Residual-Driven Model Refinement

batch: Batch 17

aliases:
Residual Analysis
Residual-Structure Refinement Loop
Error-Structure Diagnosis
Unexplained Variation Analysis
Model Residual Diagnostics

slug: refinement

slug: validation

slug: decomposition

slug: comparison

slug: imputation

slug: stationarity

slug: robustness

slug: calibration

slug: observability

slug: causality

slug: feedback

slug: iteration

components: Evaluation Population and Alignment Boundary

mechanisms:
Residual-versus-Fitted Plot
Quantile-Quantile Residual Check
Autocorrelation and Whiteness Test
Heteroscedasticity and Scale Test
Subgroup Residual Heatmap
Influence and Leverage Diagnostic
Cross-Validated Error-Slice Report
Posterior-Predictive Residual Check
Control Chart on Residuals
Residual Comparison Test
Model-Revision Experiment Log
Residual Root-Cause Review

essence_one_line: Subtract what the best current explanation predicts, then treat reproducible structure in the remainder as evidence about what the explanation still misses.

compression_statement: Residual-Driven Model Refinement defines a reference explanation and a valid residual construction, separates expected noise from reproducible leftover structure, maps that structure across cases and regimes, generates explicit missing-structure hypotheses, permits only bounded revisions with predicted diagnostic consequences, and keeps a revision only when independent evidence reduces the targeted residual pattern without creating worse failures elsewhere.

core_intuition: A useful explanation can still be wrong in patterned ways. The average error may be near zero while errors curve with scale, cluster in time, reverse across groups, or concentrate at a boundary. Those patterns are not automatically truth, but they are higher-value clues than undifferentiated fit statistics. By making the remainder explicit and testing whether it is more structured than expected noise, the analyst gains a disciplined path from model criticism to model improvement without equating every anomaly with a new theory.

canonical_formula: r = y - E[y | M, x]; diagnose(r | uncertainty, time, slices, leverage, regime) -> missing-structure hypothesis -> bounded revision M' -> held-out test that targeted structure decreases and guardrail errors do not worsen.

structural_problem: The best available model, forecast, theory, calibration, or policy expectation explains enough to be used, yet its unexplained remainder is either discarded as noise, compressed into one average score, or mined opportunistically. As a result, systematic misspecification, omitted structure, regime dependence, measurement defects, and concentrated subgroup failures persist without a controlled learning path.

trigger_conditions: A reference explanation can generate case-level, time-level, or condition-level expectations that can be aligned with observations.

trigger_conditions: Global fit or average performance appears acceptable, but decision makers suspect local, sequential, subgroup, tail, or regime-specific failure.

trigger_conditions: The same signed or shaped errors recur beyond what the stated uncertainty model would normally produce.

trigger_conditions: A model or theory is being revised repeatedly without a transparent link between diagnostic evidence and each added term, rule, or exception.

trigger_conditions: Extrapolation, distribution shift, measurement change, or process drift may have moved observations outside the reference explanation's validated regime.

trigger_conditions: A high-stakes use requires evidence that residual risk is not concentrated in a vulnerable or rare slice.

symptoms: Residuals curve, fan out, cluster, alternate, drift, or remain correlated with predictors, context, sequence, or fitted value.

symptoms: Positive and negative errors cancel globally while one subgroup is persistently underpredicted and another overpredicted.

symptoms: A small number of influential observations determine both the fitted explanation and the apparent diagnostic conclusion.

symptoms: Repeated patches improve in-sample fit but fail on future, held-out, or neighboring regimes.

symptoms: Analysts delete anomalies, change transformations, or add features without recording which residual signature motivated the change.

symptoms: Uncertainty intervals are calibrated on average but systematically miss in particular operating regions.

symptoms: The model continues to issue familiar confidence signals after a measurement, policy, or data-generating regime has changed.

root_tension: The process must remain sensitive enough to learn from leftover structure while remaining conservative enough not to overfit stochastic variation, measurement artifacts, or post-hoc stories.

anti_signatures: There is no reference explanation or defensible expectation to subtract, so the task is exploratory description rather than residual analysis.

anti_signatures: The primary task is to map a known target state against a current approximation; use approximation_target_divergence_mapping.

anti_signatures: The inquiry is centered on a deliberately selected exceptional case and within-case causal tracing; use deviant_case_analysis.

anti_signatures: The mismatch is specifically a failure of correspondence between two theories in an overlap or limiting regime; use correspondence_violation_detection_and_theory_refinement.

anti_signatures: The primary intervention is to remove a temporal trend before interpreting the remaining series; use trend_detection_and_removal.

anti_signatures: The main task is to solve a tractable baseline and add ordered perturbative corrections; use solvable_baseline_decomposition.

anti_signatures: Only a one-off visualization or test is requested, with no hypothesis, revision, and revalidation loop; that is a mechanism, not this archetype.

structural_intervention: Create a versioned residual-learning loop that binds a reference explanation to a valid remainder, an explicit adequacy standard, a multiaxis diagnostic scan, a rival-hypothesis register, constrained revisions, and independent revalidation.

action_logic: State the reference explanation, its intended use, valid population or regime, assumptions, and the exact outcome or quantity it predicts.

action_logic: Align observations and expectations by unit, time, scale, information set, and treatment status before computing any remainder.

action_logic: Choose a residual construction appropriate to the model family and preserve the sign, scale, uncertainty, and version information needed for interpretation.

action_logic: Specify the uncertainty and noise envelope and the purpose-relative criteria for residual adequacy before broad exploratory scanning where practicable.

action_logic: Scan residuals across fitted value, features, time, space, sequence, subgroup, regime, and leverage while controlling for sample support and multiplicity.

action_logic: Separate reproducible structure from data corruption, label error, alignment defects, influential cases, and artifacts of the residual definition.

action_logic: Translate the surviving signature into explicit rival hypotheses that predict what should change in both the residuals and the substantive process.

action_logic: Authorize only bounded revisions tied to those predictions, with complexity, interpretability, safety, and invariance constraints recorded in advance.

action_logic: Re-estimate or rebuild the reference and test the targeted residual signature on held-out, future, replicated, or independently generated evidence.

action_logic: Retain the revision only when it improves the intended residual behavior without unacceptable regression elsewhere; otherwise revert, narrow the use, gather new evidence, or replace the explanation.

action_logic: Stop when residuals are adequate for the decision, further cycles have diminishing evidential value, or the remaining gap is explicitly accepted and monitored.

decision_rules: Do not interpret a residual until the reference model, data version, alignment rule, and residual definition are fixed and traceable.

decision_rules: Treat a pattern as a refinement lead only when it exceeds the expected noise envelope, has adequate support, and is reproducible across a defensible resampling, replication, or future window.

decision_rules: Prefer the smallest revision that predicts a specific diagnostic improvement and preserves required invariants.

decision_rules: Do not delete influential observations solely to improve fit; first classify them as error, valid rare regime, boundary evidence, or unresolved case.

decision_rules: When many slices or diagnostics are explored, lower confidence, apply multiplicity controls, or require independent confirmation before action.

decision_rules: Reject revisions that improve the discovery sample but fail held-out revalidation, degrade consequential slices, or rely on information unavailable at use time.

decision_rules: Escalate from refinement to model replacement when residual structure persists across reasonable revisions or exposes a false reference frame.

decision_rules: Narrow or suspend high-stakes use when residual adequacy cannot be demonstrated for the affected population or regime.

target_invariants: Residuals remain explicitly relative to a named and versioned reference explanation.

target_invariants: Observation-to-expectation alignment does not leak future or post-outcome information.

target_invariants: Uncertainty, sample support, and exploratory multiplicity remain visible in pattern claims.

target_invariants: Revisions preserve required causal, semantic, physical, legal, fairness, or safety constraints.

target_invariants: Discovery evidence and confirmation evidence remain separated where feasible.

target_invariants: Prior versions, rejected hypotheses, and unresolved residual risks remain auditable.

target_invariants: Global improvement cannot silently override severe degradation in a consequential slice.

target_invariants: Stopping with acknowledged residual uncertainty remains an acceptable outcome.

expected_outcomes: Systematic error becomes localized by shape, condition, subgroup, time, or regime rather than hidden in an average metric.

expected_outcomes: Model changes become traceable to explicit evidence and predicted diagnostic consequences.

expected_outcomes: Omitted interactions, nonlinearities, regime shifts, measurement problems, and data defects are distinguished more reliably.

expected_outcomes: Overfitting pressure falls because revisions must survive held-out residual checks and guardrail comparisons.

expected_outcomes: High-consequence local failures are discovered earlier and can trigger use restrictions or targeted remediation.

expected_outcomes: The organization accumulates a reusable history of residual signatures, tested hypotheses, and model-validity boundaries.

expected_outcomes: Decision makers gain a calibrated statement of what the explanation captures, what remains unexplained, and where confidence should be reduced.

prose_tradeoffs:
- More diagnostic dimensions reveal local misspecification but increase false-discovery risk, documentation burden, and the need for independent confirmation.
- More flexible revisions can remove residual structure but reduce interpretability, transportability, and confidence that the improvement reflects a real mechanism.
- Holding out more data strengthens confirmation but leaves less information for estimating rare regimes and small groups.
- Strict adequacy thresholds improve safety but may make useful models unavailable in settings where perfect explanation is impossible.
- Rich provenance and versioning improve auditability but slow rapid iteration and can expose sensitive slice information if access controls are weak.
- Domain-expert interpretation can connect shapes to mechanisms but may also import compelling post-hoc stories unless predictions are recorded before revalidation.
- Preserving influential or boundary cases improves external validity but can make estimates less stable and require specialized models or explicit use restrictions.
- A global model is simpler to govern, while local or regime-specific models may fit better but fragment maintenance, monitoring, and accountability.

Regression Residual Diagnostics

short_definition: Use residual shape, scale, distribution, and influence diagnostics to test and refine a regression-style explanation.

distinctive_feature: The remainder is explicitly defined relative to a fitted conditional mean or related regression quantity, with leverage and functional-form diagnostics central.

distinct_from_parent: The parent also covers forecasts, simulations, counterfactual expectations, process baselines, and probabilistic generative models; this variant specializes regression families.

still_same_parent_because: It follows the same reference, residual construction, adequacy, pattern, hypothesis, bounded revision, and revalidation cycle.

use_when: A response is modeled as a function of predictors and the fitted relationship is being used for inference, prediction, or calibration.

use_when: Linearity, variance, distributional, independence, or influence assumptions may be wrong.

use_when: A revision can be tested against held-out observations or pre-specified diagnostics.

example_domains: statistics econometrics epidemiology engineering social_science

domain: econometrics

example: A wage model's residuals curve with experience and fan out by firm size, motivating a bounded nonlinear and scale revision tested out of sample.

domain: engineering

example: A calibration equation is globally accurate but leaves signed residual bands at high temperature, revealing a regime-dependent correction.

failure_modes: Selecting transformations after looking at the same residuals and reporting ordinary inferential uncertainty as though the model were pre-specified.

failure_modes: Deleting high-leverage cases solely because they weaken fit.

failure_modes: Treating normal-looking residuals as proof that the causal interpretation is correct.

Forecast Innovation Diagnostics

short_definition: Treat forecast errors or innovations as a time-ordered stream whose remaining predictability diagnoses lag, seasonality, drift, regime change, or miscalibration.

distinctive_feature: Temporal dependence, forecast horizon, information availability, and regime stability are part of the residual definition rather than incidental slices.

distinct_from_parent: The parent is not inherently time ordered; this variant requires no-leakage alignment and sequential diagnostics.

still_same_parent_because: Forecast errors remain model-relative leftovers used to generate bounded, testable revisions.

source_primes: residual_analysis stationarity uncertainty validation

use_when: Predictions are issued sequentially and evaluated after outcomes arrive.

use_when: Residual order, horizon, season, or regime is decision-relevant.

use_when: The forecast can be backtested through rolling or forward-chaining evaluation.

example: Demand forecast errors cluster after promotions and remain autocorrelated for three days, exposing a lagged intervention effect.

example: Forecast innovations shift positive after a reporting-policy change, prompting a regime marker rather than an undifferentiated refit.

failure_modes: Using future information when constructing features or residuals.

failure_modes: Calling every temporary shock a permanent regime change.

failure_modes: Averaging across forecast horizons that have different error structure.

Error-Slice Model Audit

short_definition: Audit whether an apparently adequate model leaves concentrated signed error or uncertainty in consequential populations, contexts, or operating regimes.

distinctive_feature: The decision unit is the residual distribution within consequential slices, not only the global average error.

distinct_from_parent: This variant strengthens slice governance, multiplicity control, support thresholds, and harm-sensitive adequacy criteria.

still_same_parent_because: It uses the same model-relative residual evidence and bounded revalidation cycle.

use_when: Aggregate performance may conceal subgroup, geography, device, language, tail, or rare-event failures.

use_when: Errors have asymmetric safety, fairness, or service consequences.

use_when: Slice definitions and small-sample uncertainty can be documented.

domain: healthcare

example: A risk score is calibrated overall but persistently underpredicts deterioration in one device-and-language slice, triggering data and model review.

example: A wait-time model alternates over- and underprediction by neighborhood, exposing an omitted routing constraint.

failure_modes: Data dredging across many tiny slices without uncertainty or multiplicity control.

failure_modes: Publishing sensitive small-group results that create re-identification risk.

failure_modes: Using technical parity targets without considering the consequences of false positives and false negatives.

Counterfactual Residual Review

short_definition: Study the difference between observed outcomes and an explicit counterfactual expectation to refine the causal or policy explanation behind the gap.

distinctive_feature: Residual meaning depends on an unobserved comparison state and therefore on identification, interference, and counterfactual validity assumptions.

distinct_from_parent: The parent also handles directly observed predictions; this variant requires explicit causal-baseline governance.

still_same_parent_because: The counterfactual gap is still a structured remainder used to test and refine the explanatory model.

use_when: The residual is defined against a no-intervention, alternative-policy, synthetic-control, matched, or otherwise counterfactual baseline.

use_when: Identification assumptions and spillovers can be stated and challenged.

use_when: The purpose is to learn why estimated effects vary rather than merely to report an average effect.

example: Treatment-minus-synthetic-control residuals diverge only in regions with a complementary service, motivating an interaction hypothesis.

example: Observed throughput gains fall below the counterfactual estimate on high-mix lines, revealing an omitted setup-time constraint.

failure_modes: Treating counterfactual residual structure as observed fact when the baseline is weakly identified.

failure_modes: Searching for effect modifiers until a persuasive story appears.

failure_modes: Ignoring spillovers that invalidate the comparison unit.

near_names:
Residual Analysis
Residual-Structure Refinement Loop
Error-Structure Diagnosis
Model Residual Diagnostics
Unexplained Variation Analysis


Residual Harm Accounting and Allocation

type: archetype

source: generated

batch: Batch 01

canonical_family: Adaptation Under Adversarial Pressure

aliases:
Residual Harm Governance
Residual Loss Accounting
Loss Allocation Governance
Damage Bearer Assignment
Residual Damage Policy

slug: risk

slug: accountability

slug: allocation

slug: externality

slug: equity

slug: proportionality

slug: containment

slug: resilience

slug: uncertainty

slug: boundary

slug: remediation

components:
Defense-Layer Context Map
Residual Harm Boundary
Loss-Channel Ledger
Affected Bearer Map
Causal Attribution and Uncertainty Note
Responsibility and Funding Assignment
Policy Response Portfolio
Distributional and Equity Review
Remediation or Compensation Path
Residual Acceptance Record
Feedback to Defense Layers
Dispute and Revision Process
Evidence Preservation Protocol
Temporal Loss Horizon
Non-Monetary Loss Register

mechanisms:
Loss and Damage Register
Post-Incident Residual-Loss Assessment
Claims and Compensation Fund
Residual Harm Eligibility Rule
Adaptation Gap Report
Harm-Bearer Agreement
Managed Retreat or Relocation Package
Residual-Risk Acceptance Signoff
Restorative Remedy Plan
After-Action Loss Feedback Review

essence_one_line: Name, measure, assign, and govern the harm that remains after defenses have done what they can.

compression_statement: Residual Harm Accounting and Allocation treats loss after mitigation, adaptation, prevention, or defense not as a rounding error or public-relations residue, but as its own governance object. The archetype maps the control layers already tried, defines which harms are truly residual, records loss channels and affected bearers, handles attribution uncertainty, assigns responsibility or funding, chooses a response policy, and feeds the evidence back into future prevention.

core_intuition: A system can be good at defense and still leave someone holding the damage. If that remainder is not named, it will be miscounted, shifted, moralized, or forgotten. Residual harm needs a ledger, a bearer, a remedy path, and a learning loop.

canonical_formula: governed_residual_harm = ledger(loss_channels_after_defenses) + bearer_assignment + policy_response + dispute_review + prevention_feedback

structural_problem: A hazard, intervention, failure, or environmental change passes through one or more layers of defense, mitigation, adaptation, containment, or repair. The remaining harm is real, but it is easy for the system to treat it as unavoidable background noise, an unowned externality, an accounting afterthought, or a problem for the least powerful affected parties. Without an explicit residual-harm object, the system cannot decide who bears the damage, who pays, what is repaired, what is accepted, and what should change upstream.

trigger_conditions: Prevention, mitigation, adaptation, containment, or compensation has reduced but not eliminated harm.

trigger_conditions: Multiple layers of defense make it hard to see where remaining loss belongs.

trigger_conditions: Harm channels differ in reversibility, measurability, affected bearers, time horizon, or remedy type.

trigger_conditions: Decision makers need to distinguish preventable future harm from residual harm that must be governed now.

trigger_conditions: The harmed parties, funding sources, responsible actors, and policy owners are not the same entities.

trigger_conditions: Non-monetary, delayed, or distributed harms risk being omitted because they are hard to price or prove.

trigger_conditions: Existing risk registers track possible future loss but do not handle actual residual damage after defenses fail or saturate.

symptoms: Reports celebrate mitigation success while affected parties still carry uncataloged damage.

symptoms: Residual loss is described vaguely as unfortunate, unavoidable, or external rather than assigned to a ledger and policy path.

symptoms: Compensation, repair, insurance, relocation, or acceptance decisions are made case by case with no stable criteria.

symptoms: The same damage is either double counted across programs or not counted at all.

symptoms: Losses are shifted to weaker groups, future parties, ecosystems, customers, or downstream operators because they are less visible.

symptoms: Incident closure focuses on technical containment while downstream harm remains unresolved.

symptoms: Preventable harms are prematurely relabeled as residual to avoid redesign or responsibility.

symptoms: Disputes over attribution paralyze all response, even where partial or probabilistic responsibility would be enough for action.

root_tension: The system must avoid two opposite errors: treating preventable harm as an accepted residual, and treating irreducible residual harm as if it will vanish through another round of prevention rhetoric. The remainder must be governed without normalizing it.

anti_signatures: The issue is only forecasting future risk before harm occurs; probabilistic risk weighting or failure mode anticipation may be closer.

anti_signatures: The issue is only monitoring how risk decays after a trigger event; residual_risk_decay_tracking may be closer.

anti_signatures: The issue is a reversible workflow correction or make-whole action; compensating_transaction may be closer.

anti_signatures: The issue is assigning spillover costs to the actor who created them before they pass through defense layers; externality_internalization may be closer.

anti_signatures: The issue is ex ante assessment of winners and losers from a policy; welfare_analysis_and_distributional_effects_assessment may be closer.

anti_signatures: The issue is containing a rupture before damage spreads; rupture_containment may be closer.

anti_signatures: There is no remaining harm to account for, only a theoretical exposure or a preference about risk posture.

structural_intervention: Create a residual-harm governance loop: map the defense context, declare the residual boundary, ledger loss channels, identify bearers, document attribution uncertainty, assign responsibility and funding, choose a response policy, preserve dispute rights, and route learning back into prevention and adaptation.

action_logic: Name the hazard, intervention, failure, or environmental process that produced the residual harm.

action_logic: Map the defense, mitigation, adaptation, containment, or repair layers already present or attempted.

action_logic: Separate harm that is still preventable or remediable from harm that must now be treated as residual.

action_logic: Decompose the residual into loss channels with magnitude, time horizon, reversibility, evidence quality, and affected bearers.

action_logic: Record causal attribution and uncertainty without requiring impossible certainty before action can begin.

action_logic: Assign each loss channel to a responsible owner, funding source, affected bearer, or explicit acceptance authority.

action_logic: Choose a policy response for each channel: further prevention, repair, compensation, restoration, relocation, transfer, memorialization, monitoring, or accepted burden.

action_logic: Check whether the proposed allocation pushes residual harm onto less powerful or less visible parties.

action_logic: Provide a challenge and revision route for affected parties and for new evidence.

action_logic: Feed residual-loss evidence back into defense-layer design so the same loss is not simply normalized.

decision_rules: If a harm is still reasonably preventable or reducible, route it upstream before recording it as residual.

decision_rules: If a harm remains after controls, put it on the ledger even when its monetary value, attribution, or remedy is uncertain.

decision_rules: If the bearer of harm differs from the actor controlling the hazard, require an explicit responsibility, funding, transfer, or accountability rule.

decision_rules: If the response is compensation, check that it does not become a license to repeat preventable harm.

decision_rules: If attribution is probabilistic, use evidence thresholds, shared funds, precautionary rules, or proportional allocation rather than forcing a false all-or-nothing decision.

decision_rules: If the affected bearer lacks voice, capacity, or bargaining power, require a heightened distributional and legitimacy review.

decision_rules: If residual harm is accepted without repair, record the authority, rationale, affected parties, alternatives considered, and review trigger.

decision_rules: If residual harms recur, reopen defense-layer design rather than treating the ledger as a permanent disposal channel.

target_invariants: Residual harm is explicit, not hidden inside aggregate risk or success metrics.

target_invariants: The residual boundary is revisable and does not launder preventable harm.

target_invariants: Every loss channel has at least a provisional affected bearer and policy status.

target_invariants: Uncertainty is recorded without making action impossible.

target_invariants: Non-monetary and delayed harms are visible even when they are hard to price.

target_invariants: Allocation decisions remain contestable by affected parties.

target_invariants: The response does not reward repeated negligent production of residual harm.

target_invariants: Learning flows back to prevention, mitigation, adaptation, and defense layers.

expected_outcomes: Residual losses become visible enough to govern, compare, fund, repair, or consciously accept.

expected_outcomes: Affected parties have clearer routes to compensation, remediation, recognition, or appeal.

expected_outcomes: Decision makers distinguish residual harm from preventable harm and from merely predicted risk.

expected_outcomes: Defense-in-depth and adaptation programs learn from the damage that still passes through.

expected_outcomes: Moral hazard, blame laundering, and silent loss shifting become easier to detect.

expected_outcomes: Policy portfolios can address heterogeneous loss channels rather than forcing one generic remedy.

prose_essence: Residual Harm Accounting and Allocation is for the moment after defenses, mitigation, adaptation, or repair have done what they can and someone still carries damage. The archetype makes that remainder visible as a governable object rather than an embarrassing residue. It asks: what harm passed through, who bears it, what response is owed or accepted, and what must change upstream so the same remainder is not normalized?

prose_structural_problem:
Layered defenses can create a misleading sense of completion. A levee holds for some neighborhoods but not others; a security program contains the breach but customers still suffer; a patient-safety system catches many errors but one patient is harmed; a platform removes most abuse while particular users carry severe residual damage. When success metrics center on the defense layer, the loss that survives it can become nobody’s formal problem.

The structural problem is not just that harm happened. It is that the harm sits after the main governance story: after prevention, after mitigation, after containment, after adaptation, after incident response. That position makes it easy to call the harm unfortunate, external, unavoidable, or already handled. The archetype creates a second governance object for the remainder.

prose_key_components:
### Defense-Layer Context Map

Describes the mitigation, adaptation, prevention, containment, or defense layers through which harm has already passed. The residual status of the harm only makes sense relative to the controls that were attempted, absent, overwhelmed, bypassed, or deemed infeasible.

### Residual Harm Boundary

Separates preventable or still-mitigable harm from harm that remains after reasonable control layers have been applied or exhausted. This boundary prevents actors from prematurely labeling avoidable damage as residual, while also preventing real residual harm from disappearing into vague risk language.

### Loss-Channel Ledger

Records residual harms as named channels, magnitudes, time horizons, reversibility classes, and evidence states rather than as an undifferentiated aggregate loss. A ledger makes the accounting unit explicit: deaths, displacement, ecological loss, lost output, trust damage, service interruption, cultural loss, and other channels may need different remedies.

### Affected Bearer Map

Identifies who or what actually bears each residual harm, including displaced parties, future parties, ecosystems, customers, operators, or institutions. Without a bearer map, loss becomes rhetorically acknowledged but operationally unowned.

### Causal Attribution and Uncertainty Note

Documents what is known, contested, probabilistic, or unknowable about the connection between hazard, defense layers, residual harm, and responsible actors. The archetype needs enough attribution to govern action without pretending that every residual loss has a clean single cause.

### Responsibility and Funding Assignment

Assigns decision authority, financing, repair responsibility, or accepted burden for each residual-harm channel. This component turns recognition into governance: someone must have authority and resources to compensate, repair, absorb, transfer, reduce, or explicitly accept the residual harm.

### Policy Response Portfolio

Defines the available responses to each loss channel, such as prevention escalation, compensation, restoration, relocation, insurance, reserve drawdown, redesign, memorialization, or explicit acceptance. Residual harm is rarely solved by one response. Different channels may require financial, operational, ecological, legal, symbolic, or adaptive remedies.

### Distributional and Equity Review

Checks whether residual harms and response burdens are being pushed onto weaker, less visible, less represented, or less able bearers. A technically accurate residual-loss ledger can still be unjust if the policy allocates the remaining harm to those least able to contest or absorb it.

### Remediation or Compensation Path

Provides the concrete route by which eligible residual losses are repaired, offset, compensated, substituted, or otherwise addressed. This path may involve funds, service credits, replacement assets, relocation support, ecological restoration, apologies, cure provisions, or other make-whole procedures.

### Residual Acceptance Record

Records any harm that is consciously accepted, not compensated, not repaired, or deferred, together with the rationale and review trigger. Explicit acceptance is safer than silent abandonment, but it must be reviewable and cannot be used to launder preventable harm.

### Feedback to Defense Layers

Routes residual-loss evidence back into mitigation, adaptation, prevention, design, and preparedness decisions. Loss accounting should reduce future residual harm, not merely normalize paying for repeated damage.

### Dispute and Revision Process

Allows affected parties to challenge the ledger, attribution, eligibility, valuation, bearer assignment, or chosen policy response. Residual harm is often politically and ethically contested; a revision path preserves legitimacy and corrects undercounting.

prose_common_mechanisms:
### Loss and Damage Register

Type: `accounting_artifact`. A structured record of residual harm channels, evidence, affected bearers, response status, and unresolved gaps.

### Post-Incident Residual-Loss Assessment

Type: `assessment_protocol`. A review after a hazard, breach, failure, disaster, or policy intervention that separates prevented loss from residual loss and assigns follow-up actions.

### Claims and Compensation Fund

Type: `financing_mechanism`. A funded route for eligible residual losses to be paid, repaired, or supported without renegotiating every case from zero.

### Residual Harm Eligibility Rule

Type: `governance_rule`. A rule that determines which harms qualify for residual-harm treatment, which are still preventable, and which require another remedy path.

### Adaptation Gap Report

Type: `planning_report`. A report that identifies where existing adaptation, mitigation, or defense measures leave residual harms unhandled.

### Harm-Bearer Agreement

Type: `agreement_or_contract`. A formal allocation of who carries, pays for, insures, repairs, or governs specific residual harms.

### Managed Retreat or Relocation Package

Type: `remediation_program`. A program that addresses residual place-based loss where continued defense or restoration is infeasible or unjust.

### Residual-Risk Acceptance Signoff

Type: `decision_record`. A signed record that a residual risk or harm is knowingly accepted, including who has authority to accept it and when it must be reviewed.

### Restorative Remedy Plan

Type: `remediation_plan`. A plan for repair, service restoration, ecological restoration, apology, trust rebuilding, or other non-purely-monetary redress.

### After-Action Loss Feedback Review

Type: `learning_review`. A learning review that uses residual-loss evidence to strengthen prevention, defense-in-depth, adaptation, and early-warning layers.

prose_neighbor_distinctions: This archetype is close to residual risk monitoring, compensating transactions, externality internalization, and welfare analysis, but it is not identical to any of them. Residual risk monitoring follows exposure over time; this archetype governs actual harm. Compensating transactions are one remedy path; this archetype decides which residual harms need compensation, repair, transfer, acceptance, or prevention feedback. Externality internalization pulls spillovers into decision boundaries; this archetype handles the remainder that still passes through. Welfare analysis can forecast distributional effects; this archetype records and governs the residual loss that actually lands.

prose_non_examples: A risk register before harm occurs is not this archetype. A normal rollback of a reversible workflow error is closer to compensating transaction. An insurance price by itself is not enough because it may transfer money risk without recording affected-party harm or non-economic loss. A containment plan that stops damage propagation is a neighbor, but it does not decide what happens to damage already borne downstream.

prose_invariants_to_preserve: Residual harm must remain explicit, bearers must be named, uncertainty must be documented without paralyzing response, and acceptance must be reviewable. The archetype must not become a mechanism for laundering preventable harm into an accepted residue. Every ledger should preserve a route back to stronger prevention, mitigation, adaptation, or containment.

Climate Loss-and-Damage Governance

short_definition: A climate and disaster-policy variant for accounting for losses that remain after mitigation and adaptation, then assigning finance, support, repair, retreat, or accepted burden.

distinctive_feature: The source domain foregrounds irreversible, intergenerational, distributive, and non-monetary harms that survive mitigation and adaptation.

distinct_from_parent: The parent is cross-domain residual-harm governance; this variant emphasizes climate attribution, adaptation gaps, finance, relocation, non-economic loss, and intergenerational equity.

still_same_parent_because: The structural logic remains residual harm after defense layers, explicit accounting, bearer assignment, and policy response.

use_when: Climate, disaster, or ecological harms remain after feasible mitigation, adaptation, and protective infrastructure.

use_when: Affected communities need a route for support that is not reducible to ordinary insurance, generic development aid, or future prevention.

use_when: Irreversible and non-economic losses need to be named rather than treated as zero because they are hard to price.

domain: climate policy

example: A coastal community records residual land loss after adaptation measures and routes it to relocation support, cultural-loss recognition, and future defense redesign.

failure_modes: Finance becomes symbolic while actual residual losses remain unaddressed.

failure_modes: The label “loss and damage” is used to avoid prevention duties.

Post-Incident Residual-Loss Allocation

short_definition: An incident-response variant that accounts for harm remaining after containment, remediation, and service restoration, then assigns repair and bearer responsibilities.

distinctive_feature: The temporal anchor is after an event, breach, outage, error, or disaster rather than during policy design.

distinct_from_parent: The parent covers any residual harm after defenses; this variant centers incident closure and residual obligations after response work.

still_same_parent_because: The remaining task is still to account for residual harm, assign bearers, and choose a policy response.

use_when: An incident response has contained the acute failure but customers, patients, residents, workers, or downstream systems still bear residual harm.

use_when: The organization must distinguish direct repair, compensation, apology, risk acceptance, and prevention feedback.

domain: cybersecurity

example: After a breach is contained, the organization records residual customer harm, provides monitoring and reimbursement, and updates access controls.

failure_modes: Declaring the incident closed because the technical breach stopped while affected parties still carry unresolved loss.

Residual Harm Acceptance Case

short_definition: A governance variant that explicitly documents why a residual harm will not be further prevented, compensated, transferred, or repaired at present.

distinctive_feature: The central action is legitimate explicit acceptance of residual harm rather than compensation or repair.

distinct_from_parent: The parent includes many response policies; this variant focuses on the ethically risky case of choosing acceptance.

still_same_parent_because: Acceptance still depends on residual-harm accounting, bearer mapping, policy choice, and review.

source_primes: loss_and_damage risk accountability proportionality

use_when: A system has remaining harm that decision makers believe cannot feasibly be eliminated or remedied now.

use_when: Silent non-action would be less legitimate than explicit acceptance with authority, reasons, review triggers, and affected-party visibility.

domain: engineering safety

example: A safety case records a low-probability residual harm, why further control is infeasible, who may accept it, and when the decision must be revisited.

failure_modes: Acceptance becomes a euphemism for abandonment of weak affected parties.

Non-Economic Loss Recognition

short_definition: A variant that prevents residual harms from being ignored merely because they are hard to price, such as cultural, ecological, dignity, trust, or place-based loss.

distinctive_feature: The main challenge is non-commensurable or partially commensurable harm, not just financing a known monetary loss.

distinct_from_parent: The parent includes monetary and non-monetary harms; this variant focuses on preserving non-economic loss as a named object of governance.

still_same_parent_because: The pattern still treats residual harm as an accounting unit with a bearer and policy response.

use_when: Residual harm includes goods that cannot be fully restored or valued through market price.

use_when: A purely monetary ledger would erase or underweight the most important loss channels.

domain: community recovery

example: A recovery plan records loss of place and cultural continuity even when no market-value replacement can fully restore them.

failure_modes: Recognition becomes purely symbolic and never influences resources, duties, or future prevention.

near_names:
Residual Harm Governance
Residual Loss Accounting
Loss Allocation Governance
Damage Bearer Assignment
Uncompensated Loss Management


Residual Risk Decay Tracking

batch: First Iteration/Batch 18/Wave 2

aliases:
Risk Decay Tracking
Post-Event Risk Decay
Residual Risk Monitoring
Risk Clearance Window
Decay-Based Control Release
Post-Incident Risk Tapering

related_primes: uncertainty threshold feedback boundedness causality state_and_state_transition accountability data_integrity confidence_intervals

slug: threshold

slug: boundedness

components:
Trigger Event Record
Residual Risk Signal
Risk Decay Curve
Control Release Threshold
Control Intensity Ladder
Monitoring Cadence
Reentry Condition
Validation Check
Exception Trigger
Audit Trail
Uncertainty Buffer
Safeguard Floor
Stakeholder Impact Review
Control Expiration Rule
Risk Classification Scheme

mechanisms:
Risk Decay Dashboard
Post-Incident Monitoring Plan
Control Release Review
Quarantine Duration Protocol
Cooling-Off Period Policy
Probation Review Schedule
Fraud Risk Decay Model
Access Restoration Ladder
Clearance Certificate
Release Threshold Checklist

essence_one_line: Track how risk decays after an event so restrictions, monitoring, or reentry can be timed appropriately.

compression_statement: When a past event leaves residual risk that declines rather than vanishes, treat risk as a decaying state: monitor the remaining risk, set release thresholds, step controls down as evidence accumulates, and validate reentry so controls are neither lifted too early nor kept too long.

core_intuition: Risk after a triggering event is often neither zero nor permanent. A breach, exposure, conflict, contamination, anomaly, or unsafe action may leave a tail of danger that fades with time, remediation, clean observations, or changed conditions. The archetype replaces binary thinking with a governed decay curve so release decisions are evidence-sensitive and accountable.

canonical_formula: trigger event + residual risk signal + decay curve + control ladder + release threshold + validation check → timely, accountable control release

structural_problem: A prior event leaves continuing risk, but the governing process treats that risk as either fully present or fully absent. This causes premature release when residual risk is underestimated, or excessive restriction when risk decline is ignored.

trigger_conditions: A triggering event, exposure, incident, decision, anomaly, conflict, contamination, or violation has created residual risk.

trigger_conditions: The residual risk plausibly changes over time or with remediation, clean observations, repeated checks, or changed conditions.

trigger_conditions: There are controls that can be maintained, reduced, stepped down, or released as risk declines.

trigger_conditions: Both early release and overlong restriction create meaningful costs, hazards, fairness issues, or operational burdens.

trigger_conditions: Risk can be monitored directly or through credible proxy signals.

trigger_conditions: A release or reentry decision must be justified to stakeholders, regulators, operators, or affected parties.

symptoms: Restrictions remain in place because nobody owns the release threshold.

symptoms: Controls are lifted because a fixed time elapsed, even though residual risk was not rechecked.

symptoms: A reentry or permission decision depends on gut feel rather than a traceable risk signal.

symptoms: Monitoring intensity never tapers after an incident, creating alert fatigue or review burden.

symptoms: Different reviewers make inconsistent clearance decisions for similar post-event risks.

symptoms: A system repeatedly oscillates between overrestriction and premature normalization.

symptoms: Stakeholders cannot tell whether a probation, quarantine, cooldown, or heightened monitoring period is safety work or inertia.

root_tension: The system needs protection while residual risk remains, but protection itself imposes cost, delay, restriction, stigma, workload, or lost opportunity. The core tension is between safety from premature release and fairness or efficiency from timely release.

anti_signatures: The risk is not expected to decay or improve with time, remediation, or evidence; use containment, elimination, redesign, or permanent control instead.

anti_signatures: The issue is only scheduling a repeated action around a decaying residual state; use Half-Life-Based Timing unless risk governance and control release are central.

anti_signatures: The issue is returning a system through a transition without a risk-decay model; use Controlled Reentry or a staged transition archetype.

anti_signatures: The issue is uncertainty before any triggering event; use risk assessment, scenario planning, or monitoring rather than residual-risk decay tracking.

anti_signatures: The control has no meaningful release decision; if nothing can be adjusted, the archetype cannot operate.

anti_signatures: The domain requires authoritative legal, clinical, safety, or ethical judgment that cannot be replaced by a generic decay heuristic.

structural_intervention: Name the triggering event, define the residual risk signal, estimate or observe risk decay, map control levels to risk thresholds, monitor at an appropriate cadence, validate release conditions, and preserve an audit trail for the step-down or reentry decision.

action_logic: Record the triggering event and the specific residual risks it creates.

action_logic: Classify which risks decay, which remain constant, which may recur, and which require permanent redesign or containment.

action_logic: Select direct or proxy residual-risk signals that can be checked over time.

action_logic: Define a decay curve or staged risk model that includes uncertainty and exception triggers.

action_logic: Map risk levels to a ladder of controls, such as restriction, monitoring, conditional access, ordinary operation, or closure.

action_logic: Set release thresholds and validation checks before controls are reduced.

action_logic: Review residual risk at a cadence that matches both expected decay and consequence severity.

action_logic: Step controls down when evidence satisfies the threshold; slow, hold, or reverse release when exception triggers appear.

action_logic: Document the reasoning, evidence, approvals, and remaining safeguard floor.

decision_rules: Do not release controls solely because time elapsed; require residual-risk evidence or a justified conservative rule.

decision_rules: Do not maintain controls indefinitely without active renewal, exception evidence, or an explicit non-decaying risk classification.

decision_rules: Use stricter thresholds when consequences of premature release are severe or monitoring is weak.

decision_rules: Use stepped release when binary release would create either excessive restriction or excessive exposure.

decision_rules: Reassess the decay model when new incidents, failed checks, changed exposure conditions, or new information appears.

decision_rules: Separate safety logic from punishment, stigma, convenience, politics, or administrative inertia.

decision_rules: In rights-sensitive, medical, legal, or safety-critical contexts, require qualified review and domain-specific standards.

target_invariants: Residual risk must be treated as a changing state, not as a binary label.

target_invariants: Control release must be tied to explicit thresholds and validation evidence.

target_invariants: Controls must remain proportionate to remaining risk and consequence severity.

target_invariants: Restrictions should not persist by inertia after their residual-risk rationale expires.

target_invariants: Release decisions must preserve an audit trail and exception path.

target_invariants: Uncertainty must be visible, especially when harm from premature release is severe.

target_invariants: Human-facing controls must preserve fairness, dignity, due process, consent where relevant, and safety.

expected_outcomes: Controls are lifted neither too early nor too late relative to residual risk.

expected_outcomes: Monitoring burden tapers as risk declines, while escalation paths remain available.

expected_outcomes: Reentry, clearance, permission, or normalization decisions become explainable and reviewable.

expected_outcomes: Stakeholders can distinguish active safety logic from administrative inertia.

expected_outcomes: The system reduces avoidable restriction while preserving protection against residual hazards.

expected_outcomes: Future incidents improve the decay model through evidence and audit feedback.

prose_essence:
Residual Risk Decay Tracking is the pattern of treating post-event risk as a changing state rather than a permanent label or a vanished problem. A triggering event leaves a tail of risk. That tail may decline with time, clean evidence, remediation, repeated checks, or changed conditions, but it should not be assumed away. The archetype tracks the remaining risk and uses it to govern when restrictions, monitoring, quarantine, review holds, or access limits can be reduced.

The core question is not simply “how long should we wait?” It is “what remaining risk justifies what remaining control, and what evidence allows that control to step down?”

prose_when_to_use:
Use this archetype when a system has already experienced an incident, exposure, anomaly, conflict, contamination, violation, or other trigger that leaves residual risk. The risk should plausibly decline, but the timing of that decline is uncertain enough that a simple binary rule would be unsafe or unfair.

It is especially useful when early release could cause harm, but indefinite restriction creates its own harm: operational delay, exclusion, privacy burden, stigma, workload, lost access, or unnecessary monitoring.

prose_structural_problem:
Many control systems are binary. An account is locked or unlocked. A person is isolated or cleared. A process is under heightened supervision or normal supervision. A flagged actor is restricted or unrestricted. But residual risk often does not behave that way. It fades, sometimes quickly, sometimes slowly, sometimes only after remediation or repeated clean observations.

When the system lacks a residual-risk model, two opposite failures appear. Controls may be released too early because a fixed date arrived, or controls may persist too long because nobody has defined what would count as enough safety to release them.

prose_intervention_logic:
The intervention begins by naming the trigger and the specific residual risks it leaves behind. It then identifies a signal or proxy for remaining risk, sketches a decay curve or staged risk model, and maps that model onto a control ladder. Controls do not have to jump from maximum restriction to no restriction. They can step down through intermediate states as validation checks pass.

The archetype therefore combines five moves: record the trigger, track the residual risk, define release thresholds, validate release, and keep an audit trail. If exception signals appear, the process slows, pauses, or reverses.

prose_key_components:
### Trigger Event Record

The trigger event record defines what happened and why it created a residual-risk tail. Without it, reviewers cannot tell whether the remaining controls are tied to a real hazard, a stale label, or institutional inertia.

### Residual Risk Signal

The residual risk signal shows how much risk remains. It can be direct, such as a test result, or indirect, such as repeated clean observations. The important feature is that it supports release decisions rather than merely naming the original incident.

### Risk Decay Curve

The risk decay curve represents how remaining risk is expected to fall. It may be numerical, qualitative, staged, or conservative. A good curve includes uncertainty and conditions that would invalidate the assumption of decline.

### Control Release Threshold

The control release threshold defines when a restriction, monitoring burden, or heightened control can be reduced. It prevents both arbitrary early release and indefinite control.

### Control Intensity Ladder

The control ladder provides intermediate states: full restriction, partial access, heightened monitoring, ordinary monitoring, and closure. This allows controls to remain proportionate to the remaining risk.

### Monitoring Cadence

The monitoring cadence determines when residual risk is checked. Early checks may be frequent; later checks may taper as clean evidence accumulates. The cadence should match risk severity and uncertainty.

### Reentry Condition

The reentry condition links residual-risk evidence to practical restoration. It states what must be true before a person, account, process, facility, or decision path returns to ordinary operation.

### Validation Check

The validation check confirms that the observed decline is credible. It protects against releasing controls because a date passed while evidence remained weak.

### Exception Trigger

The exception trigger identifies signals that stop the decay assumption from operating: recurrence, failed tests, new evidence, changed exposure, noncompliance, or a new pathway of harm.

### Audit Trail

The audit trail records evidence, thresholds, decisions, approvals, and remaining safeguards. Residual-risk decisions often affect safety and fairness, so the reasoning must remain inspectable.

prose_common_mechanisms:
A **risk decay dashboard** implements the archetype by making residual-risk signals, thresholds, control levels, and review dates visible. It is not the archetype itself; it is one way to display the state of the release decision.

A **post-incident monitoring plan** turns residual-risk tracking into an operational procedure. It specifies what will be watched, how often, and what would justify tapering or renewing controls.

A **control release review** is a recurring meeting or ritual where reviewers compare evidence against thresholds. It helps prevent both unmanaged drift and arbitrary decisions.

A **quarantine duration protocol** is a domain-specific mechanism for isolation or contamination cases. It should not be generalized into medical or public-health advice; structurally, it is a bounded release procedure tied to residual-risk evidence.

A **cooling-off period policy** applies the pattern when risk comes from a transient state such as conflict, volatility, influence, or high arousal. It is only a residual-risk mechanism when the waiting period is linked to release criteria.

A **probation review schedule** or **access restoration ladder** stages the return of privileges, permissions, or ordinary access. These mechanisms are useful when binary release would be too abrupt.

A **release threshold checklist** helps reviewers avoid missing key conditions: trigger, risk signal, decay evidence, validation check, exception triggers, and audit record.

prose_failure_modes:
The most obvious failure is premature release: controls are removed because a timer ended, not because residual risk has fallen below the threshold. The opposite failure is indefinite restriction: risk is never reassessed, so controls persist as punishment, stigma, or administrative habit.

Another failure is false precision. A decay curve can look scientific while hiding sparse data or changed conditions. Monitoring blind spots can also mislead reviewers if the chosen signal does not cover the pathway by which harm can recur.

A human-facing misuse risk is using residual-risk language to justify coercion, exclusion, or surveillance after the safety rationale has expired. This is why audit trail, expiration rules, appeal paths, and stakeholder impact review matter.

prose_neighbor_distinctions:
**Half-Life-Based Timing** is the closest first-wave neighbor. It schedules action around persistence and decay in general. Residual Risk Decay Tracking adds a risk-governance layer: controls, release thresholds, monitoring burden, validation, and auditability.

**Controlled Reentry** manages the transition back into ordinary operation. Residual Risk Decay Tracking determines whether the risk tail is low enough for reentry or step-down. It often feeds controlled reentry, but it is not the whole transition plan.

**Washout and Clearance Period** waits for residual effects to clear before the next exposure or measurement. Residual Risk Decay Tracking is broader and more governance-heavy: it concerns post-event risk, restrictions, monitoring, and release accountability.

**Queue Expiration and Staleness Control** handles outdated items or information. Residual Risk Decay Tracking handles remaining danger after a triggering event.

**Boundary Permeability Control** decides what can cross a boundary. Residual Risk Decay Tracking may tell that boundary when to loosen as risk declines.

prose_cross_domain_examples:
In cybersecurity, a compromised account may move from lockout to limited access to normal permissions only after remediation and clean monitoring. In safety operations, heightened supervision after a near miss can taper after stable runs and no recurrence signals. In public-health or contamination control, clearance depends on a risk window plus validation criteria rather than convenience alone.

In finance, a fraud flag can decay through verification, repayment, clean behavior, and limited activity. In governance, a cooling-off period can delay a decision until volatility or conflict risk declines. In access management, privileges can return in stages rather than all at once.

prose_non_examples:
A simple countdown timer is not this archetype if it releases controls without any residual-risk reasoning. Permanent exclusion is not this archetype if no reassessment or release threshold exists. Generic monitoring before any event has occurred is not this archetype because there is no residual-risk tail.

A cache expiration rule is usually Half-Life-Based Timing or staleness control, not residual-risk decay, unless the cached state creates post-event risk and governs release controls. A punishment period chosen for deterrence is not this archetype unless it is explicitly tied to risk decline and validation.

prose_tradeoffs:
Residual Risk Decay Tracking trades simplicity for proportionality. A binary rule is easier to administer, but it often releases too early or too late. A staged release process is more accurate but requires data, judgment, and governance.

It also trades certainty for explicit uncertainty. Making uncertainty visible can feel less decisive, but it prevents overconfidence. Conservative buffers protect safety but can burden people and operations. Faster release reduces burden but may increase tail risk.

prose_invariants_to_preserve:
The first invariant is that residual risk must remain visible. The system should not confuse elapsed time with clearance unless that substitution is explicitly justified.

The second invariant is proportionality. Controls should be strong enough for the remaining risk but should not persist by inertia after the risk rationale expires.

The third invariant is accountability. Release, extension, and escalation decisions should be reviewable, especially when they affect access, liberty, safety, livelihood, or care.

The fourth invariant is uncertainty disclosure. A decay model should not pretend to be more certain than the available evidence allows.

prose_target_outcomes:
The target outcome is timed, justified control release. The system should reduce restrictions, monitoring, or quarantine when evidence supports doing so, while retaining safeguards when residual risk remains too high.

A successful implementation also reduces arbitrary inconsistency. Similar cases should receive similar release logic, and exceptions should be traceable to actual differences in risk, evidence, or consequences.

prose_parameter_dimensions:
Important tuning dimensions include the severity of the residual risk, the confidence in the decay curve, the cost of continued control, the harm of premature release, the number of control levels, the review cadence, and the type of validation required.

A high-consequence risk with weak monitoring needs a wider uncertainty buffer and slower release. A lower-consequence risk with strong clean evidence may support faster tapering. Human-facing restrictions require additional parameters for fairness, consent, appeal, privacy, and proportionality.

prose_variants_and_near_names:
The draft recognizes several variants. **Quarantine Risk Decay Window** covers isolation or contamination-style release. **Post-Incident Monitoring Decay** covers tapering monitoring after a breach, near miss, anomaly, or incident. **Access Restoration Decay Ladder** covers staged return of privileges or permissions. **Cooling-Off Risk Decay** covers transient risk after conflict, volatility, influence, or high-arousal states.

Near names such as “risk decay tracking,” “post-event risk decay,” “residual risk monitoring,” and “risk clearance window” should point to this archetype. “Quarantine duration,” “cooling-off period,” and “probation period” are usually mechanisms or domain names unless their drafts develop distinct cross-domain structure.

Quarantine Risk Decay Window

short_definition: Track post-exposure or post-contamination risk until release criteria justify ending isolation or heightened precautions.

distinctive_feature: The control state is isolation or restricted contact, and release depends on residual-risk evidence rather than time alone.

distinct_from_parent: It is a high-salience public-health, contamination, or safety subtype of residual-risk decay tracking.

still_same_parent_because: The core logic remains residual risk signal, decay estimate, release threshold, validation check, and reentry condition.

use_when: A prior exposure or contamination event creates risk that is expected to decline with time, testing, clearance, or absence of symptoms.

use_when: Release too early could expose others or reintroduce danger, while release too late creates avoidable burden or isolation.

Residual Risk Signal

role: Tracks remaining exposure or contamination risk.

Control Release Threshold

role: Defines when isolation can end.

Validation Check

role: Confirms release criteria.

Quarantine Duration Protocol

role: Operationalizes the isolation and release window.

Clearance Certificate

role: Documents release criteria and evidence.

example_domains:
public health
contamination control
incident response

domain: public health

example: Release isolation after a defined risk window plus negative checks.

domain: facility safety

example: Reopen a room only after contamination risk falls below the release threshold.

failure_modes: Using a fixed duration without checking changed exposure conditions.

failure_modes: Letting release become political or convenient rather than evidence-governed.

Post-Incident Monitoring Decay

short_definition: Reduce monitoring intensity after an incident as clean observations accumulate and recurrence risk declines.

distinctive_feature: The decaying control is monitoring intensity rather than permission, isolation, or access.

distinct_from_parent: It specializes the parent pattern around surveillance cadence and monitoring burden.

still_same_parent_because: It still uses residual-risk signal, decay evidence, thresholds, exception triggers, and audit trail.

use_when: An incident leaves heightened risk, but the probability of recurrence should decline if the system remains stable.

use_when: Monitoring has cost or burden and should not remain elevated indefinitely without evidence.

Monitoring Cadence

role: Sets high-to-low review frequency.

Exception Trigger

role: Restarts heightened monitoring when warning signs appear.

Post-Incident Monitoring Plan

role: Documents monitoring rules.

Risk Decay Dashboard

role: Shows decay and monitoring status.

example_domains:
cybersecurity
operations safety
quality assurance
compliance

example: Lower log-review frequency after repeated clean checks following a breach.

domain: operations

example: Reduce supervisor checks after a safety incident only after stable observations.

failure_modes: Monitoring falls off before recurrence risk is actually understood.

Access Restoration Decay Ladder

short_definition: Restore access, privileges, permissions, or autonomy in stages as residual risk declines and validation checks pass.

distinctive_feature: The control object is permission or access level, often with fairness and due-process implications.

distinct_from_parent: It emphasizes staged restoration and decision accountability rather than generic risk monitoring.

still_same_parent_because: Its logic remains release thresholds based on residual-risk decay and validation.

source_primes: threshold accountability

use_when: Full immediate restoration would be unsafe, but indefinite restriction is excessive.

use_when: Permissions can be staged and monitored as evidence accumulates.

Control Intensity Ladder

role: Maps stages of restored access.

Audit Trail

role: Documents release rationale.

Stakeholder Impact Review

role: Checks fairness and burden.

Access Restoration Ladder

role: Stages permission return.

Control Release Review

role: Reviews evidence at each stage.

example_domains:
identity and access management
education
workplace safety
platform governance

domain: software security

example: Restore administrative permissions in tiers after a compromised account is remediated.

domain: school discipline

example: Return privileges gradually after clean behavior and support checks.

failure_modes: Staged restoration becomes indefinite punishment under a technical-sounding risk label.

Cooling-Off Risk Decay

short_definition: Use a bounded waiting period after a volatile, biased, conflicted, or high-arousal event so residual risk can decline before action resumes.

distinctive_feature: The decaying risk often comes from transient state, influence, or volatility rather than contamination or technical incident.

distinct_from_parent: It specializes residual-risk tracking around temporary decision unreliability or unsafe state.

still_same_parent_because: It still requires trigger event, decay assumption, release threshold, validation, and exception triggers.

use_when: A recent event creates transient risk from emotion, conflict, volatility, intoxication, influence, panic, or bias.

use_when: The appropriate response is delayed or gated action, not permanent prohibition.

Trigger Event Record

role: Identifies the event that begins the cooling-off window.

Control Expiration Rule

role: Prevents the waiting period from becoming indefinite.

Cooling-Off Period Policy

role: Defines the waiting and release conditions.

Release Threshold Checklist

role: Checks whether action can resume.

example_domains:
governance
consumer protection
conflict management
financial controls

domain: conflict management

example: Delay a decision after an argument until reactivity has declined and review conditions are met.

domain: finance

example: Hold a risky transaction after an anomaly until verification and a cooldown threshold are met.

failure_modes: Cooldown becomes arbitrary delay or coercive control without explicit residual-risk rationale.

near_names:
Risk Decay Tracking
Post-Event Risk Decay
Residual Risk Monitoring
Risk Clearance Window
Quarantine Duration
Cooling-Off Period
Probation Period
Legal Waiting Period


Resilience Capacity Building

batch: First Iteration/Batch 16

aliases:
resilience planning
resilience capability building
continuity capacity building
preparedness and recovery capacity

slug: robustness

slug: redundancy

slug: continuity

components:
Critical Function Map
Shock Scenario
Adaptive Capacity
Recovery Resource
Continuity Threshold
Resilience Governance Owner
Incident Learning Loop
Dependency Map
Communication Channel Plan
Activation Rule

mechanisms:
Resilience Planning Workshop
Business Continuity Plan
Disaster Recovery Plan
Emergency Preparedness Drill
Tabletop Exercise
Community Resilience Program
After-Action Review

essence_one_line: Build the capacity to absorb shocks, adapt under disruption, and recover without losing critical function.

compression_statement: When a system faces shocks it cannot fully prevent, build resilience capacity so it can absorb disruption, adapt, and recover while preserving critical function.

core_intuition: Some shocks cannot be fully prevented or predicted. Resilience capacity building works by preparing the functions, resources, roles, adaptive options, and learning loops that let a system bend, improvise, and recover without losing what matters most.

canonical_formula: critical functions + plausible shocks + adaptive capacity + recovery resources + learning loop -> preserved viability under disruption

structural_problem: A system is exposed to shocks, disruptions, or volatility that cannot be eliminated and may exceed ordinary operating assumptions.

condition: Unavoidable shock exposure

description: The system faces disruptions that cannot be eliminated by prediction, prevention, or ordinary control.

condition: Critical-function dependency

description: Some functions must continue or be restored quickly because their loss threatens viability, duty of care, legitimacy, safety, or core mission.

condition: Recovery cannot be improvised from zero

description: The system may need pre-positioned resources, decision rights, relationships, alternates, or knowledge before disruption occurs.

condition: Disruption exceeds normal operating assumptions

description: Routine processes are insufficient because the event changes constraints, resource availability, demand, or coordination conditions.

symptom: Continuity depends on heroic improvisation

meaning: People can sometimes keep the system alive, but only through ad hoc action that is not prepared, supported, or repeatable.

symptom: Plans exist but resources do not

meaning: Documents describe recovery, but no deployable capacity, authority, training, or material support exists when the shock arrives.

symptom: Recovery repeats the same failures

meaning: The system restores service after incidents but does not change structures, routines, or resources in response to what was learned.

symptom: Local survival undermines system viability

meaning: Parts of the system protect themselves during disruption while critical shared functions, dependencies, or vulnerable stakeholders fail.

root_tension: The system must be efficient enough to function in ordinary conditions but capable enough to absorb, adapt, and recover when ordinary assumptions break.

case: A single known component needs a backup switch

why_not: Use failover or redundant backup provisioning when the main issue is activating an alternate component.

case: A design merely needs extra tolerance under expected variation

why_not: Use robustness margin design when the main intervention is margin against variation, not adaptive recovery capacity.

case: The problem is safe halting after hazardous failure

why_not: Use fail-safe default or protective shutdown when stopping safely is more important than continuity or recovery.

case: There are no critical functions to preserve

why_not: Capacity building becomes vague preparedness unless it is anchored to functions, shocks, resources, recovery, and learning.

structural_intervention: Identify critical functions, shock scenarios, adaptive capacities, recovery resources, and learning loops that preserve viability.

Map critical functions

logic: Identify the functions whose loss would threaten viability, legitimacy, safety, mission, or recovery potential.

Model shock classes

logic: Describe plausible disruptions and stress envelopes without pretending every event can be predicted exactly.

Build adaptive capacity

logic: Prepare flexible roles, alternate pathways, decision rights, knowledge, and resources that can be redeployed under changed conditions.

Pre-position recovery resources

logic: Ensure that material, informational, relational, financial, and governance resources are available when restoration begins.

Practice activation and coordination

logic: Rehearse the transition from normal operation to resilience posture so the capacity is deployable rather than merely documented.

Learn and update

logic: Use incidents, exercises, near misses, and weak signals to revise maps, resources, roles, and assumptions.

rule: Anchor preparation to functions, not fears

guidance: Do not build generic preparedness lists until the critical functions and their disruption modes are clear.

rule: Prefer capacities that remain useful across several shock types

guidance: When uncertainty is high, invest in adaptable resources, cross-training, relationships, and decision authority rather than brittle scenario scripts.

rule: Treat plans as mechanisms, not as capacity

guidance: A plan counts only when it is connected to resources, roles, tests, ownership, and update routines.

rule: Escalate from capacity to redesign when repeated shocks exceed prepared response

guidance: If resilience work repeatedly absorbs the same preventable failure, redesign the underlying system rather than normalizing chronic disruption.

invariant: Critical function continuity

description: The most important functions continue at an acceptable level or are restored quickly enough to preserve viability.

invariant: Adaptive decision authority

description: People or subsystems with situational information have enough authority to act within prepared guardrails during disruption.

invariant: Recovery resource availability

description: The resources required for recovery are accessible despite degraded conditions.

invariant: Learning capture

description: Disruptions and exercises produce changes in the system instead of disappearing as anecdotes.

invariant: Protection of vulnerable dependencies

description: Resilience is not achieved by shifting failure onto hidden dependencies, downstream users, or less powerful stakeholders.

outcome: Reduced loss of critical function

description: Shocks cause less interruption to the functions that matter most.

outcome: Faster and safer recovery

description: Recovery begins with prepared resources, roles, and decision rules rather than panic improvisation.

outcome: Better adaptation under novelty

description: The system can adjust to changed conditions rather than merely execute a brittle script.

outcome: Improved preparedness over time

description: Incident learning, drills, and near-miss review update capacity instead of leaving it stale.

outcome: More credible continuity commitments

description: Stakeholders can see how essential functions will be protected, restored, or gracefully prioritized.

prose_essence:
Resilience Capacity Building is the intervention of preparing a system to keep its most important functions viable when disruption cannot be fully prevented. It is not the claim that a system is “resilient” in general. The archetype becomes concrete only when it names the critical functions, the shocks or stress envelopes that threaten them, the adaptive capacities that can be used under changed conditions, the recovery resources that restore function, and the learning loop that updates the capacity over time.

The core idea is simple: some shocks will outrun ordinary control. A system that has already prepared flexible capacity, authority, recovery resources, and learning routines can absorb more disturbance, improvise with less panic, recover with fewer secondary failures, and improve after the event.

prose_when_to_use:
Use this archetype when a system faces disruptions that cannot be eliminated and when continuity or recovery of critical functions matters. It is especially useful when the system has experienced outages, disasters, supply shocks, staffing shocks, infrastructure failures, cyber incidents, ecological disturbances, institutional crises, or volatile demand that exceeds ordinary planning assumptions.

Do not use it as a synonym for toughness, optimism, or “being prepared.” It should produce a visible capacity structure: mapped critical functions, plausible shock scenarios, adaptive options, recovery resources, activation authority, and an update loop.

prose_structural_problem:
The structural problem is exposure to disruptive events that exceed normal operating assumptions. The system may be optimized for ordinary conditions, but disruption changes the availability of resources, the reliability of dependencies, the accuracy of information, the demand placed on the system, or the coordination environment.

The failure is often not a single broken part. It is the absence of prepared capacity: nobody knows which functions matter most, which dependencies will break together, who can change priorities, which resources are available under degraded conditions, how recovery should start, or how lessons become structural changes after the incident.

prose_intervention_logic:
The intervention starts by identifying critical functions. A system cannot protect everything equally during disruption, so it must name what must continue, what may degrade, and what must recover first. It then maps shock scenarios or stress envelopes, including duration, severity, correlated failures, and resource scarcity.

Next, the system builds adaptive capacity: flexible roles, alternate pathways, local authority, redeployable resources, cross-training, mutual aid, communication channels, and decision guardrails. Recovery resources are then made reachable under degraded conditions. Finally, exercises and incidents are used to update the capacity so that resilience improves rather than decays.

A plan is useful only when it changes deployable capacity. A polished continuity document with no ownership, resources, rehearsal, or learning loop is resilience theater, not this archetype.

prose_key_components:
### Critical Function Map

A critical function map identifies what must keep working or be restored first. It prevents the archetype from becoming generic preparedness by tying resilience to specific functions, dependencies, minimum acceptable service levels, owners, and consequences of loss.

### Shock Scenario

A shock scenario represents plausible disruptions or stress envelopes. It does not need to predict the future exactly. Its purpose is to reveal assumptions about scope, duration, severity, resource scarcity, shared dependencies, and coordination breakdown.

### Adaptive Capacity

Adaptive capacity is the system’s ability to change roles, priorities, resources, routes, or modes of operation under disruption. It can appear as cross-training, local autonomy, flexible resource pools, improvisation protocols, modular decision rights, or alternate operating modes.

### Recovery Resource

A recovery resource is anything needed to restore critical function: supplies, money, repair capability, data, backup access, trusted relationships, legal authority, skilled people, or communication channels. The key test is whether the resource is accessible when ordinary conditions have degraded.

### Continuity Threshold

The continuity threshold defines the minimum acceptable function during disruption and the target for restoration. It separates acceptable degradation from unacceptable failure and prevents the system from overbuilding capacity for functions that do not need immediate continuity.

### Resilience Governance Owner

A resilience governance owner maintains accountability for preparedness, activation, recovery, and learning. Without ownership, plans go stale, resources expire, exercises stop, and nobody has authority to adapt when the shock arrives.

### Incident Learning Loop

The incident learning loop turns disruptions, drills, and near misses into updates. It should track actions to completion so lessons become changed resources, roles, thresholds, maps, exercises, and decision rules.

prose_common_mechanisms:
### Resilience Planning Workshop

A resilience planning workshop is a method for gathering stakeholders to map functions, shocks, dependencies, and capacity gaps. It implements the archetype by making implicit fragility visible, but the workshop itself is not the archetype unless it leads to deployable capacity.

### Business Continuity Plan

A business continuity plan documents continuity priorities, alternates, roles, activation rules, and resources. It is a mechanism under this archetype, not a separate archetype. A plan without rehearsed authority and resources does not create resilience capacity.

### Disaster Recovery Plan

A disaster recovery plan specifies restoration sequence, resources, validation, and responsibilities after disruption. It supports this archetype when it is tied to critical functions and recovery resources; it may also neighbor Graceful Recovery Pathway when the focus is staged restoration.

### Emergency Preparedness Drill

A drill rehearses activation and coordination under simulated stress. It implements resilience capacity by testing whether people can actually use the prepared roles, communication channels, resources, and decision rules.

### Tabletop Exercise

A tabletop exercise simulates decision-making under disruption. It is especially useful for surfacing missing authority, unrealistic assumptions, weak dependencies, and coordination gaps before an actual incident.

### Community Resilience Program

A community resilience program organizes local assets, mutual aid, vulnerable-population support, communication channels, preparedness training, and recovery support. It implements the archetype at community scale.

### After-Action Review

An after-action review is a learning ritual that converts disruption experience into structural improvements. It implements the learning loop when it produces completed changes rather than only a record of observations.

prose_failure_modes:
The most common failure mode is resilience theater: plans, dashboards, slogans, or committees that do not produce deployable resources or authority. Another is narrow-scenario brittleness, where the system prepares for the last disaster but cannot adapt to the next one. Capacity decay is also common: supplies expire, skills fade, contact lists rot, and plans become obsolete.

A more serious misuse is burden shifting. A system may appear resilient because visible services continue while hidden workers, downstream partners, or vulnerable communities absorb the cost. Another failure mode is over-normalization: repeated preventable disruptions are tolerated because the organization becomes proud of coping instead of fixing upstream causes.

prose_neighbor_distinctions:
Resilience Capacity Building is distinct from Robustness Margin Design. Robustness margins help a system keep functioning across expected variation; resilience capacity prepares adaptation and recovery when disruption exceeds ordinary assumptions.

It is distinct from Capacity Reservation and System Slack. Spare capacity can support resilience, but this archetype also requires critical-function mapping, shock framing, adaptive authority, recovery resources, and learning.

It is distinct from Failover. Failover switches to an alternate component after failure. Resilience capacity is broader: it includes preparing the wider system to absorb shocks, adapt, recover, and learn.

It is distinct from Graceful Degradation. Graceful degradation preserves partial service by reducing function. Resilience capacity may include graceful degradation, but it also includes pre-shock preparation and post-shock recovery.

It is distinct from Fail-Safe Default. A fail-safe default forces a least-harmful state when failure occurs, even if function stops. Resilience capacity may preserve or restore function, but safety constraints can override continuity.

It is distinct from Chaos Exposure Testing. Chaos exposure testing injects bounded disruption to reveal weaknesses. It can validate resilience capacity, but it is a testing mechanism or neighboring archetype rather than the whole capacity-building pattern.

prose_cross_domain_examples:
In municipal services, a city can map lifeline functions, prepare alternate staffing and supplies, build mutual-aid agreements, rehearse emergency operations, and revise protocols after storms.

In software operations, a platform team can map user-critical functions, prepare incident roles and recovery access, practice outages, and update runbooks after postmortems.

In healthcare, a hospital can prepare surge staffing, triage authority, supply alternatives, backup communication, and after-action learning.

In supply networks, a manufacturer can map critical supply functions, prepare alternate suppliers, define activation triggers, and review disruptions to redesign weak points.

In ecological management, a watershed project can restore buffers, protect refugia, diversify habitat, and monitor recovery after drought, flood, or fire.

prose_non_examples:
A single backup generator is not this archetype unless it is embedded in critical-function mapping, activation rules, recovery resources, maintenance, and learning.

A rugged design that tolerates wider temperature ranges is Robustness Margin Design, not Resilience Capacity Building.

A system that shuts down immediately when danger is detected is Fail-Safe Default or Protective Shutdown, not this archetype unless broader recovery capacity is also being built.

An inspirational statement that an organization is resilient is not an archetype implementation. It names an aspiration without a capacity structure.

prose_tradeoffs:
The main tradeoff is efficiency versus preparedness. Capacity that sits unused in normal times may look wasteful, but the absence of capacity can be catastrophic under disruption. There is also a tradeoff between specific plans and general adaptability. Specific plans help with known shocks; flexible capacity helps when shocks arrive in unfamiliar forms.

Another tradeoff is continuity versus safety. Sometimes continuing service is valuable; sometimes the safest move is to stop or enter a safe mode. Resilience capacity must respect fail-safe boundaries rather than treating continuity as always superior.

prose_invariants_to_preserve:
The main invariant is critical function viability: the system should continue or restore what matters most before the loss becomes unrecoverable. A second invariant is adaptive authority: the people or subsystems closest to disruption need enough prepared authority to act without waiting for perfect information. A third invariant is recovery-resource availability under degraded conditions. A fourth is learning capture, because a system that does not learn will repeatedly rebuild the same fragility.

A final invariant is ethical: resilience should not be achieved by shifting harm onto hidden dependencies, downstream users, exhausted staff, or vulnerable communities.

prose_target_outcomes: The target outcomes are reduced loss of critical function, faster recovery, safer adaptation under novelty, clearer continuity commitments, and improvement over time. A mature resilience capacity does not promise that shocks will not hurt. It promises that the system has prepared ways to absorb, adapt, recover, and learn.

prose_parameter_dimensions:
Important tuning dimensions include the breadth of shock scenarios, the number and type of critical functions covered, the minimum continuity threshold for each function, the depth and accessibility of recovery resources, the degree of local autonomy allowed during disruption, the diversity of adaptive options, the rehearsal cadence, and the strictness of post-incident learning.

A narrow configuration is cheaper and easier to maintain but may fail under surprise. A broad configuration handles more uncertainty but costs more to govern, rehearse, and update. Highly centralized activation can align priorities but may be slow when information is local. Highly local activation can adapt quickly but may fragment system-level priorities.

prose_variants_and_near_names:
Organizational Resilience Routines apply the archetype to institutions through roles, governance, rehearsals, continuity plans, and incident learning. Community Resilience Capacity applies it to distributed local systems through mutual aid, local assets, communication channels, and shared recovery support.

Surprise Preparedness is captured as a merge-review variant because it emphasizes unknown or high-impact shocks rather than named scenarios. Multi-Scale Resilience Architecture is also captured for review because it distributes resilience capacity across nested levels.

Business continuity planning, disaster recovery planning, and emergency preparedness are near names or mechanisms. They point to this archetype only when they build actual adaptive and recovery capacity, not when they merely produce documents or checklists.

Organizational Resilience Routines

short_definition: Build reusable organizational routines for maintaining critical services during disruption and restoring normal operations afterward.

distinctive_feature: The capacity is embedded in routines, roles, rehearsals, and governance rather than only in spare technical capacity.

distinct_from_parent: The parent is any cross-domain resilience capacity; this variant focuses on organizational operating routines.

still_same_parent_because: It still builds capacity to absorb, adapt, recover, and learn from shocks while preserving critical function.

use_when: A team, institution, or enterprise must continue essential work through disruption.

use_when: Roles, decision rights, escalation paths, and recovery resources must be prepared before a crisis.

Critical Function Map

role: Clarifies which services or obligations must continue under disruption.

Resilience Governance Owner

role: Assigns accountability for preparedness, activation, recovery, and learning.

Incident Learning Loop

role: Turns events and near misses into routine improvements.

Business Continuity Plan

role: Documents continuity roles, priorities, alternates, and activation rules.

After-Action Review

role: Captures lessons and updates preparedness after disruption.

example_domains:
organizations
public agencies
health systems
education systems

domain: public agency

example: A city defines essential services, backup staffing, emergency communications, and post-event improvement routines.

example: A hospital prepares alternate staffing, triage protocols, supply reserves, and incident reviews.

failure_modes: Plans remain on paper and are not rehearsed under realistic constraints.

failure_modes: Preparedness optimizes for a narrow crisis and becomes brittle under different shocks.

Community Resilience Capacity

short_definition: Strengthen distributed local capacity so a community can absorb shocks, coordinate mutual aid, and recover shared function.

distinctive_feature: The capacity is distributed across people, institutions, shared resources, and coordination channels rather than located inside one organization.

distinct_from_parent: The parent covers resilience capacity generally; this variant emphasizes community-scale coordination, mutual aid, and social capital.

still_same_parent_because: The intervention logic is still to identify critical functions, prepare adaptive resources, recover function, and learn from disruption.

use_when: A geographically or socially connected community faces disasters, outages, economic shocks, or institutional disruptions.

use_when: Centralized response alone is insufficient and local coordination capacity matters.

Critical Function Map

role: Identifies lifeline functions such as food, shelter, care, communication, and mobility.

Recovery Resource

role: Captures local assets, relationships, reserves, skills, and institutions available in recovery.

Communication Channel Plan

role: Prepares redundant channels for warning, coordination, and recovery updates.

Community Resilience Program

role: Organizes local preparation, mutual aid, resource mapping, and rehearsals.

Emergency Preparedness Drill

role: Practices response roles and reveals coordination gaps.

example_domains:
local government
neighborhood organizations
public health
disaster preparedness

domain: neighborhood preparedness

example: A neighborhood maps vulnerable residents, backup communications, supply caches, and volunteer roles.

example: A region prepares alternate care sites, outreach networks, and recovery support after a health shock.

failure_modes: Mutual-aid expectations are vague and collapse under stress.

failure_modes: Capacity investments ignore inequitable access to resources and recovery support.

Surprise Preparedness

short_definition: Prepare generic capacities for high-impact surprises without pretending to forecast their exact form.

distinctive_feature: It emphasizes unknown shock forms and generic readiness rather than named scenario preparation.

distinct_from_parent: The parent can prepare for known shock classes; this variant deliberately prepares for surprise and model failure.

still_same_parent_because: It still uses critical functions, adaptive capacity, recovery resources, and learning as the intervention core.

use_when: The shock space contains unknown or low-probability high-impact events.

use_when: Precise prediction is unreliable but broad adaptive capacity can still be built.

Shock Scenario

role: Uses broad stress envelopes and wildcard prompts rather than only exact scenarios.

Adaptive Capacity

role: Emphasizes improvisable, redeployable, and general-purpose capability.

Wildcard Exercise

role: Tests readiness against unexpected scenario twists.

Tabletop Exercise

role: Rehearses decision-making under uncertainty and incomplete information.

example_domains:
strategic foresight
disaster preparedness
enterprise risk
public policy

domain: enterprise risk

example: A firm prepares cross-trained response capacity and decision rights for shocks not in its forecast model.

domain: public policy

example: An agency rehearses wildcard disruptions that break its normal assumptions about response capacity.

failure_modes: Surprise becomes a label for vague anxiety rather than preparation of concrete adaptive capacity.

failure_modes: The organization mistakes scenario imagination for deployable recovery resources.

Multi-Scale Resilience Architecture

short_definition: Build resilience capacity at multiple nested scales so local failures can be absorbed without sacrificing system-wide continuity.

distinctive_feature: The key distinction is nested scale design: local absorption, intermediate coordination, and system-level recovery capacity.

distinct_from_parent: The parent does not require explicit multi-level architecture; this variant makes scale placement and cross-scale coordination central.

still_same_parent_because: It remains a resilience capacity intervention: critical functions, resources, adaptation, recovery, and learning are distributed across levels.

source_primes: resilience redundancy scale

use_when: Local, regional, and system-wide layers each need distinct resilience responsibilities.

use_when: A shock can propagate across levels unless capacity is distributed and coordinated.

Critical Function Map

role: Maps which functions must be protected at each scale.

Resilience Governance Owner

role: Assigns accountability across local, intermediate, and system-wide layers.

Layered Continuity Plan

role: Documents resilience responsibilities at multiple scales.

Regional Mutual Aid Compact

role: Creates cross-scale backup and coordination capacity.

example_domains:
infrastructure
public health
supply networks
federal systems

domain: infrastructure

example: Local microgrids, regional balancing, and national restoration resources support continuity at different scales.

example: Clinics, regional hospitals, and national stockpiles each hold different recovery responsibilities.

failure_modes: Local resilience is optimized in a way that shifts risk upward or outward.

failure_modes: Scale layers duplicate responsibility but do not coordinate activation or recovery.

near_names:
Resilience Planning
Business Continuity Planning
Disaster Recovery Planning
Emergency Preparedness
Organizational Resilience