Distributional-Assumption Governance

type: archetype

source: generated

batch: Batch 19

aliases:
Distributional Assumption Audit
Distribution-Family Governance
Probability-Model Assumption Governance
Shape-Commitment Review
Distributional Model Contract

slug: assumption

slug: probability

slug: uncertainty

slug: robustness

slug: validation

slug: stationarity

slug: calibration

components: Random Quantity, Estimand, and Unit Definition

components: Substantive Rationale and Mechanism Link

components: Joint Dependence and Conditional Structure

components: Missingness, Censoring, Truncation, and Selection Map

components: Alternative-Family and Assumption-Light Comparator

components: Simulation, Predictive, and Holdout Validation

components: Fit-for-Use Acceptance and Exception Thresholds

components: Robust Decision Fallback and Escalation Path

components: Regime-Drift Monitoring and Revision Trigger

components: Provenance, Communication, Equity, and Accountability

mechanisms: Support, Shape, and Tail Diagnostic Suite

mechanisms: Holdout Calibration and Coverage Backtest

essence_one_line: Make probability-distribution commitments explicit, evidence-grounded, consequence-aware, stress-tested, and revisable before they govern inference or action.

compression_statement: When an estimate or decision depends on assuming a particular distributional family, support, tail, dependence, or observation process, define that commitment as a governed contract: specify its use and boundary; justify and diagnose it; compare plausible alternatives; propagate misspecification to the decision; accept, limit, or reject it by stakes-aware criteria; monitor validity; and preserve accountable fallback.

core_intuition: A distribution is not merely a curve fitted to data. It encodes which values are possible, how often extremes occur, whether observations move together, and how evidence outside the sample is extrapolated. Making those commitments inspectable prevents convenience from becoming hidden certainty.

canonical_formula: governed distributional commitment = explicit scope + plausible process + plural diagnostics + alternatives + consequential sensitivity + fit-for-use decision + monitoring + accountable fallback

structural_problem: Statistical and operational decisions often inherit a distributional family from convention, software defaults, thin data, or center-fit diagnostics. The commitment remains invisible even though support, tails, mixtures, dependence, missingness, and regime assumptions control uncertainty and action.

trigger_conditions: an estimate, interval, forecast, threshold, reserve, or ranking depends on a probability model

trigger_conditions: tail or boundary behavior drives consequence

trigger_conditions: data are sparse, censored, selected, clustered, or nonstationary

trigger_conditions: credible alternative families produce different decisions

trigger_conditions: a model is reused beyond its original population or regime

trigger_conditions: automated or irreversible action amplifies misspecification

symptoms: the chosen family is described only as standard

symptoms: fit is defended by one statistic or plot

symptoms: marginal fit substitutes for dependence validation

symptoms: diagnostics never reach decision sensitivity

symptoms: model limitations have no fallback consequence

symptoms: the assumption has no owner or expiry trigger

root_tension: A stronger probability model can improve efficiency, interpretability, and extrapolation, but the same commitment can manufacture precision and hide consequence when its shape assumptions outrun evidence.

anti_signatures: the quantity is deterministic for the relevant decision

anti_signatures: the intervention concerns general uncertainty communication without a distributional commitment

anti_signatures: the task is only to characterize observed variability

anti_signatures: the response is pure black-swan preparedness outside modelable event classes

anti_signatures: a concrete test or plot is the entire requested artifact

structural_intervention: Turn each consequential distributional commitment into an explicit, reviewable, stakes-aware lifecycle with defined boundaries, evidence, alternatives, validation, decision sensitivity, acceptance status, operational fallback, monitoring, and accountable retirement.

action_logic: define the decision use, modeled quantity, unit, population, horizon, and consequence of error

action_logic: record family, support, parameter, tail, dependence, transformation, and observation-process commitments

action_logic: connect the proposed shape to substantive process and empirical evidence

action_logic: inspect support, tails, zeros, mixtures, dependence, missingness, censoring, truncation, and selection

action_logic: compare credible families and assumption-light alternatives

action_logic: propagate alternatives through decision outputs

action_logic: validate on simulation, replication, holdout, future, and subgroup evidence where feasible

action_logic: apply fit-for-use acceptance, exception, or rejection thresholds

action_logic: activate robust fallback and escalation when evidence is inadequate

action_logic: monitor distribution shift and retire or revise the commitment

action_logic: communicate provenance, limitations, affected groups, and downstream uses

decision_rules: the greater the irreversible or safety consequence, the stronger the evidence and independent review required

decision_rules: failure of center fit is sufficient to reject, but apparent center fit is not sufficient to accept tail or joint claims

decision_rules: if credible alternatives change the action, report model dependence and use a robust or reversible response

decision_rules: if data cannot discriminate among consequential families, widen uncertainty or use assumption-light inference

decision_rules: if the observation process can create the apparent shape, model or bound that process before population inference

decision_rules: if drift crosses a named trigger, suspend inherited acceptance until revalidation

target_invariants: the modeled quantity, unit, boundary, and decision use remain stable and traceable

target_invariants: support and impossible values are respected

target_invariants: tail and dependence claims do not outrun evidence

target_invariants: subgroup and rare-case harms remain reviewable

target_invariants: acceptance has ownership, expiry, and revision triggers

expected_outcomes: more defensible uncertainty and extrapolation

expected_outcomes: decisions robust to plausible misspecification

expected_outcomes: earlier detection of tail, dependence, selection, and regime failures

expected_outcomes: clearer model limitations and fallback actions

expected_outcomes: improved reproducibility and accountability

expected_outcomes: less false precision and rare-case erasure

prose_essence:
Distributional-Assumption Governance makes commitments about probability shape explicit before they govern inference or action. It treats a proposed family, support, tail, dependence structure, transformation, and observation process as a revisable contract rather than an invisible technical default.

The archetype is needed because a distribution does more than summarize data. It determines which values are possible, how probability moves into unobserved regions, whether extremes are rare or merely unobserved, whether units fail together, and how missing or selected observations are interpreted. Those commitments can change intervals, rankings, reserves, thresholds, safety margins, and who bears error.

Governance does not require one universal test or the most flexible model. It requires evidence proportionate to consequence, credible alternatives, decision-level sensitivity, an enforceable fallback, and a named path for revision when the process or population changes.

prose_when_to_use:
Use it when a probability-distribution commitment materially affects an estimate, forecast, interval, simulation, allocation, reserve, threshold, or automated decision. It is especially important when support, zeros, mixtures, tails, dependence, censoring, selection, sparse evidence, or regime change can alter the conclusion.

Use a lighter implementation for reversible, low-stakes description and a stronger implementation for safety, rights, solvency, irreversible investment, or automated decisions. The verification premium should rise when evidence is thin, extrapolation is long, tails dominate harm, or affected groups cannot easily contest an error.

Do not use this archetype merely to decorate a report with diagnostics. If the commitment has no decision consequence, variability characterization may suffice. If evidence cannot justify a strong family, assumption-light inference or bounds may be the appropriate response. If the event class lies outside trusted models, black-swan preparedness remains necessary.

prose_structural_problem:
Many workflows select a familiar distribution before clarifying what quantity, unit, population, horizon, or decision it represents. Software then estimates parameters and reports precision conditional on that choice. The conditional uncertainty looks rigorous while structural uncertainty about family, support, tails, dependence, observation, and regime remains hidden.

Observed shape is not automatically process shape. Aggregation can create smoothness; selection can remove low or high values; censoring can compress tails; mixtures can imitate heavy dispersion; clustering can make independent-looking records share causes. A good fit near the center can coexist with catastrophic error at a decision threshold.

The root tension is productive commitment versus unjustified certainty. Some assumption is often necessary to learn efficiently and act. Yet convenience, precedent, or thin evidence can harden a provisional family into infrastructure. Mature governance preserves useful structure while making its scope, alternatives, consequences, and expiry visible.

prose_intervention_logic:
1. **Lifecycle step 1.** Define the decision use, modeled quantity, unit, population, horizon, and consequence of error. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
2. **Lifecycle step 2.** Record family, support, parameter, tail, dependence, transformation, and observation-process commitments. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
3. **Lifecycle step 3.** Connect the proposed shape to substantive process and empirical evidence. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
4. **Lifecycle step 4.** Inspect support, tails, zeros, mixtures, dependence, missingness, censoring, truncation, and selection. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
5. **Lifecycle step 5.** Compare credible families and assumption-light alternatives. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
6. **Lifecycle step 6.** Propagate alternatives through decision outputs. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
7. **Lifecycle step 7.** Validate on simulation, replication, holdout, future, and subgroup evidence where feasible. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
8. **Lifecycle step 8.** Apply fit-for-use acceptance, exception, or rejection thresholds. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
9. **Lifecycle step 9.** Activate robust fallback and escalation when evidence is inadequate. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
10. **Lifecycle step 10.** Monitor distribution shift and retire or revise the commitment. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.
11. **Lifecycle step 11.** Communicate provenance, limitations, affected groups, and downstream uses. The record must show who owns the claim, which evidence supports it, and what downstream action changes if the step fails.

The order matters. A diagnostic cannot establish fitness until the decision use and modeled quantity are stable. An alternative-family comparison cannot protect a decision until differences are propagated into the action. Monitoring cannot preserve validity unless crossing a trigger changes acceptance status and downstream behavior.

prose_key_components:
### Decision Use and Consequence Scope (`decision_use_and_consequence_scope`)

Defines the inference, forecast, allocation, threshold, or risk decision that will consume the assumed distribution and identifies what changes if the assumption is wrong.

Semantic canonical mapping retained the complete legacy component record: {"slug":"decision_use_and_consequence_scope","name":"Decision Use and Consequence Scope","role":"Defines the inference, forecast, allocation, threshold, or risk decision that will consume the assumed distribution and identifies what changes if the assumption is wrong.","notes":"Adequacy is use-relative. A harmless descriptive approximation may be unacceptable when tail probability sets a safety limit, benefit eligibility, capital reserve, or clinical action.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Random Quantity, Estimand, and Unit Definition (`random_quantity_estimand_and_unit_definition`)

States exactly what uncertain quantity is modeled, at which unit, aggregation level, time horizon, and population or process boundary.

Semantic canonical mapping retained the complete legacy component record: {"slug":"random_quantity_estimand_and_unit_definition","name":"Random Quantity, Estimand, and Unit Definition","role":"States exactly what uncertain quantity is modeled, at which unit, aggregation level, time horizon, and population or process boundary.","notes":"A distribution fitted to transactions, people, sites, days, or averages answers different questions. The unit must not drift between fitting, validation, and decision use.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Data-Generating and Sampling Boundary (`data_generating_and_sampling_boundary`)

Documents how observations arise, which cases can enter the data, and which deployment conditions the data are expected to represent.

Semantic canonical mapping retained the complete legacy component record: {"slug":"data_generating_and_sampling_boundary","name":"Data-Generating and Sampling Boundary","role":"Documents how observations arise, which cases can enter the data, and which deployment conditions the data are expected to represent.","notes":"Distributional shape can be an artifact of sampling, aggregation, measurement, censoring, selection, or intervention. The boundary separates evidence about observed data from claims about the underlying process.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Distribution-Family Commitment Register (`distribution_family_commitment_register`)

Names the proposed family or class, support, discreteness, parameters, tail behavior, dependence claims, transformations, and conditions under which the commitment is asserted.

Semantic canonical mapping retained the complete legacy component record: {"slug":"distribution_family_commitment_register","name":"Distribution-Family Commitment Register","role":"Names the proposed family or class, support, discreteness, parameters, tail behavior, dependence claims, transformations, and conditions under which the commitment is asserted.","notes":"The register converts an invisible modeling default into a reviewable contract. It must distinguish fixed structure, estimated quantities, conventions, and unresolved uncertainty.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Substantive Rationale and Mechanism Link (`substantive_rationale_and_mechanism_link`)

Explains why the process could plausibly generate the proposed shape instead of relying only on convenience or familiar software defaults.

Semantic canonical mapping retained the complete legacy component record: {"slug":"substantive_rationale_and_mechanism_link","name":"Substantive Rationale and Mechanism Link","role":"Explains why the process could plausibly generate the proposed shape instead of relying only on convenience or familiar software defaults.","notes":"Mechanistic rationale does not prove fit, but it constrains plausible families and exposes when a mathematically adequate curve tells a substantively impossible story.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Empirical Shape and Diagnostic Profile (`empirical_shape_and_diagnostic_profile`)

Characterizes center, spread, skew, discreteness, bounds, zeros, modes, outliers, tails, and residual structure using multiple views and uncertainty-aware diagnostics.

Semantic canonical mapping retained the complete legacy component record: {"slug":"empirical_shape_and_diagnostic_profile","name":"Empirical Shape and Diagnostic Profile","role":"Characterizes center, spread, skew, discreteness, bounds, zeros, modes, outliers, tails, and residual structure using multiple views and uncertainty-aware diagnostics.","notes":"No single fit statistic is sufficient. The profile should reveal which regions of the distribution drive the intended decision and where data are sparse.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Support, Tail, Zero, and Mixture Review (`support_tail_zero_and_mixture_review`)

Checks whether the family respects possible values and captures structural zeros, point masses, truncation, heavy tails, multimodality, and latent mixtures.

Semantic canonical mapping retained the complete legacy component record: {"slug":"support_tail_zero_and_mixture_review","name":"Support, Tail, Zero, and Mixture Review","role":"Checks whether the family respects possible values and captures structural zeros, point masses, truncation, heavy tails, multimodality, and latent mixtures.","notes":"Good center fit can conceal impossible negative values, understated extremes, or pooled subpopulations. Decision-critical tail and boundary behavior require their own evidence.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Joint Dependence and Conditional Structure (`joint_dependence_and_conditional_structure`)

Makes independence, conditional independence, clustering, serial dependence, spatial dependence, and cross-variable tail dependence explicit.

Semantic canonical mapping retained the complete legacy component record: {"slug":"joint_dependence_and_conditional_structure","name":"Joint Dependence and Conditional Structure","role":"Makes independence, conditional independence, clustering, serial dependence, spatial dependence, and cross-variable tail dependence explicit.","notes":"Correct marginal distributions do not validate a joint model. Dependence errors can dominate pooled risk, uncertainty intervals, and multi-stage decisions.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Missingness, Censoring, Truncation, and Selection Map (`missingness_censoring_truncation_and_selection_map`)

Traces which values are unobserved, coarsened, excluded, delayed, or preferentially sampled and how that observation process changes the apparent distribution.

Semantic canonical mapping retained the complete legacy component record: {"slug":"missingness_censoring_truncation_and_selection_map","name":"Missingness, Censoring, Truncation, and Selection Map","role":"Traces which values are unobserved, coarsened, excluded, delayed, or preferentially sampled and how that observation process changes the apparent distribution.","notes":"Treating observed values as an unbiased sample can turn an observation mechanism into a false shape claim. The map connects data limitations to allowable inference.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Alternative-Family and Assumption-Light Comparator (`alternative_family_and_assumption_light_comparator`)

Defines credible rival families, transformations, flexible or nonparametric alternatives, and simple baselines against which the preferred commitment is evaluated.

Semantic canonical mapping retained the complete legacy component record: {"slug":"alternative_family_and_assumption_light_comparator","name":"Alternative-Family and Assumption-Light Comparator","role":"Defines credible rival families, transformations, flexible or nonparametric alternatives, and simple baselines against which the preferred commitment is evaluated.","notes":"Governance needs a counterfactual: whether the conclusion depends on this family, any reasonable family, or no strong parametric commitment at all.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Decision-Consequential Sensitivity Map (`decision_consequential_sensitivity_map`)

Shows how estimates, rankings, thresholds, intervals, and actions change across plausible distributional commitments and parameter uncertainty.

Semantic canonical mapping retained the complete legacy component record: {"slug":"decision_consequential_sensitivity_map","name":"Decision-Consequential Sensitivity Map","role":"Shows how estimates, rankings, thresholds, intervals, and actions change across plausible distributional commitments and parameter uncertainty.","notes":"The key question is not whether every diagnostic rejects, but whether plausible misspecification changes a consequential conclusion. Sensitivity must reach the decision output.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Simulation, Predictive, and Holdout Validation (`simulation_predictive_and_holdout_validation`)

Tests whether the fitted commitment reproduces decision-relevant features in simulated, replicated, held-out, or future observations.

Semantic canonical mapping retained the complete legacy component record: {"slug":"simulation_predictive_and_holdout_validation","name":"Simulation, Predictive, and Holdout Validation","role":"Tests whether the fitted commitment reproduces decision-relevant features in simulated, replicated, held-out, or future observations.","notes":"Validation should inspect calibration, residual structure, extremes, subgroup behavior, and operational quantities rather than rewarding in-sample fit alone.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Fit-for-Use Acceptance and Exception Thresholds (`fit_for_use_acceptance_and_exception_thresholds`)

Defines ex ante evidence thresholds for accepting, conditionally accepting, limiting, escalating, or rejecting a distributional commitment.

Semantic canonical mapping retained the complete legacy component record: {"slug":"fit_for_use_acceptance_and_exception_thresholds","name":"Fit-for-Use Acceptance and Exception Thresholds","role":"Defines ex ante evidence thresholds for accepting, conditionally accepting, limiting, escalating, or rejecting a distributional commitment.","notes":"A p-value or information criterion cannot silently decide fitness. Thresholds should reflect stakes, sample size, tail consequence, reversibility, and available alternatives.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Robust Decision Fallback and Escalation Path (`robust_decision_fallback_and_escalation_path`)

Specifies safer actions, wider uncertainty, bounds, manual review, assumption-light methods, or deferred commitment when distributional evidence is inadequate.

Semantic canonical mapping retained the complete legacy component record: {"slug":"robust_decision_fallback_and_escalation_path","name":"Robust Decision Fallback and Escalation Path","role":"Specifies safer actions, wider uncertainty, bounds, manual review, assumption-light methods, or deferred commitment when distributional evidence is inadequate.","notes":"An audit without an operational fallback merely documents fragility. The response should reduce dependence on unsupported shape claims while preserving necessary decisions.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Regime-Drift Monitoring and Revision Trigger (`regime_drift_monitoring_and_revision_trigger`)

Monitors whether the data-generating process, observation process, population, or dependence structure changes enough to invalidate the accepted commitment.

Semantic canonical mapping retained the complete legacy component record: {"slug":"regime_drift_monitoring_and_revision_trigger","name":"Regime-Drift Monitoring and Revision Trigger","role":"Monitors whether the data-generating process, observation process, population, or dependence structure changes enough to invalidate the accepted commitment.","notes":"Distributional adequacy expires. Named triggers, cadence, ownership, and retirement rules prevent a once-reasonable model from becoming an inherited truth.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

### Provenance, Communication, Equity, and Accountability (`provenance_communication_equity_and_accountability`)

Communicates the assumption, evidence, alternatives, limitations, affected groups, ownership, approvals, and downstream uses in decision-facing language.

Semantic canonical mapping retained the complete legacy component record: {"slug":"provenance_communication_equity_and_accountability","name":"Provenance, Communication, Equity, and Accountability","role":"Communicates the assumption, evidence, alternatives, limitations, affected groups, ownership, approvals, and downstream uses in decision-facing language.","notes":"Distributional choices can shift burdens across subgroups, erase rare cases, or manufacture false precision. Traceability and contestability are structural safeguards, not documentation polish.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["distributional_assumption_governance"],"evidence_expectation":"Record the source, uncertainty, owner, decision consequence, review trigger, and evidence needed to retain or revise this part of the assumption contract.","not_a_mechanism_because":"This is a persistent responsibility in governing a distributional commitment, not a particular plot, test, simulation, worksheet, or software procedure."}

prose_common_mechanisms:
### Distributional-Assumption Card (`distributional_assumption_card`)

**Type:** `documentation`

Summarizes modeled quantity, family, support, parameter treatment, rationale, evidence, limitations, decision use, owner, and revision trigger.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"distributional_assumption_card","name":"Distributional-Assumption Card","mechanism_type":"documentation","role":"Summarizes modeled quantity, family, support, parameter treatment, rationale, evidence, limitations, decision use, owner, and revision trigger.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

### Support, Shape, and Tail Diagnostic Suite (`support_shape_and_tail_diagnostic_suite`)

**Type:** `diagnostic`

Combines empirical distributions, residual views, quantile comparisons, boundary checks, and tail-focused summaries without treating any one view as decisive.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"support_shape_and_tail_diagnostic_suite","name":"Support, Shape, and Tail Diagnostic Suite","mechanism_type":"diagnostic","role":"Combines empirical distributions, residual views, quantile comparisons, boundary checks, and tail-focused summaries without treating any one view as decisive.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

### Candidate-Family Comparison Grid (`candidate_family_comparison_grid`)

**Type:** `comparison`

Compares credible families and flexible baselines by support, rationale, predictive adequacy, tail behavior, complexity, and decision consequence.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"candidate_family_comparison_grid","name":"Candidate-Family Comparison Grid","mechanism_type":"comparison","role":"Compares credible families and flexible baselines by support, rationale, predictive adequacy, tail behavior, complexity, and decision consequence.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

### Predictive Replication Check (`predictive_replication_check`)

**Type:** `simulation`

Generates replicated data from the fitted commitment and compares decision-relevant features with observed or held-out behavior.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"predictive_replication_check","name":"Predictive Replication Check","mechanism_type":"simulation","role":"Generates replicated data from the fitted commitment and compares decision-relevant features with observed or held-out behavior.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

### Resampling Robustness Audit (`resampling_robustness_audit`)

**Type:** `resampling`

Re-estimates conclusions under resampling or perturbation to reveal finite-sample instability and dependence on a few observations.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"resampling_robustness_audit","name":"Resampling Robustness Audit","mechanism_type":"resampling","role":"Re-estimates conclusions under resampling or perturbation to reveal finite-sample instability and dependence on a few observations.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

### Distributional Sensitivity Grid (`distributional_sensitivity_grid`)

**Type:** `sensitivity_review`

Propagates plausible family, tail, dependence, transformation, and parameter choices through the final decision output.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"distributional_sensitivity_grid","name":"Distributional Sensitivity Grid","mechanism_type":"sensitivity_review","role":"Propagates plausible family, tail, dependence, transformation, and parameter choices through the final decision output.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

### Tail and Boundary Stress Scenario (`tail_and_boundary_stress_scenario`)

**Type:** `stress_test`

Tests actions against heavier tails, structural zeros, truncation, mixtures, extremes, and support-boundary behavior that ordinary fit can hide.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"tail_and_boundary_stress_scenario","name":"Tail and Boundary Stress Scenario","mechanism_type":"stress_test","role":"Tests actions against heavier tails, structural zeros, truncation, mixtures, extremes, and support-boundary behavior that ordinary fit can hide.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

### Holdout Calibration and Coverage Backtest (`holdout_calibration_and_coverage_backtest`)

**Type:** `validation`

Checks predictions, intervals, ranks, or event rates on data not used to choose or fit the distributional commitment.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"holdout_calibration_and_coverage_backtest","name":"Holdout Calibration and Coverage Backtest","mechanism_type":"validation","role":"Checks predictions, intervals, ranks, or event rates on data not used to choose or fit the distributional commitment.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

### Distribution-Shift Trigger Dashboard (`distribution_shift_trigger_dashboard`)

**Type:** `monitoring`

Tracks shape, residual, tail, subgroup, missingness, and dependence indicators against revision thresholds.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"distribution_shift_trigger_dashboard","name":"Distribution-Shift Trigger Dashboard","mechanism_type":"monitoring","role":"Tracks shape, residual, tail, subgroup, missingness, and dependence indicators against revision thresholds.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

### Independent Assumption-Challenge Gate (`independent_assumption_challenge_gate`)

**Type:** `governance`

Requires an independent reviewer to inspect rationale, alternatives, consequential sensitivity, safeguards, and exception decisions before high-stakes use.

Selection constraints and retained implementation evidence: match the data-generating and observation process; reach the decision-critical region rather than center fit only; preserve alternatives, limitations, and revision evidence; Complete legacy mechanism record retained: {"slug":"independent_assumption_challenge_gate","name":"Independent Assumption-Challenge Gate","mechanism_type":"governance","role":"Requires an independent reviewer to inspect rationale, alternatives, consequential sensitivity, safeguards, and exception decisions before high-stakes use.","maturity":"provisional","instantiates_archetypes":["distributional_assumption_governance"],"selection_constraints":["match the data-generating and observation process","reach the decision-critical region rather than center fit only","preserve alternatives, limitations, and revision evidence"],"not_an_archetype_because":"This is a concrete document, diagnostic, comparison, simulation, resampling procedure, stress test, backtest, dashboard, or review gate used within the broader governance lifecycle."}

prose_failure_modes:
### Software Default Commitment

**Cause.** A familiar package or default family substitutes for substantive rationale and alternatives.

**Mitigation.** Require an explicit family register, mechanism link, and independent comparison. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Single Metric Fit Absolutism

**Cause.** One test, score, or plot is treated as universal proof of adequacy.

**Mitigation.** Use plural diagnostics and fit-for-use thresholds tied to decision-critical features. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Center Fit Tail Failure

**Cause.** Good average fit hides understated extremes, structural zeros, bounds, or mixtures.

**Mitigation.** Run tail, support, zero, and mixture review plus stress scenarios. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Marginal Fit Joint Failure

**Cause.** Marginal distributions look adequate while dependence or clustering invalidates joint conclusions.

**Mitigation.** Model and validate conditional, serial, spatial, clustered, and tail dependence explicitly. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Observation Process Blindness

**Cause.** Missingness, censoring, truncation, selection, or measurement creates the apparent shape.

**Mitigation.** Map the observation process and limit inference to supported boundaries. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Diagnostic Data Reuse

**Cause.** The same data select, fit, tune, and validate the family, producing optimistic adequacy.

**Mitigation.** Use holdout, replication, resampling, or protected validation evidence where feasible. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Decision Disconnect

**Cause.** Diagnostics are reported but plausible alternatives are never propagated to the actual decision.

**Mitigation.** Build a consequential sensitivity map and define action thresholds. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### False Precision Under Weak Evidence

**Cause.** Sparse data are converted into narrow estimates by an unjustified family commitment.

**Mitigation.** Widen uncertainty, use bounds or assumption-light comparators, and escalate irreversible uses. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Subgroup And Rare Case Erasure

**Cause.** A pooled distribution fits the majority while harming or misrepresenting smaller groups and rare cases.

**Mitigation.** Validate by affected subgroup, preserve tail cases, and document burden shifts. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Stationary Forever Assumption

**Cause.** A once-accepted family persists after the process, population, measurement, or dependence structure changes.

**Mitigation.** Monitor regime evidence and use named revision and retirement triggers. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Assumption Documentation Theater

**Cause.** A model card exists, but unsupported commitments still drive automated or irreversible decisions.

**Mitigation.** Link exception status to enforceable fallback, approval, and downstream-use controls. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

### Method Over Archetype Drift

**Cause.** The intervention collapses into a catalog of tests or plots without ownership and decision governance.

**Mitigation.** Preserve the lifecycle: scope, commitment, evidence, alternatives, consequence, acceptance, monitoring, and accountability. Confirm that remediation changes acceptance status or downstream action rather than only adding documentation.

prose_neighbor_distinctions:
### `coverage_probability_calibration`

Calibrates whether an interval procedure achieves its nominal coverage. Distributional-Assumption Governance is broader and upstream: it governs family, support, tail, dependence, observation-process, alternative, and retirement commitments across many outputs.

### `knowledge_warrant_audit`

Audits the type and strength of warrant for beliefs generally. This archetype specializes that discipline into the structure and consequences of probability-distribution commitments.

### `assumption_light_inference`

Selects methods that require fewer fragile assumptions. It is one fallback or comparator; the parent also governs when a stronger distributional commitment is justified and how it remains accountable.

### `assumption_stress_testing`

Breaks or reverses plan assumptions to test resilience. This archetype examines a specific statistical commitment through fit, alternatives, decision sensitivity, validation, and monitoring.

### `uncertainty_explicitness`

Makes unknowns, ranges, and confidence visible. Distributional governance determines whether the probability structure used to produce those representations is defensible.

### `variability_characterization`

Describes observed variation before acting. This archetype governs inferential commitments about the data-generating and observation processes, including extrapolation and decision use.

### `stationarity_validation`

Checks whether predictive conditions remain stable through time or regime. Stationarity is one distributional premise; the parent also covers support, family, tails, mixtures, dependence, and selection.

### `sensitivity_analysis_protocol`

Varies assumptions or parameters to reveal conclusion fragility. Distributional sensitivity is a mechanism inside a broader contract that also includes rationale, acceptance, fallback, provenance, and retirement.

### `generalization_validation`

Tests whether a learned pattern transfers to new cases. Distributional governance may use holdout evidence but also addresses whether the assumed probability form itself is plausible and decision-fit.

### `representative_sampling_design`

Designs observation selection to represent a target population. Sampling is an input boundary here; even representative samples can be modeled with an unsuitable family or dependence structure.

### `black_swan_preparedness`

Builds survival capacity for high-impact surprise outside trusted forecasts. Distributional governance improves known model commitments but must not claim to eliminate deep uncertainty or unknown event classes.

### `tail_risk_preservation`

Protects rare important cases from common-case optimization. Tail-case protection is a safeguard here, but the parent governs the full distributional model lifecycle, including ordinary, joint, and limited-data settings.

The decisive boundary is lifecycle ownership. Related archetypes may supply uncertainty representation, assumption reduction, stress tests, coverage calibration, warrant review, sampling, stationarity, or generalization evidence. Distributional-Assumption Governance integrates these selectively around one specific commitment and its decision consequences.

prose_cross_domain_examples:
### Clinical Trials

Govern a response-distribution and dropout commitment before using a treatment-effect interval for approval.

Support, missingness, subgroup, tail, and decision thresholds materially affect safety and benefit claims.
 The implementation records its modeled unit, family and observation assumptions, alternatives, decision sensitivity, acceptance status, fallback, and revision trigger.

### Insurance And Finance

Govern severity, frequency, and dependence assumptions used to size reserves and concentration limits.

Tail and joint misspecification can produce solvency decisions far outside observed samples.
 The implementation records its modeled unit, family and observation assumptions, alternatives, decision sensitivity, acceptance status, fallback, and revision trigger.

### Supply And Service Operations

Govern demand and lead-time distributions used for inventory and staffing thresholds.

Zeros, bursts, seasonality, truncation, and regime shifts change shortage and excess-capacity tradeoffs.
 The implementation records its modeled unit, family and observation assumptions, alternatives, decision sensitivity, acceptance status, fallback, and revision trigger.

### Machine Learning

Govern residual, class, and deployment-shift assumptions used to produce predictive uncertainty.

Held-out center performance can conceal subgroup, tail, dependence, and shift failures.
 The implementation records its modeled unit, family and observation assumptions, alternatives, decision sensitivity, acceptance status, fallback, and revision trigger.

### Environmental Risk

Govern precipitation or loss-tail extrapolation used for infrastructure design levels.

Sparse extremes, nonstationarity, threshold choice, and irreversible consequence demand explicit limits and stress bounds.
 The implementation records its modeled unit, family and observation assumptions, alternatives, decision sensitivity, acceptance status, fallback, and revision trigger.

### Public Policy

Govern a benefit or burden distribution used to set eligibility, enforcement, or resource thresholds.

Pooled fit can hide subgroup burden and turn modeling convention into distributive policy.
 The implementation records its modeled unit, family and observation assumptions, alternatives, decision sensitivity, acceptance status, fallback, and revision trigger.

### Extended emergency-service example

A regional emergency service uses a probability model to set staffing and supply capacity. The team first defines calls per zone-hour as the modeled quantity and distinguishes routine demand from declared incidents. It records count support, excess zeros, seasonality, spatial clustering, response-time censoring, and population changes. Several count, mixture, flexible, and heavy-tail alternatives are justified and compared. The team examines predictive replication, holdout coverage, extreme-day performance, neighborhood slices, and dependence during shared events. Each credible alternative is propagated through missed-service and overtime decisions. Where the action changes, managers adopt a robust staffing floor and escalation reserve rather than choosing the most convenient family. Acceptance is conditional, with independent review, drift triggers, quarterly revalidation, and a public limitation note. The distribution is therefore useful without being treated as timeless truth.

Across domains, the mathematical family changes but the intervention remains stable: expose the commitment, test what matters for use, compare alternatives, propagate uncertainty into action, and revise when evidence or conditions change.

prose_non_examples:
- Selecting a normal distribution because a software dialog preselects it
- Running one normality test and declaring the entire model valid
- Plotting a histogram without linking shape to inference or action
- Using a nonparametric test without governing the decision boundary
- Publishing a model card whose exception status has no operational effect
- Claiming a tail model predicts every black swan

A fit result is not governance, a caveat is not a fallback, and an assumption is not validated forever. The archetype exists only when evidence, decision consequence, acceptance, accountability, and revision are connected.

prose_tradeoffs:
- **Model parsimony versus distributional flexibility:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Interpretability versus predictive adequacy:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Stronger assumptions versus information efficiency:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Center fit versus tail fidelity:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Pooled stability versus subgroup validity:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Analytic convenience versus support realism:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Early decision versus additional validation:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Narrow precision versus misspecification robustness:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Standardization versus domain-specific rationale:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Monitoring sensitivity versus false alarms:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Open disclosure versus security or privacy:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.
- **Automation speed versus independent challenge:** resolve the balance using decision consequence, data limits, explanatory plausibility, downstream dependence, reversibility, and the distribution of error burdens.

The governance process itself has cost. Additional alternatives, validation, and independent review consume time and expertise. But efficiency should be assessed against the cost of hidden misspecification, rework, unsafe thresholds, unfair exclusions, and inherited models whose limitations can no longer be traced.

prose_invariants_to_preserve:
- The modeled quantity, unit, boundary, and decision use remain stable and traceable.
- Support and impossible values are respected.
- Tail and dependence claims do not outrun evidence.
- Credible alternatives remain visible.
- Diagnostics reach decision consequences.
- Exceptions produce enforceable fallback.
- Subgroup and rare-case harms remain reviewable.
- Acceptance has ownership, expiry, and revision triggers.
- Conditional estimates must never be communicated as unconditional certainty.
- Failure to reject a diagnostic null must never be treated as proof of the assumed family.
- No accepted commitment may lose its owner, provenance, exception status, or expiry trigger when embedded downstream.

These invariants protect both epistemic and operational integrity. They allow the specific family, mechanism, and threshold to change while preserving inspectability, contestability, and safe response to misspecification.

prose_target_outcomes:
- Fewer hidden probability-model defaults.
- More defensible uncertainty and extrapolation.
- Decisions robust to plausible misspecification.
- Earlier detection of tail, dependence, selection, and regime failures.
- Clearer model limitations and fallback actions.
- Improved reproducibility and accountability.
- Less false precision and rare-case erasure.

Success is not a claim that the true distribution has been discovered. It is evidence that the chosen commitment is explicit, proportionate to its use, compared with credible alternatives, validated where consequences concentrate, and linked to action when evidence weakens.

prose_parameter_dimensions:
- **Decision stakes:** Consequence, reversibility, rights, safety, and scale determine evidence and review depth.
- **Modeled unit:** Individual, group, site, interval, transaction, or aggregate changes the implied process.
- **Family rigidity:** Named parametric family, flexible class, mixture, semi-parametric, or nonparametric comparator.
- **Support contract:** Bounded, nonnegative, discrete, continuous, circular, zero-inflated, censored, or truncated values.
- **Tail emphasis:** How much validation effort reaches rare but consequential regions.
- **Dependence scope:** Serial, spatial, hierarchical, network, conditional, and tail dependence included.
- **Alternative breadth:** Number and plausibility of rival families and assumption-light baselines.
- **Extrapolation distance:** How far the decision reaches beyond observed values, populations, or regimes.
- **Acceptance threshold:** Evidence needed for accepted, conditional, limited, escalated, or rejected status.
- **Sensitivity tolerance:** How much output or decision movement is acceptable across credible commitments.
- **Fallback conservatism:** Bounds, wider intervals, robust action, delay, manual review, or reserve.
- **Validation separation:** Degree of protection between selection, fitting, tuning, and evaluation data.
- **Monitoring cadence:** Frequency and power of shift, tail, residual, subgroup, and missingness review.
- **Revision latency:** Time allowed between trigger crossing and downstream model change.
- **Communication depth:** Technical record, decision memo, public limitation notice, or affected-party explanation.
- **Independent challenge:** Who may contest evidence, exceptions, and burden distribution.

Tune dimensions jointly. A rigid family may aid interpretation but magnify misspecification; a flexible model may fit better but obscure mechanism and extrapolation. Stronger monitoring can detect drift earlier but create false alarms. The right balance is governed by the cost of error, not by methodological fashion.

prose_variants_and_near_names:
### Parametric-Family Selection Governance

Govern selection among named parametric families when interpretable parameters, compact models, or analytic decisions make a family commitment useful.

Makes family comparison, parameter interpretation, complexity, and fit-for-use thresholds central. The same explicit register, alternatives, validation, sensitivity, fallback, monitoring, and accountability structure applies.

Use it when a named family supports the intended inference; and sample size and mechanism provide discriminating evidence; and family choice materially affects estimates or decisions. Typical failure modes are software-default family, information-criterion absolutism, parameter meaning drift, in-sample overfit. It remains a variant while those differences can be governed through the shared lifecycle.

### Tail and Extreme-Distribution Governance

Govern commitments about rare, extreme, bounded, or heavy-tailed outcomes when tail shape drives safety, reserve, or loss decisions.

Centers tail evidence limits, threshold choice, extrapolation distance, stress bounds, and asymmetric loss. Tail commitments still require explicit scope, rationale, alternatives, validation, sensitivity, fallback, monitoring, and communication.

Use it when extremes dominate consequence; and ordinary samples contain little tail evidence; and threshold, reserve, or safety decisions depend on extrapolation. Typical failure modes are thin-tail convenience, unbounded extrapolation, threshold shopping, rare-case erasure. It remains a variant while those differences can be governed through the shared lifecycle.

### Joint-Dependence Distribution Governance

Govern joint-distribution and dependence assumptions when correlated observations, clustered systems, or conditional relationships drive conclusions.

Makes conditional structure, common causes, tail dependence, hierarchy, and joint validation load-bearing. It retains the parent's explicit commitment, evidence, alternative, sensitivity, validation, monitoring, and accountability logic.

Use it when marginal fit can conceal joint failure; and observations cluster across time, place, group, or network; and pooling or portfolio claims depend on independence. Typical failure modes are independence by default, marginal-only validation, cluster leakage, crisis correlation blindness. It remains a variant while those differences can be governed through the shared lifecycle.

### Limited-Data Distribution Governance

Govern distributional commitments when small samples, censoring, missing tails, shifting regimes, or weak observability prevent decisive family validation.

Centers uncertainty inflation, partial identification, bounds, assumption-light comparators, expert challenge, and reversible action. The commitment remains explicit, tested to the extent possible, propagated into decisions, monitored, and revisable.

Use it when evidence cannot distinguish plausible families; and important regions are missing or censored; and decisions must proceed despite irreducible shape uncertainty. Typical failure modes are false precision, prior laundering, silent exception, irreversible action under weak evidence. It remains a variant while those differences can be governed through the shared lifecycle.

`distributional_assumption_audit`, `distribution_family_review`, `shape_family_commitment_review`, and `probability_model_assumption_governance` are near names for the parent. A named distribution, diagnostic plot, fit test, bootstrap, simulation, model card, or software selector is a mechanism or artifact rather than a separate archetype.

Parametric-Family Selection Governance

distinct_from_parent: The parent also governs flexible, semi-parametric, tail, mixture, and dependence commitments.

use_when: a named family supports the intended inference

use_when: sample size and mechanism provide discriminating evidence

use_when: family choice materially affects estimates or decisions

example_domains: experimental_science forecasting operations econometrics

failure_modes:
software-default family
information-criterion absolutism
parameter meaning drift
in-sample overfit

name: Tail and Extreme-Distribution Governance

distinct_from_parent: Many distributional decisions concern center, discreteness, zeros, or ordinary predictive fit rather than extremes.

use_when: ordinary samples contain little tail evidence

use_when: threshold, reserve, or safety decisions depend on extrapolation

failure_modes:
thin-tail convenience
unbounded extrapolation
threshold shopping
rare-case erasure

distinct_from_parent: Univariate settings may not require a full joint-dependence contract.

use_when: observations cluster across time, place, group, or network

use_when: pooling or portfolio claims depend on independence

failure_modes:
independence by default
marginal-only validation
cluster leakage
crisis correlation blindness

Limited-Data Distribution Governance

distinct_from_parent: Better-observed settings may support firmer fit-for-use acceptance.

use_when: evidence cannot distinguish plausible families

use_when: important regions are missing or censored

use_when: decisions must proceed despite irreducible shape uncertainty

failure_modes:
false precision
prior laundering
silent exception
irreversible action under weak evidence

near_names:
Distributional Assumption Audit
Shape-Family Commitment Review
Parametric Assumption Review


batch: Batch 24

canonical_family: Conceptual Structuring & Schemas

slug: iteration

slug: refinement

slug: convergence

slug: foresight

slug: optionality

slug: feedback

slug: constraint

slug: decision

slug: optimization

slug: diversity

essence_one_line: Alternate protected option expansion with evidence-led narrowing, using explicit gates and reopening rules so creativity and commitment strengthen rather than sabotage each other.

compression_statement: When a design process must both discover alternatives and deliver a coherent choice, separate generative from evaluative modes, require sufficient breadth before narrowing, converge with declared criteria and evidence, retain rejected options and rationale, and loop back when tests reveal that the chosen frame or solution is inadequate.

core_intuition: Generation and evaluation use conflicting cognitive and organizational conditions. If they are mixed, criticism suppresses novelty; if they are never rejoined, exploration produces no decision. Deliberate phase separation plus governed transitions lets the process alternate between variety and commitment without treating either as permanent.

canonical_formula: scoped_challenge -> protected_divergence -> breadth_gate -> evidence_generation -> criteria_led_convergence -> provisional_commitment -> feedback -> reopen_or_continue

structural_problem: A design process needs both variety and commitment, yet the norms that produce many meaningfully different options conflict with the criteria, comparison, and accountability needed to select and refine one. Without orchestration, evaluation begins too early, breadth becomes performative, or exploration continues without a defensible commitment.

trigger_conditions: The challenge admits multiple plausible frames, architectures, concepts, or implementation paths.

trigger_conditions: Early commitment would create path dependence before important dimensions or stakeholders are represented.

trigger_conditions: The team must eventually select, combine, test, or sequence a bounded set of options.

trigger_conditions: New evidence may require reopening either the problem frame or the solution space.

trigger_conditions: Generation and evaluation involve different roles, norms, information, or time horizons.

trigger_conditions: The cost of missing a materially different option exceeds the cost of a bounded exploration phase.

symptoms: The first plausible concept becomes the default before alternatives are made comparable.

symptoms: Brainstorming produces many cosmetic variants inside one hidden solution family.

symptoms: Evaluation criteria change after a favored option appears.

symptoms: Teams oscillate chaotically between ideation and critique without explicit phase intent.

symptoms: Downselection deletes rationale and reserves, making later reopening expensive.

symptoms: Prototypes confirm feasibility but do not test the assumptions that distinguished options.

symptoms: A decision meeting mixes idea generation, advocacy, scoring, and approval in one undifferentiated activity.

symptoms: Repeated iteration polishes a local optimum while the challenge frame remains untested.

root_tension: The process must suspend enough judgment to discover non-obvious alternatives while retaining enough guardrails, evidence, and decision discipline to converge safely and on time.

anti_signatures: Only one lawful or physically feasible action exists and exploration cannot change it.

anti_signatures: The task is execution of a settled design rather than search among alternatives.

anti_signatures: The main problem is correcting measured deviation from a known target; use convergence guidance or approximation-target mapping.

anti_signatures: The main problem is group agreement around a known option set; use consensus convergence.

anti_signatures: The option space is already broad and the only missing step is staged screening; use progressive narrowing.

anti_signatures: Safety or incident response requires immediate containment before any exploratory phase.

structural_intervention: Establish a governed cycle with distinct generative and evaluative modes, explicit breadth and transition gates, evidence-producing comparisons, provisional commitment, preserved alternatives, and feedback-triggered reopening of the relevant frame, dimension, or option family.

action_logic: Frame the challenge, decision horizon, non-negotiable constraints, and uncertainties without embedding a preferred solution.

action_logic: Declare the current mode and its rules so participants know whether they are expanding, testing, comparing, or committing.

action_logic: Diverge across independent dimensions, stakeholder perspectives, assumptions, scales, and concept families.

action_logic: Test expansion sufficiency with diversity, coverage, independence, and excluded-space checks rather than raw idea count.

action_logic: Preserve non-suspendable ethical, legal, physical, and resource guardrails while deferring ordinary preference filters.

action_logic: Select prototypes or evidence tasks that expose differences among option families and their governing assumptions.

action_logic: Reintroduce convergence criteria in a declared order, normalize evidence, and compare options without rewriting criteria around a favorite.

action_logic: Select, combine, sequence, hold, or reject options with recorded rationale, uncertainty, dissent, and reserve status.

action_logic: Commit provisionally at a level proportional to evidence and reversibility, then monitor implementation feedback.

action_logic: Reopen the problem frame, exploration dimensions, option pool, or criteria when a predeclared trigger fires.

decision_rules: Do not enter convergence until the breadth gate shows meaningful coverage across relevant dimensions and stakeholder perspectives.

decision_rules: Do not suspend non-negotiable safety, legality, rights, or feasibility guardrails during divergence.

decision_rules: Prefer criteria defined before option scoring; record any later criterion change and rescore consistently.

decision_rules: Use prototypes when verbal comparison cannot discriminate key assumptions, interactions, or implementation risks.

decision_rules: Preserve at least one credible reserve when commitment is expensive, uncertainty is high, or conditions may change.

decision_rules: Reopen only the narrowest layer invalidated by evidence unless feedback challenges the original problem frame.

decision_rules: Stop cycling when the selected option clears the decision threshold, residual uncertainty is acceptable, and further search has declining expected value.

decision_rules: Escalate when no option clears mandatory constraints rather than disguising failure through relative ranking.

target_invariants: Generative and evaluative modes remain distinguishable even when cycles are short.

target_invariants: Expansion is judged by meaningful difference and coverage, not idea volume alone.

target_invariants: Non-suspendable safety, legal, ethical, and rights constraints remain active.

target_invariants: Convergence criteria and evidence provenance remain visible and contestable.

target_invariants: Rejected and reserve options retain lineage, rationale, and reopening conditions.

target_invariants: Commitment strength remains proportional to evidence, uncertainty, and reversibility.

target_invariants: Dissent and minority evidence are not erased by aggregation.

target_invariants: Feedback can reopen the appropriate layer without resetting all learning by default.

expected_outcomes: More meaningfully distinct option families reach comparison before commitment.

expected_outcomes: Teams avoid both premature closure and indefinite ideation.

expected_outcomes: Selection rationale becomes traceable to criteria, prototypes, constraints, and stakeholder evidence.

expected_outcomes: Rejected options remain recoverable when assumptions or conditions change.

expected_outcomes: Iterations target invalidated assumptions instead of merely polishing the incumbent concept.

expected_outcomes: Creative range, decision quality, implementation readiness, and learning continuity improve together.

prose_essence:
The pattern is broader than brainstorming and broader than downselection. It controls the relationship between the two, including how criteria enter, what remains non-negotiable, what evidence is needed, what gets preserved, and when a choice is allowed to become provisional or durable.

Its compressed form is a governed alternation: frame the challenge, protect meaningful expansion, prove that breadth is sufficient, gather evidence that distinguishes option families, narrow through declared criteria, commit at a depth proportional to evidence and reversibility, preserve credible reserves, and reopen only the layer invalidated by later learning. Creativity and decision discipline are therefore not competing values; they are different modes with an explicit interface.

prose_when_to_use:
Use this archetype when a design challenge admits several plausible frames, architectures, concepts, or implementation paths and an early choice would create costly path dependence. It is especially useful when generation and evaluation require different roles or norms, when prototypes can expose differences among options, and when later evidence may require a bounded return to exploration.

It is also appropriate when a team repeatedly swings between unstructured ideation and premature approval, produces many cosmetic variants inside one hidden solution family, changes criteria after seeing a favorite, or loses rejected alternatives so thoroughly that every change in context forces a restart. The pattern earns its coordination cost when missing a materially different option is more consequential than running a bounded exploration phase.

prose_structural_problem:
Design work has to perform two incompatible jobs. It must create enough genuine variation to escape the first visible answer, and it must reduce that variation to a coherent, accountable commitment. Mixing the jobs suppresses novelty; separating them without a transition produces workshops full of ideas but no decision. This archetype governs the whole repeating cycle: frame, expand, test breadth, gather discriminating evidence, narrow, commit proportionally, retain reserves, and reopen the right layer when feedback changes the case.

The underlying tension is not simply “more ideas versus fewer ideas.” Divergence requires permission to challenge defaults, vary dimensions, and withhold ordinary preference judgments. Convergence requires comparable evidence, constraint awareness, accountability, and a willingness to exclude. Without a governed transition, whichever mode has more status or urgency colonizes the other: critique strangles novelty, or novelty postpones responsibility.

prose_intervention_logic:
Begin with a solution-neutral challenge frame, an explicit decision horizon, and a clear distinction between non-suspendable guardrails and ordinary criteria. Declare the generative mode, expand across independent dimensions and affected perspectives, and use a breadth gate based on structural difference rather than idea count. Only then commission prototypes or evidence tasks designed to discriminate among option families.

During convergence, reintroduce declared criteria in a controlled sequence, normalize evidence without hiding uncertainty or dissent, and record whether each option is selected, combined, sequenced, held, reserved, or rejected. Commitment remains provisional where evidence is weak or reversal is cheap. After implementation, predeclared triggers decide whether to continue, refine the incumbent, activate a reserve, reopen one solution dimension, or revisit the problem frame itself.

prose_key_components:
The **Challenge Scope Frame** and **Cycle Objective and Decision Horizon** specify what is open and what the current cycle must learn or decide. The **Divergence Phase Contract**, **Exploration Dimension Map**, and **Option Diversity Floor** protect meaningful breadth. The **Non-Suspendable Guardrail Set** prevents creative license from becoming safety or rights evasion.

The **Candidate Option Inventory** preserves alternatives and provenance. The **Convergence Criterion Set**, **Phase Transition Gate**, **Evidence and Prototype Plan**, and **Option Comparison Frame** turn breadth into a defensible choice. The **Convergence Commitment Record** and **Reserve Option Pool** distinguish selection from erasure. Finally, the **Reopening Trigger**, **Cycle Cadence and Budget**, and **Learning and Trace Record** make the cycle bounded, adaptive, and cumulative.

Four components intentionally reuse frozen indexed structures: Divergence Phase Contract, Non-Suspendable Guardrail Set, Reopening Trigger, and Learning and Trace Record. Their reuse connects this parent to accepted tactic-level archetypes without collapsing those neighbors.

prose_common_mechanisms:
The mechanisms range from macro-workflows to compact methods. A **Double-Diamond Workflow** runs paired problem and solution cycles. **Parallel Concept Sprints**, a **Morphological Matrix Then Scoring**, or **Assumption Reversal and Recombination** can create structurally different candidates. A **Prototype Tournament**, **Pairwise Option Comparison**, or **Multi-Criteria Decision Matrix** builds comparative evidence. A **Concept Screening Funnel** or **Stage-Gate Concept Review** controls investment depth. A **Reserve Option Board** and **Reopening Trigger Review** preserve adaptive capacity after commitment.

Mechanisms should be combined according to uncertainty and reversibility. A short, reversible choice may need only Two-Pass Generation and Evaluation plus a lightweight evidence gate. An expensive architecture decision needs independent concepts, discriminating prototypes, versioned criteria, explicit reserves, and a rigorous reopening rule.

prose_failure_modes:
Cosmetic divergence is the most common failure: many outputs differ in presentation while sharing one frame. Premature convergence follows when deadlines, hierarchy, or fatigue override the breadth gate. Criteria capture occurs when a preferred option changes the rubric. Performative prototypes demonstrate effort without testing differences. False convergence hides veto constraints, uncertainty, or dissent behind a single score. The option cemetery destroys recoverability. Endless cycling treats every objection as a reason to reopen.

The mitigations are structural: independent dimensions, explicit guardrails, versioned criteria, discriminating test questions, raw evidence retention, reserve records, decision horizons, and predeclared reopening thresholds.

prose_neighbor_distinctions:
**Evaluation Criteria Suspension During Divergence** protects generative work from premature filters. **Multi-Dimensional Solution Space Exploration** ensures breadth across independent axes. Both are direct and mature, but each ends at a convergence boundary. This archetype owns the lifecycle on both sides of that boundary.

**Progressive Narrowing** and **Variation Consolidation and Feature Selection** are convergence-side methods. **Coarse-to-Fine Search** changes search resolution. **Iterative Refinement Loop** improves an incumbent. **Consensus Convergence** aligns people, while **Convergence Guidance** steers toward a target. **False Convergence Prevention** audits apparent closure. **Adaptive Mutation Rate Management** tunes variation volume. **Branching and Merging** governs version integration. **Approximation-Target Divergence Mapping** closes gaps to a known target. None replaces a design cycle that can expand frames, generate options, compare evidence, commit provisionally, and reopen.

prose_cross_domain_examples:
The archetype fits an engineering architecture choice, a venture search over problem and business-model combinations, a public-service redesign with affected users, or a scenario-based strategy portfolio. In each case multiple meaningful alternatives can be generated, evidence can discriminate them, and later feedback can change the right layer of commitment.

In engineering design, teams can compare passive, active, and hybrid architectures with prototypes targeted at failure assumptions, then retain a fallback by reliability trigger. In entrepreneurship, they can vary customer, problem, channel, and business-model assumptions before choosing reversible market tests. In public services, affected users can shape exploration dimensions and access criteria before two delivery models enter a pilot. In strategy, independent postures can be tested across scenarios, producing a robust core and trigger-based contingent moves.

prose_non_examples:
It does not fit an emergency shutdown, a fixed-standard calibration correction, or a legally singular action. It also does not describe a popularity vote over cosmetic variants, serial polishing of one incumbent, or a consensus ritual whose outcome was predetermined.

A workshop that generates many sticky notes but never defines a transition or decision is only divergence. A scoring matrix applied to a predetermined favorite is only legitimization. Repeatedly revising one prototype is iterative refinement, not divergence-convergence, unless the process preserves and can reopen materially different option families. A safety response that must contain harm immediately should stabilize first and explore later.

prose_tradeoffs:
The pattern costs time and coordination. Broader exploration consumes attention and prototype budget. Explicit criteria can favor what is measurable. Parallel work increases integration cost. Reserves can weaken commitment. Reopening can invite political relitigation. These costs are managed through bounded cycles, breadth gates, discriminating evidence, declining-value stop rules, activation conditions, and narrow reopening.

Other tensions are unavoidable. Independent tracks reduce anchoring but duplicate effort. Formal comparison improves traceability but can create false precision. Stakeholder participation improves dimension coverage but extends cadence and may surface irreducible value conflict. Stronger stopping rules increase execution focus while raising the risk of missing late evidence. The design task is to expose and tune these tradeoffs, not to pretend the cycle eliminates them.

prose_invariants_to_preserve:
Several invariants may not change. Generative and evaluative modes must remain distinguishable. Breadth must reflect meaningful difference rather than idea count. Non-suspendable constraints remain active. Criteria and evidence must be traceable. Minority evidence and reserve options must survive aggregation. Commitment strength must match evidence and reversibility. Feedback must be able to reopen the narrowest invalidated layer without deleting all prior learning.

These invariants are the identity test for the archetype. A process may use a double-diamond diagram, workshops, prototypes, or scoring matrices and still fail to instantiate the pattern if evaluation leaks into generation, hard protections are waived, criteria become post hoc, or rejected options and dissent disappear. Conversely, a very short individual workflow can instantiate the pattern if the modes, evidence, and transition logic remain real.

prose_target_outcomes:
When the pattern works, more genuinely different option families reach comparison, decisions are tied to evidence rather than advocacy, and teams avoid both premature closure and endless ideation. Selection rationale remains inspectable, rejected concepts remain recoverable, and later iteration targets invalid assumptions instead of polishing a local optimum.

The desired result is not maximum variety or permanent optionality. It is a better-calibrated commitment: one that has faced credible alternatives, exposes why it won, preserves what could change the decision, and directs subsequent learning toward the uncertainties that still matter. The organization gains both decision velocity and recoverability because it does not have to choose between amnesia and indecision.

prose_parameter_dimensions:
Important parameters include cycle duration, option-family breadth, dimension coverage, participant independence, evidence cost, criterion weights, uncertainty tolerance, commitment reversibility, reserve capacity, and reopening sensitivity. These can change by domain and decision scale.

Tuning should follow stakes and reversibility. High-cost, hard-to-reverse decisions justify broader option families, more independent tracks, stronger evidence, and more formal gates. Cheap reversible decisions can use short cycles, lighter comparison, and rapid reopening. Increasing diversity without increasing comparison capacity creates overload; increasing gate rigor without protecting exploration creates incumbent bias.

prose_variants_and_near_names:
Four variants preserve the parent logic while changing cadence, evidence, or governance. **Double-Diamond Phase Architecture** couples one cycle for problem framing with another for solution development. **Rapid Ideation-Downselection Sprint** compresses the cycle for cheap and reversible commitments. **Evidence-Intensive Prototype Tournament** makes competing prototypes the dominant bridge between expansion and convergence. **Distributed Parallel Exploration and Recombination** protects independence by using semi-separate teams before synthesis.

Near names such as “expand-then-refine cycle,” “design exploration-selection cycle,” “divergent-convergent design cycle,” and “design search-and-commit cycle” point to the same parent when they include real phase separation, evidence-led narrowing, commitment records, and reopening rules. “Double diamond” is best treated as a temporal variant or mechanism-facing name, not as a second parent. Brainstorming and dot voting remain mechanisms because neither owns the full lifecycle.

Double-Diamond Phase Architecture

short_definition: Runs one divergence-convergence cycle for problem framing and another for solution development.

distinctive_feature: Two coupled diamonds separate problem-space and solution-space commitment.

distinct_from_parent: It fixes a two-cycle macro-architecture; the parent permits any evidence-driven number and nesting of cycles.

still_same_parent_because: It preserves the same expansion, transition, convergence, commitment, and reopening logic.

use_when: Both the problem definition and solution architecture remain uncertain.

example: Reframe the service need, then expand and select delivery concepts.

failure_modes: Treating the diagram as a fixed waterfall.

Rapid Ideation-Downselection Sprint

short_definition: Compresses generation and narrowing into a short cycle while retaining explicit mode separation.

distinctive_feature: Very short cadence with lightweight breadth and evidence gates.

distinct_from_parent: It narrows duration and documentation but not the intervention logic.

still_same_parent_because: The same phase, criteria, reserve, and reopening invariants remain.

use_when: Commitment is reversible and evidence can be gathered cheaply.

example: Generate interface concepts and choose two reversible tests in one session.

failure_modes: Using the timebox to justify cosmetic breadth.

Evidence-Intensive Prototype Tournament

short_definition: Advances multiple option families through discriminating prototypes before convergence.

distinctive_feature: Evidence generation is the dominant bridge between divergence and convergence.

distinct_from_parent: It requires competing prototypes; the parent can converge through analysis or field evidence.

still_same_parent_because: Prototypes implement the same governed evidence-and-selection transition.

use_when: Verbal reasoning cannot resolve important technical or user uncertainties.

example: Compare three architectures with targeted load and maintainability prototypes.

failure_modes: Testing build quality instead of differentiating assumptions.

name: Distributed Parallel Exploration and Recombination

short_definition: Uses semi-independent teams to develop distinct concepts before comparison and recombination.

distinctive_feature: Organizational separation protects option independence before a governed merge.

distinct_from_parent: It specifies a multi-team participation architecture.

still_same_parent_because: It instantiates the same breadth, evidence, convergence, and reserve logic.

source_primes: diversity composition

use_when: Anchoring or hierarchy would suppress independent exploration.

domain: strategy

example: Independent teams develop strategic postures before an evidence-first synthesis.

failure_modes: Premature cross-team leakage that recreates one dominant concept.

near_names:
Divergence-Convergence Cycle
Design Diverge-Converge Cycle
Expand-Then-Refine Cycle
Design Option Expansion and Narrowing
Design Exploration-Selection Cycle
Divergent-Convergent Design Cycle
Double-Diamond Cycle Governance
Idea Expansion and Downselection
Design Search and Commit Cycle
Explore-Then-Focus Design
Generative-Evaluative Phase Separation
Option Generation and Convergence
Design-Space Expansion and Selection
Iterative Diverge-Converge Protocol
Creative Expansion-Refinement Cycle
Lateral Thinking and Convergence Cycle