnerable actors to expose private preferences, it violates this archetype; protection and aggregation are required.

structural_intervention: Replace naive reliance on visible preference expression with a protected, incentive-aware preference-revelation architecture: map expression costs, elicit private held preferences through safer channels, compare private and public distributions, report divergence at an appropriate level of aggregation, reduce retaliation or reward asymmetry, and create a legitimate path for public expression to recalibrate over time.

step: Define the preference domain

logic: Specify the decision, norm, policy, product, candidate, practice, identity claim, or social expectation whose private and public distributions may differ.

step: Map expression costs and rewards

logic: Identify what actors gain or lose by voicing each preference across public, semi-public, confidential, anonymous, and behavioral contexts.

step: Segment exposure risk

logic: Determine whether costs differ by rank, dependency, identity, geography, platform visibility, legal exposure, relationship dependence, or status.

step: Capture private preference signal

logic: Use protected, anonymous, confidential, aggregate, sealed, randomized, third-party, or incentive-compatible elicitation so held preferences can be measured with lower risk.

step: Trace visible expression

logic: Record public statements, votes, endorsements, silence, compliance, likes, meeting behavior, or visible participation that decision makers currently treat as preference evidence.

step: Estimate divergence and uncertainty

logic: Compare private and public distributions, identify where public signal is inflated or suppressed, and preserve uncertainty rather than claiming perfect hidden truth.

step: Protect against harm and manipulation

logic: Prevent disclosure from exposing individuals, enabling retaliation, legitimizing harassment, or letting actors game anonymous channels.

step: Redesign expression incentives

logic: Change formal rules, facilitation, anonymity, sequencing, leadership response, status rewards, retaliation penalties, or participation constraints so truthful expression is less costly.

step: Report aggregate findings safely

logic: Convert the divergence into usable common knowledge at the right granularity, often with confidence bands, caveats, and no individual attribution.

step: Create a recalibration path

logic: Give actors and institutions a face-saving, procedurally legitimate way to revise public positions, decisions, or norms without punishing early or late truth-telling.

step: Monitor recurrence

logic: Track retaliation, new taboo formation, gaming, and return of divergence after the initial correction.

rule: Protect before revealing

guidance: Do not publish or solicit identifiable preferences until the expression-cost and retaliation environment is safe enough for the actors at risk.

rule: Treat public expression as a constrained signal

guidance: Where expression costs are asymmetric, public speech, silence, compliance, or applause should not be read as unqualified preference evidence.

rule: Aggregate when individual exposure is dangerous

guidance: Use aggregate reporting, thresholds, noise, confidence bands, third-party custodians, or delayed disclosure when individuals could be targeted.

rule: Separate measurement from advocacy

guidance: The revelation channel should first measure the distribution; it should not become a propaganda channel for a preferred outcome.

rule: Escalate to incentive redesign when divergence persists

guidance: If private-public divergence remains large after protected measurement, change the payoff surface rather than repeatedly asking people to be brave.

rule: Do not infer hidden truth from one private mechanism alone

guidance: Triangulate private reports with behavior, exit data, qualitative interviews, and uncertainty checks when stakes are high.

target_invariants: Held preferences must not be exposed in ways that endanger actors who supplied them.

target_invariants: The system must distinguish preference, belief, compliance, fear, and strategic signaling rather than collapsing them into one public-expression measure.

target_invariants: Aggregate truth-seeking must not become compelled ideological disclosure.

target_invariants: Expression-cost changes must be monitored after intervention, because protection can decay or be gamed.

target_invariants: The design must preserve legitimate privacy, deliberative uncertainty, and minority rights while improving public signal fidelity.

expected_outcomes: The gap between public expression and held preference becomes visible enough to prevent false consent or false demand inference.

expected_outcomes: Actors gain safer routes to express, withhold, nuance, or update preferences.

expected_outcomes: Decision makers stop over-weighting the cheap-to-voice position as if it were the genuine distribution.

expected_outcomes: Norms, policies, products, or collective decisions recalibrate toward more accurate preference information.

expected_outcomes: Retaliation risk and speech-cost asymmetry decline over time, or remain visible enough to govern interpretation.

Authoritarian Public-Opinion Revelation

short_definition: A high-risk public or political variant where preferences are falsified because dissenting expression can trigger surveillance, legal sanction, violence, exclusion, or loss of livelihood.

distinctive_feature: Expression cost includes coercive state, institutional, or community power rather than only embarrassment or status loss.

distinct_from_parent: The parent is cross-domain; this variant requires stronger safety, anonymity, and custody assumptions.

still_same_parent_because: The core structure remains cost-asymmetric preference expression and protected aggregate revelation.

use_when: Public opinion looks uniform under credible repression or surveillance.

use_when: The key design problem is to measure aggregate preference without exposing individuals.

use_when: Disclosure may need third-party custody, delayed publication, or high aggregation thresholds.

example: A protected polling design reports aggregate preference only when subgroup size and custody safeguards are sufficient.

failure_modes: Published aggregates expose local minorities.

failure_modes: External actors use the data to target groups.

name: Organizational Preference Falsification Repair

short_definition: A workplace or institutional variant where employees, members, or lower-status participants publicly endorse the safe preference while privately holding a different one.

distinctive_feature: Organizational power and career dependence shape the expression-cost map.

distinct_from_parent: The parent covers any domain; this variant centers hierarchy, meeting design, leadership response, and employment or membership risk.

still_same_parent_because: The core transformation remains public/private preference divergence repair through protected revelation and expression-cost redesign.

use_when: Hierarchy, promotion, evaluation, funding, or belonging makes some preferences expensive to voice.

use_when: Public meetings or surveys are treated as evidence of buy-in.

use_when: Leaders can change facilitation, sequencing, anonymity, or retaliation monitoring.

example: A leadership team collects sealed private support before public endorsement and revises the plan when the gap is large.

failure_modes: Leaders treat anonymous feedback as disloyalty.

failure_modes: Participants do not trust confidentiality.

short_definition: A survey, research, or public-response variant where people state the socially approved preference rather than the preference that predicts private behavior.

distinctive_feature: The distortion is often soft social desirability rather than explicit retaliation.

distinct_from_parent: The parent covers broader expression-cost asymmetry; this variant focuses on measurement error in stated preference contexts.

still_same_parent_because: A socially costly preference is still being under-expressed publicly or identifiably.

use_when: Stated preference is likely distorted by desirability, shame, moralized identity, or interviewer effects.

use_when: Private choice, anonymous response, or indirect questioning may produce a different distribution.

use_when: The goal is measurement fidelity rather than moral judgment.

example: A study compares identifiable brand-value claims with anonymous choice tasks to avoid overestimating socially approved preferences.

failure_modes: Indirect methods are over-interpreted as exact truth.

failure_modes: Participants infer the hidden target and continue performative responding.

near_names:
Preference Falsification Mitigation
Safe Preference Revelation Design
Truthful Preference Expression Design
Expression-Cost Preference Calibration
Private-Public Preference Gap Repair


Counterexample Boundary-Shift Audit

batch: Batch 02

slug: category

slug: boundary

slug: quantifier

slug: predicate

slug: falsifiability

slug: counterfactuals

slug: universality

slug: classification

slug: essentialism

slug: interpretation

essence_one_line: Freeze the original category scope before judging whether a counterexample can be excluded.

compression_statement: Counterexample boundary-shift audit disciplines the no-true-Scotsman move by separating a claim's quantifier, subject category, membership rule, and asserted predicate; recording the counterexample under the pre-challenge criteria; testing any proposed exclusion for independent justification; and requiring either principled scope revision or claim weakening when the counterexample still belongs.

core_intuition: A category may need refinement, but it cannot be refined solely by subtracting every case that makes the claim false. When the boundary moves only after the inconvenient case appears, the universal claim stops risking error and starts defining itself as true by construction.

canonical_formula: Given claim ∀x∈C: P(x) and candidate a such that a∈C_old and ¬P(a), valid exclusion requires an independent criterion R(a)=false that is not equivalent to ¬P(a) and is not introduced solely after observing ¬P(a); otherwise revise ∀.

structural_problem: A universal or category-based claim encounters a counterexample. Instead of accepting the counterexample, weakening the quantifier, narrowing the claim by a principled prior criterion, or revising the theory, the claimant redefines membership in the subject category to exclude the counterexample. The claim is preserved, but the subject category is emptied of independent content because membership increasingly means “being the kind of case that satisfies the claim.”

trigger_conditions: A claim has a universal, near-universal, purity, essence, or identity form such as “all X are Y” or “real X do Y.”

trigger_conditions: A case is presented that appears to belong to the subject category while violating the asserted predicate.

trigger_conditions: The response introduces “true,” “real,” “genuine,” “authentic,” “proper,” or “not really one of us” language after the counterexample appears.

trigger_conditions: Membership criteria were vague, implicit, or different before the challenge than after the challenge.

trigger_conditions: The category carries social, moral, institutional, scientific, or identity stakes that make admitting exceptions costly.

trigger_conditions: The claimant treats category membership as a flexible defense of the claim rather than as an independently testable condition.

trigger_conditions: Repeated counterexamples are handled by repeated boundary contraction instead of by claim revision.

symptoms: Every disconfirming member is dismissed as not a “true” member, while confirming members are accepted without the same scrutiny.

symptoms: The exclusion rule is introduced only after the counterexample is named.

symptoms: The exclusion criterion closely mirrors the predicate the universal claim was supposed to prove.

symptoms: No possible case can both count as a member and disconfirm the claim.

symptoms: The conversation shifts from whether the claim is true to whether the troublesome case deserves membership status.

symptoms: Definitions become increasingly moralized, purity-based, or loyalty-based as evidence accumulates.

symptoms: The claim retains rhetorical force as a broad universal while technically applying only to a narrowed, favorable subset.

root_tension: Categories require boundaries, but a boundary used to test a claim must not be rebuilt from the claim after disconfirming evidence appears.

anti_signatures: The exclusion follows a clear membership criterion that was stated before the counterexample and is independent of the asserted predicate.

anti_signatures: The counterexample is shown by evidence to be a misclassified case under a stable taxonomy.

anti_signatures: The claim is explicitly definitional or analytic, and the speaker is not using it as empirical evidence about a broader group.

anti_signatures: A theory is honestly revised by narrowing its scope, downgrading its quantifier, and documenting the reason for the change.

anti_signatures: The task is a general fairness or inclusion review of category boundaries; use category_boundary_audit or boundary_critique_audit.

anti_signatures: The task is to find counterexamples before trusting a rule; use counterexample_search.

anti_signatures: The task is to design a yes-or-no criterion from scratch; use predicate_criterion_formalization.

structural_intervention: Convert the dispute from an elastic identity assertion into an auditable claim-scope record. Parse the original claim, freeze the pre-challenge membership criteria, record the counterexample, separate membership from the asserted property, test any proposed exclusion for independent justification and timing, then require a visible remedy: admit the counterexample, weaken the quantifier, revise the category with a noncircular rationale, or abandon the claim.

action: Record the claim as originally stated, including quantifier, subject category, predicate, speaker context, and intended scope.

reason: The fallacy depends on changing the domain after pressure arrives, so the original formulation must be preserved.

action: Separate “all/some/no/most,” the subject category, the membership criterion, and the asserted property.

reason: No-true-Scotsman moves hide by blending category membership with the property under dispute.

action: Write down the membership criteria implied or used before the counterexample was introduced.

reason: A boundary can be revised, but the revision must be compared against the prior boundary.

action: Assess whether the candidate case belonged under the pre-challenge criteria using evidence independent of the asserted predicate.

reason: A genuine misclassification is not a fallacy; an inconvenient member is.

action: Ask whether the new exclusion criterion has a category purpose, evidence base, and application rule independent of the desire to save the claim.

reason: The key diagnostic is whether exclusion is principled or merely claim-preserving.

action: Flag criteria introduced after the counterexample that are equivalent to the predicate or selectively applied only to disconfirming cases.

reason: Post-hoc circular criteria make the universal unfalsifiable by membership manipulation.

action: If the counterexample remains admissible, weaken the quantifier, qualify the domain, split the category, or abandon the claim; if exclusion is legitimate, document the independent criterion.

reason: A valid process should produce either a revised claim or a defensible category rule, not a silent rescue.

action: Publish a small diff showing what changed in the category boundary and what happens to past and future cases.

reason: Boundary-change records prevent the same counterexample from disappearing without epistemic cost.

decision_rules: If the proposed exclusion rule was introduced only after the counterexample and tracks the disputed predicate, treat it as ad hoc unless independently justified.

decision_rules: If the candidate case satisfies the old membership criterion and violates the predicate, the universal claim must be weakened or rejected.

decision_rules: If a category is legitimately refined, update the claim wording so its apparent scope no longer exceeds the new domain.

decision_rules: Do not treat “true,” “real,” or “genuine” as evidence; require an explicit membership criterion.

decision_rules: Require symmetrical application: a criterion used to exclude disconfirming cases must also be applied to confirming cases.

decision_rules: When category membership is socially or legally consequential, add an appeal path and harm review before accepting boundary revision.

decision_rules: If no observation could count as a member that disconfirms the claim, relabel the claim as definitional, normative, or unfalsifiable rather than empirical.

target_invariants: The original claim remains recoverable after revision.

target_invariants: Membership criteria are independent from the disputed predicate whenever the claim is empirical.

target_invariants: Counterexamples cannot be removed without a documented exclusion rationale.

target_invariants: Claim scope and quantifier match the revised domain.

target_invariants: Boundary revisions apply symmetrically to favorable and unfavorable cases.

target_invariants: Legitimate category learning remains possible; only claim-saving circular revision is blocked.

expected_outcomes: Universal claims become corrigible rather than self-protecting.

expected_outcomes: Legitimate taxonomic refinement is separated from fallacious category contraction.

expected_outcomes: Counterexamples retain evidentiary force unless independently disqualified.

expected_outcomes: Debates move from purity rhetoric to explicit scope, criteria, and quantifier revision.

expected_outcomes: Groups, institutions, and theories become less able to protect identity claims by silently excluding embarrassing members.