Rights–Freedoms Obligation Mapping

batch: Batch 20

canonical_family: Normative Relations & Governance

aliases:
Claim–Liberty Relationship Mapping
Rights and Duties Mapping
Entitlement–Freedom Boundary Design
Hohfeldian Relation Mapping
Positive–Negative Rights Clarification
Rights–Obligations Architecture
Liberty and Claim Reconciliation

related_primes: authority consent proportionality fairness constraint accountability boundary legitimacy delegation_of_authority mandatory_vs_default_norms equity balance trade_offs sovereignty adjudication_dispute_resolution procedural_fairness_due_process

slug: normativity

slug: authority

slug: consent

slug: proportionality

slug: fairness

slug: boundary

slug: legitimacy

slug: equity

slug: balance

slug: sovereignty

components: Scope, Condition, and Jurisdiction Boundary

components: Normative Conflict and Interaction Matrix

components: Proportionality and Least-Restrictive Screen

components: Normative Uncertainty and Dissent Register

mechanisms:
Hohfeldian Relation Matrix
Claim–Liberty Classification Tree
Counterparty Obligation Register
Rights-Conflict Scenario Table
Least-Restrictive Alternative Screen
Proportionality Balancing Record
Permission–Entitlement Crosswalk
Remedy Traceability Review

essence_one_line: Map every asserted right or freedom to its bearer, counterpart relation, scope, justification, conflict, and remedy before deciding what must be protected, provided, permitted, restrained, or reviewed.

compression_statement: “Right” can mean a protected liberty to act, a claim that another actor must act or refrain, a power to alter legal or organizational relations, or an immunity from another actor's power. These positions have different counterpart structures and implementation costs. Rights–Freedoms Obligation Mapping prevents category drift by classifying each asserted position; naming bearer and counterparty; specifying object, jurisdiction, conditions, duration, and exceptions; tracing positive provision and negative restraint duties; detecting collisions; testing authority, necessity, proportionality, and least-restrictive alternatives; and attaching enforcement, remedy, change, and review paths. It produces a contestable relationship map rather than pretending substantive conflicts can be solved by a single slogan or scalar balance.

core_intuition: A freedom describes an open action space only relative to others' lack of a claim to stop it; a claim-right describes an obligation borne by someone or some institution. Confusing the two hides who must provide, pay, refrain, enforce, or accept exposure. The solution is to make the relational grammar visible before choosing priorities or procedures.

canonical_formula: asserted_interest + bearer_and_counterparty_map + normative_relation_classifier + scope_and_condition_boundary + counterpart_obligation_map + conflict_matrix + authority_and_warrant_record + proportionality_and_least_restrictive_test + remedy_and_change_path -> coherent_contestable_rights_freedoms_architecture

structural_problem: A policy, contract, platform rule, organizational mandate, or public controversy uses “right,” “freedom,” “permission,” “entitlement,” “choice,” and “duty” as if they were interchangeable. The statement omits the relation's bearer, counterparty, object, jurisdiction, conditions, costs, exceptions, or remedy. Competing actors then debate abstractions while implementing incompatible assumptions: a liberty is treated as a guaranteed service, a claim is treated as optional permission, a power is mistaken for a liberty, or one party's protected zone silently creates an unassigned duty or burden for another.

trigger_conditions: A proposal claims to protect a right or freedom without identifying the counterpart relation.

trigger_conditions: Positive provision and negative noninterference obligations are being conflated.

trigger_conditions: Multiple rights claims collide across people, roles, resources, jurisdictions, or time.

trigger_conditions: A permission system is expected to encode substantive entitlements or immunities.

trigger_conditions: A restriction is justified by broad public interest without necessity or least-restrictive analysis.

trigger_conditions: An actor has formal power to change another's position, but the scope or review path is unclear.

trigger_conditions: A protected liberty is unusable because capability, access, or institutional provision is absent.

trigger_conditions: A claim is recognized rhetorically but has no duty bearer, capacity allocation, enforcement, or remedy.

trigger_conditions: Exceptions accumulate until the nominal right or freedom has no stable operational core.

trigger_conditions: A dispute process exists but the underlying normative positions remain unclassified.

symptoms: Policy language alternates among right, permission, benefit, option, and privilege without definitions.

symptoms: Different teams assign contradictory duties to the same actor or institution.

symptoms: A protected action zone expands by exporting risk, cost, or constraint to less powerful parties.

symptoms: Enforcement is asymmetric because one party has a remedy and another has only an aspiration.

symptoms: Consent is treated as sufficient even when a non-waivable claim or power imbalance is present.

symptoms: Balancing occurs before scope, evidence, authority, and counterpart duties are established.

symptoms: A digital access control grants a capability but no service-level entitlement, or promises service without access authority.

symptoms: “Freedom from” language hides necessary monitoring, funding, or protective duties.

symptoms: A claim is universal in rhetoric but conditional, geographically bounded, or resource-contingent in operation.

symptoms: Appeals repeatedly revisit the same category mistake rather than resolving case facts.

root_tension: Rights language must be abstract enough to protect stable principles across cases, yet implementation requires relational specificity about who may do what, who must act or refrain, under which authority, at whose cost, and with what remedy. Excessive abstraction hides obligations; excessive case specificity fragments equal protection and makes rights contingent on administrative convenience.

anti_signatures: The normative positions are already explicit and the only issue is whether a hearing was fair; use Procedural Fairness Design.

anti_signatures: The relation is clear and the task is to scale a restriction or remedy to severity and necessity; use Proportionality Calibration.

anti_signatures: A consenting party needs information, voluntariness, specificity, authority, and withdrawal; use Informed Consent Governance.

anti_signatures: The task is solely to protect a bounded permissionless operating zone; use Autonomous Action Zone Protection.

anti_signatures: A case requires evidence, a decision maker, remedy, and appeal under settled standards; use Adjudication Process Design.

anti_signatures: Materially different barriers require differentiated support under a stable standard; use Equity Adjustment.

anti_signatures: The primary problem is hidden value premises rather than ambiguous normative relations; use Normative Assumption Explicitness.

anti_signatures: The task is philosophical taxonomy without authority or intent to redesign a policy, contract, service, or governance system.

structural_intervention: Replace undifferentiated rights language with a typed, relational, scoped, and reviewable map. Classify each position; identify bearers and counterparties; specify objects, conditions, powers, duties, restraints, provisions, and remedies; model interactions among positions; test the authority and justification for constraints; choose proportionate and least-restrictive implementations; and maintain versioned change and review paths.

action_logic: State the concrete interest, action, resource, protection, decision, or relationship at issue.

action_logic: Identify every bearer, affected party, duty bearer, regulator, provider, and actor with power to alter the relation.

action_logic: Classify each asserted position as claim, liberty, duty, no-claim, power, liability, immunity, or disability.

action_logic: Record the correlative and opposite positions rather than leaving counterpart consequences implicit.

action_logic: Define scope by object, actor, role, jurisdiction, context, time, triggering condition, and termination condition.

action_logic: Separate negative restraint obligations from positive provision, access, capacity, or enforcement obligations.

Trace authority and warrant: source, delegation, consent, precedent, policy basis, evidence, and contestability.

action_logic: Build a conflict matrix showing direct contradictions, resource contention, cumulative burden, and priority rules.

action_logic: Test proposed constraints for legitimate purpose, evidence, necessity, proportionality, and least-restrictive alternatives.

action_logic: Specify enforcement, remedy, appeal, restoration, compensation, and non-retaliation paths appropriate to the relation.

action_logic: Test implementation in representative and edge cases, including low-power parties and cross-jurisdiction interactions.

action_logic: Publish the map in usable language while preserving a traceable technical record.

action_logic: Version changes and require explicit review when scope, capacity, authority, technology, or affected populations change.

decision_rules: Do not call a liberty a guaranteed service unless a separate claim and duty bearer are established.

decision_rules: Do not recognize a claim without identifying who owes what, to whom, under what conditions, and with what remedy.

decision_rules: Do not infer a power to alter another's position from a liberty to act.

decision_rules: Do not treat consent as waiving an immunity or non-waivable protection unless valid authority and scope are established.

decision_rules: Classify and scope positions before balancing them.

decision_rules: Prefer narrow, reversible, evidence-linked constraints over broad discretionary restrictions.

decision_rules: Where resources are finite, state allocation and capacity limits openly rather than converting claims into invisible queues.

decision_rules: Preserve a stable core while making exceptions explicit, justified, reviewable, and time-bounded.

decision_rules: When positions cannot be reconciled, preserve dissent and route the conflict to legitimate adjudication rather than hiding it in implementation.

decision_rules: Use plain-language statements that retain the underlying relational type and counterparty.

target_invariants: Every normative position has an identified bearer, object, and relation type.

target_invariants: Claim-rights have explicit counterpart duties or institutional obligations.

target_invariants: Liberties state the relevant no-claim or noninterference boundary without implying unassigned provision duties.

target_invariants: Powers and immunities identify who can alter whose position and who is protected from such alteration.

target_invariants: Scope, conditions, exceptions, and duration are visible and versioned.

target_invariants: Constraints have authority, evidence, purpose, necessity, and proportionality records.

target_invariants: Remedies and review paths match the type and severity of breach.

target_invariants: Resource and capacity assumptions are not hidden behind universal language.

target_invariants: Low-power and indirectly affected parties remain visible in conflict analysis.

target_invariants: The map is contestable and does not replace legitimate democratic, legal, or organizational authority.

expected_outcomes: Fewer category errors among rights, permissions, liberties, benefits, powers, duties, and immunities.

expected_outcomes: Clearer ownership of positive provision and negative restraint obligations.

expected_outcomes: Earlier detection of rights conflicts, burden transfers, and unassigned counterparties.

expected_outcomes: More precise policies, contracts, access controls, consent flows, and decision-rights matrices.

expected_outcomes: Constraints that are narrower, better justified, and easier to review or reverse.

expected_outcomes: Remedies and appeals connected to the actual normative relation breached.

expected_outcomes: Better cross-team and cross-jurisdiction consistency without erasing legitimate disagreement.

prose_essence:
Rights–Freedoms Obligation Mapping turns compressed normative language into a usable relationship architecture. The key move is not to rank “rights” against “freedoms” as abstract nouns. It is to ask what kind of position each statement creates, for whom, against whom, concerning what action or object, within which scope, under which authority, and with what consequence if the relation is breached.

The method recognizes that several different structures are routinely called a right. A claim places a duty on another party. A liberty means the bearer has no duty not to act, paired with another party's lack of a claim to prevent that act. A power allows one actor to alter a normative relation, paired with another actor's liability to that change. An immunity protects a bearer from someone else's power, paired with that actor's disability. These categories can coexist in one policy. For example, a user may have a liberty to publish, a claim that the operator follow a published moderation procedure, a power to withdraw consent for some data uses, and an immunity from retroactive alteration of a protected contractual term.

The archetype adds operational depth to that classification. It identifies positive provision and negative restraint duties, defines scope and exceptions, traces the source and legitimacy of authority, maps conflicts, tests restrictions, translates the result into implementation, and attaches remedy and review. Its output is not a mathematically final answer to moral conflict. It is a transparent, contestable account of what each proposed answer would require.

prose_when_to_use:
Use this archetype when a system must translate normative language into action and the words “right,” “freedom,” “permission,” “entitlement,” “choice,” “power,” or “duty” are carrying more than one meaning. It is particularly useful before writing policy, negotiating a contract, encoding permissions, delegating authority, designing a consent flow, limiting conduct, rationing a service, or sending a contested issue into adjudication.

The strongest signal is a missing counterparty. If someone says a person “has a right,” ask who must act, refrain, provide, recognize, enforce, or accept review. If no answer exists, the statement may name a liberty, aspiration, interest, or unfinished claim rather than an operational claim-right. Conversely, when someone says an actor is “free” to do something, ask whether the action changes another party's position, consumes shared capacity, enters a protected domain, or depends on a provision duty. The apparent liberty may include a power, constraint, or claim that needs separate treatment.

Use the archetype early enough to shape the decision, not merely as documentation after implementation. It is most valuable when multiple teams or jurisdictions would otherwise encode different interpretations. It is also useful during review: repeated appeals, access failures, inconsistent enforcement, and exception growth often reveal that the original relation was never typed or scoped precisely.

Do not use it to simulate legal authority. A mapping team can expose assumptions, omissions, and implementation consequences, but legitimate institutions and qualified reviewers must decide jurisdiction-specific doctrine, public authority, collective rights, non-waivable protections, and contested priorities.

prose_structural_problem:
Ambiguous rights language compresses a relational system into a prestigious label. Compression is often politically and communicatively useful, but it becomes dangerous when the implementation boundary arrives. A product manager hears “right to access” and creates a permission. An operations team hears the same phrase and assumes a service-level obligation. A finance team sees no assigned capacity owner. A security team adds a revocation power. A customer expects a remedy. Each interpretation may be coherent alone, yet the combined system is contradictory.

The structural defect is deeper than unclear wording. Normative positions are relational: they distribute freedom, obligation, exposure, authority, protection, cost, and remedy among actors. Omitting one side does not remove it; it makes the distribution informal. Informal distributions usually favor parties with implementation control, information, money, status, or access to review.

The same defect appears in substantive conflict. “Balance privacy and safety” is not yet a decision frame. Which privacy claim or liberty, held by whom, against which collection or disclosure? Which safety claim, owed by whom, supported by what evidence and capacity? Who has power to change the rule? Which actors are liable to that change? What immunity or non-waivable floor limits the power? What remedy exists after error? Without these questions, balancing becomes an intuitive comparison of slogans, and the party who defines the metrics controls the result.

The archetype also addresses implementation decay. Rights and freedoms change through policy versions, technical releases, emergency rules, service closures, jurisdiction changes, reorganizations, and accumulated exceptions. A stable map needs change, transition, and reliance rules so the operational relation cannot drift while public language remains constant.

prose_intervention_logic:
Begin with the concrete interest or action rather than the label. Describe what a person or institution seeks to do, receive, avoid, change, protect, or contest. Identify all directly and indirectly affected actors. Then classify each relation. The classifier is a gate: ambiguous mixed positions are decomposed, and classification is revisited when implementation reveals a missing counterparty or unexpected burden.

Next, map correlatives and opposites. A claim should reveal a duty bearer. A liberty should reveal the relevant no-claim and noninterference zone. A power should reveal who is liable to a change. An immunity should reveal whose power is disabled. This relational map is then scoped by object, role, jurisdiction, context, time, conditions, exceptions, and termination. Positive obligations are separated from negative restraints so provision capacity and noninterference are not silently exchanged.

Only after classification and scope should the team map conflicts. Some conflicts disappear when positions are narrowed; others become resource, timing, jurisdiction, or remedy problems rather than absolute contradictions. Remaining constraints are tested against authority, evidence, purpose, necessity, proportionality, and less restrictive alternatives. The analysis retains distribution and dissent instead of forcing all positions into a single score.

Finally, translate the adopted map into operational rules and remedies. Policy prose, permissions, service commitments, audit signals, review authority, restoration, compensation, and change rules should trace back to the same relation record. Representative scenarios test ordinary and edge cases. Versioned reviews catch changes in authority, technology, capacity, evidence, or affected populations.

prose_key_components:
The first four components define the relation. The Affected Interest and Bearer Map establishes whose interests are visible. The Normative Relation Classifier supplies the typed grammar. The Counterpart Obligation Map assigns restraint, provision, enforcement, or exposure to specific actors. The Liberty and Noninterference Zone protects open action space without pretending that permission automatically creates resources or immunity.

The next three components establish boundaries and legitimacy. Scope, Condition, and Jurisdiction Boundary prevents universal language from hiding contextual limits. Normative Conflict and Interaction Matrix identifies contradictions, cumulative burdens, scarce capacity, and indirect effects. Authority Source and Warrant Record separates the fact that an actor can impose a rule from the claim that the actor may legitimately do so.

Constraint Justification Test and Proportionality and Least-Restrictive Screen form the scrutiny layer. The former asks whether a restriction has legitimate purpose, evidence, harm definition, and authority. The latter compares burdens and alternatives after the positions are clear. These should not be collapsed: a proportionate measure can still lack authority, and an authorized measure can still be unnecessarily broad.

Remedy, Enforcement, and Review Path makes the structure credible. Change, Revocation, and Transition Rule protects against silent erosion and handles reliance. Normative Relation Decision Record maintains end-to-end traceability.

Optional components deepen contexts where formal liberty and effective capability diverge, where collective and individual positions interact, or where reasonable disagreement remains unresolved. They are optional because not every mapping exercise requires resource modeling, collective overlays, or a formal dissent register; they are not optional when those conditions materially determine exercise or burden.

prose_common_mechanisms:
The Hohfeldian Relation Matrix is the most recognizable analytic mechanism. It is valuable because it forces actor-pair and correlative reasoning, but it is not the archetype. A matrix can classify relations while omitting capacity, scope, conflicts, justification, remedies, and change.

The Claim–Liberty Classification Tree is a faster intake mechanism. It asks whether another party must act, refrain, accept a status change, or lack power. It is useful for policy workshops and requirements reviews, where ambiguous phrases need to be decomposed before detailed mapping.

The Counterparty Obligation Register translates claims into accountable ownership, service levels, enforcement, and capacity. Rights-Conflict Scenario Table then tests combinations of actors, conditions, scarce resources, and exceptions. The Least-Restrictive Alternative Screen and Proportionality Balancing Record support reasoned constraint selection without hiding evidence or dissent.

The Permission–Entitlement Crosswalk is essential in digital and organizational implementation. It catches situations where a user can technically act without having a service claim, or has a policy claim that the software cannot execute. Remedy Traceability Review follows a breach from detection through notice, review authority, state change, restoration, and closure. It detects “appeals” that can explain a result but cannot correct it.

Mechanisms should be selected according to the problem. A simple role-policy clarification may need the classification tree, relation matrix, and decision record. A high-stakes public restriction may require the full scenario, alternatives, proportionality, dissent, remedy, and independent review set.

prose_failure_modes:
Label inflation is the most common failure. If every interest becomes a right, the map cannot assign coherent priority or duties. Liberty-to-entitlement drift creates unowned provision. Claim-without-duty-bearer produces symbolic rights. Power-as-liberty confusion hides who is exposed to another's decision.

Premature balancing lets actors compare slogans before defining relations. Technical authorization substitution treats existing permissions as proof of legitimacy. Exception erosion preserves formal language while operational protection disappears. Remedy theater creates an appeal that cannot change state, restore access, compensate loss, or prevent retaliation.

Resource concealment turns universal claims into opaque queues and discretionary scarcity. Matrix false neutrality hides value judgments behind tables. Other failures include overclassification, inaccessible process, privacy-invasive mapping, doctrinal overclaim, and importing public-law language into private systems to inflate status.

Detection should be structural. Look for empty counterpart fields, unowned capacity, claims without remedies, powers without liabilities, permissions without warrant, exceptions without expiry, crosswalk gaps, scenario contradictions, and unresolved burdens on absent parties. Failure remediation returns to relation classification and authority, not merely better wording.

prose_neighbor_distinctions:
Procedural Fairness Design governs notice, voice, evidence, impartiality, reasons, and review. Rights–Freedoms Obligation Mapping defines what substantive positions the process is considering. A perfectly fair process can apply an incoherent relation map; a coherent map can still be implemented through an unfair process. Both may be required.

Adjudication Process Design resolves disputes through evidence, standards, decision, remedy, and appeal. This archetype can operate before any dispute, during policy design, or after adjudication reveals a category gap. Proportionality Calibration selects response scale after the protected positions and constraints are known.

Autonomous Action Zone Protection creates a bounded liberty-like domain with noninterference and override rules. It does not classify the full system of claims, powers, immunities, provision duties, and remedies. Informed Consent Governance handles voluntary, informed, specific, authorized, and revocable agreement; it cannot make every protection waivable or create every authority.

Equity Adjustment changes rules or support for relevant differences. Normative Assumption Explicitness exposes value premises. Outside-Authority Influence Channel Mapping finds informal pressure on sovereignty. Each is composable but owns a distinct intervention signature.

prose_cross_domain_examples:
In a cloud service, an API key grants technical permission, a subscription may create an availability claim, the provider retains a security power to suspend, and the customer may hold an immunity from retroactive pricing changes under a contract. Mapping these relations reveals different notice, evidence, capacity, and remedy requirements.

In a hospital, treatment choice includes liberties against coercion and claims to material information and comprehension support. Access may add positive duties subject to capacity and allocation. Clinical authority includes powers bounded by consent, emergency rules, competence, and review. One “patient rights” page is insufficient without this structure.

In a matrix organization, a team may have liberty to choose implementation, power to deploy within a boundary, duty to meet shared controls, liability to a valid security override, and immunity from unrelated functional veto. Decision-rights mapping prevents autonomy rhetoric from hiding centralized control.

In public policy, a time-and-place restriction can be evaluated only after identifying the protected liberty, safety claims, duty bearers, authority, evidence, geographic and temporal scope, unequal burden, alternatives, remedy, and sunset. In education, participation and fair assessment claims require accessibility duties while academic integrity constrains acceptable alternatives.

prose_non_examples:
A grievance panel that needs better notice and evidence rules is a Procedural Fairness or Adjudication problem unless the underlying relation is also ambiguous. A regulator selecting sanction severity needs Proportionality Calibration. A research team improving an opt-in flow needs Informed Consent Governance. A unit seeking permissionless execution within a charter needs Autonomous Action Zone Protection.

A philosopher comparing theories without an implementation decision is not yet applying this solution archetype. A security engineer adding a role to an access-control list is using a mechanism unless substantive entitlement and authority are disputed. A workshop that merely asks participants to rank values without counterpart duties, scope, remedy, or translation is not Rights–Freedoms Obligation Mapping.

prose_tradeoffs:
Precision costs time, expertise, and maintenance. Typed relation maps can feel legalistic, especially in settings accustomed to flexible norms. The remedy is layered representation and proportional depth, not abandonment of counterpart analysis.

Making positive duties explicit can expose promises that exceed capacity. That may create political or organizational conflict, but hiding the conflict creates informal rationing and unreliable claims. Capacity mapping should support phased provision, minimum floors, transparent allocation, and review rather than immediate erasure of the position.

Formal relation typing can underrepresent traditions that frame rights collectively, relationally, culturally, or through responsibilities rather than individual claims. The Collective–Individual Relation Overlay and Dissent Register mitigate this, but expert and community review remain necessary.

Strong remedy and scrutiny improve credibility but can slow decisions. Interim protective measures may be necessary under urgency, with narrow scope, evidence preservation, expiry, notice, and rapid review. Finally, public transparency can conflict with privacy or safety; decision records should disclose reasons and structure while minimizing sensitive data.

prose_invariants_to_preserve:
Preserve relational completeness. Every adopted position must identify bearer, object, type, counterparty consequence, scope, and remedy status. Preserve categorical honesty: a liberty is not silently upgraded to a claim, a technical capability is not treated as legitimate power, and an aspiration is not reported as an enforceable entitlement.

Preserve the distinction between current practice and justified design. An organization may currently exercise power that it lacks legitimate authority to exercise. A system may deny a claim because implementation is absent. The map records both facts without deriving ought from is.

Preserve affected-party visibility, especially for low-power, indirectly affected, future, or collectively represented groups. Preserve capacity and distribution assumptions when positive obligations are involved. Preserve a stable core against exception creep while allowing legitimate, explicit, time-bounded exceptions.

Preserve contestability. The map is an aid to authority, not authority itself. Evidence, value judgments, dissent, uncertainty, and reviewer roles remain visible. Preserve proportionality and least-restrictive reasoning when constraints are imposed. Preserve remedy and transition so rights are not recognized only at the moment of drafting.

Finally, preserve domain humility. The same relational grammar can illuminate software, organizations, contracts, services, and public law, but those domains do not confer the same legal or moral status. Cross-domain reuse is structural analogy, not doctrinal equivalence.

prose_target_outcomes:
The immediate outcome is precise language: participants can say whether they mean permission, noninterference, provision, authority, protection from authority, or remedy. The operational outcome is aligned implementation: policy, code, contracts, staffing, capacity, monitoring, and appeals express compatible positions.

The governance outcome is better conflict handling. Scope clarification resolves false conflicts; true conflicts identify affected parties, duties, burdens, authority, alternatives, and review. Restrictions become narrower and more evidence-linked. Positive claims become more credible because owners, capacity, queues, and remedies are visible.

The equity outcome is not automatic agreement but reduced invisibility. Burdens exported to people outside the focal relationship appear in the interaction matrix. Formal liberties that cannot be exercised because of barriers become distinguishable from provision claims and capacity choices. Remedy gaps are measurable.

The learning outcome is a reusable record of interpretation. Future teams can see why a relation was classified, what alternatives were rejected, what dissent remained, and what changes should trigger review. This reduces repeated category debates and makes silent erosion easier to detect.

prose_parameter_dimensions:
Granularity is the first tuning choice. Principle-level maps communicate stable commitments; actor-pair maps expose implementation. Mature practice keeps both: a concise public layer with traceable detailed relations underneath. Excess detail without hierarchy becomes unmaintainable, while broad language without counterparties becomes performative.

Scope precision should increase with severity and diversity of context. High precision names jurisdiction, role, object, trigger, duration, exception, and termination. Yet brittle precision can create loopholes and exclude unanticipated cases. Scenario testing and review triggers balance that tradeoff.

Positive-obligation depth depends on practical exercise. Some liberties need only noninterference. Others are nominal without information, accessibility, infrastructure, staff, funding, or enforcement. Increasing depth reveals capacity and allocation decisions, but it must not turn current scarcity into an automatic justification for denial.

Constraint scrutiny rises with irreversibility, coercion, surveillance, bodily impact, exclusion, vulnerability, and power asymmetry. Remedy strength should track breach severity and include actual state-changing authority. Dissent visibility should also rise with contestation: a single adopted interpretation may be necessary for operation, but alternative views and review conditions should not disappear.

The archetype should never tune for tidy matrices alone. A more complete map may expose genuine pluralism, uncertainty, and resource conflict. That apparent messiness is information needed for legitimate governance.

prose_variants_and_near_names:
Public-Policy Rights–Obligation Mapping adds state authority, fiscal capacity, jurisdiction, public reason, and independent review. Digital Permission–Entitlement Mapping adds executable roles, credentials, service states, audit, and technical remedy. Organizational Decision-Rights Mapping emphasizes delegation, consultation, veto, escalation, accountability, and transition. Positive–Negative Rights Capacity Mapping deepens the difference between noninterference and effective provision.

These remain variants because they share the same parent sequence: type the relation, identify counterparties, scope it, map conflicts, verify warrant, test constraints, attach remedy, and govern change. Promotion would require a distinct lifecycle or boundary system that no longer fits the parent.

Near names should be routed carefully. Hohfeldian analysis names a mechanism and conceptual vocabulary. Decision-rights matrix is often an organizational mechanism. Positive versus negative rights is a structural variant. Generic “rights balancing” is too narrow because the archetype insists that classification and scope precede balancing.

Public-Policy Rights–Obligation Mapping

short_definition: Maps public claims, liberties, state duties, powers, immunities, constraints, and remedies across affected populations and institutions.

distinctive_feature: Constitutional, statutory, regulatory, fiscal, jurisdictional, and public-legitimacy questions dominate.

distinct_from_parent: Adds public authority, legislative mandate, state capacity, non-derogation, public reason, and cross-jurisdiction review.

still_same_parent_because: Retains relation typing, counterpart duties, scope, conflicts, justification, proportionality, remedies, and change control.

source_primes: rights_vs_freedoms normativity authority proportionality

use_when: Public authority defines or restricts protected positions

use_when: Multiple populations and institutions are affected

use_when: Provision and noninterference duties interact

failure_modes: Remedy inaccessible to low-power parties

Digital Permission–Entitlement Mapping

short_definition: Translates user, operator, owner, and regulator positions into permissions, prohibitions, service commitments, powers, immunities, audit, and remedy paths.

distinctive_feature: Roles, capabilities, credentials, service levels, data use, automated decisions, audit logs, and technical rollback dominate.

distinct_from_parent: Adds executable policy translation, identity lifecycle, system states, logging, code review, and failure recovery.

still_same_parent_because: Retains typed relations, counterpart obligations, scope, authority, conflicts, constraints, remedies, and change records.

use_when: Software permissions implement policy positions

use_when: Service access and capability are distinct

use_when: Automated enforcement can alter positions

use_when: Technical and policy owners need traceability

failure_modes:
Permission mistaken for entitlement
Policy-code drift
Orphaned duty after service change
Opaque automated revocation
Appeal cannot change system state

Organizational Decision-Rights Mapping

short_definition: Maps who may decide, who must be consulted or informed, who can veto or review, and what duties accompany organizational authority.

distinctive_feature: Mandates, delegation, role boundaries, vetoes, consultation, escalation, accountability, and transition dominate.

distinct_from_parent: Adds organizational roles, operating cadence, delegated authority, succession, and performance accountability.

still_same_parent_because: Retains bearers, relation typing, powers, liabilities, scope, conflict, warrant, remedy, and change rules.

use_when: Decision rights need counterpart accountability

failure_modes: Reorganization silently changes obligations

name: Positive–Negative Rights Capacity Mapping

short_definition: Separates noninterference duties from provision, access, capacity, funding, and enforcement duties required for effective exercise.

distinctive_feature: Capacity floors, funding, service levels, queues, accessibility, eligibility, rationing, and distribution dominate.

distinct_from_parent: Deepens positive obligation feasibility and the interface between formal liberty and effective capability.

still_same_parent_because: Retains relation typing, counterpart assignment, scope, conflict, authority, proportionality, remedy, and change control.

source_primes: rights_vs_freedoms constraint equity balance

use_when: A nominal freedom may be unusable without provision

use_when: Resources or service capacity are finite

use_when: Universal language hides queue or rationing rules

use_when: Equity and accessibility affect effective exercise

failure_modes: Formal equality with inaccessible exercise

near_names:
Claim Rights vs. Liberties Mapping
Hohfeldian Rights Analysis
Decision-Rights Matrix


Risk-Adjustment and Benchmark Selection

type: archetype

source: generated

batch: Second Iteration/Batch 07

slug: probability

slug: uncertainty

slug: confounding

slug: counterfactuals

slug: causality

slug: overfitting

slug: robustness

slug: calibration

slug: stationarity

components:
Claim Scope and Evaluation Horizon
Reference Universe Definition
Risk-Factor Specification
Benchmark Construction Rule
Risk-Adjustment Mapping
Abnormal Residual Interpretation Rule
Alternative-Benchmark Robustness Check
Benchmark Assumption Disclosure Log
Pre-Analysis Benchmark Precommitment
Implementability Filter
Model-Risk Review Gate

mechanisms: Style-, Sector-, or Case-Matched Benchmark

essence_one_line: Before calling performance abnormal, inefficient, or skillful, choose a benchmark that matches the relevant risk exposure, opportunity set, time horizon, and information conditions.

compression_statement: This archetype turns performance evaluation into disciplined comparator design: define the claim, define the eligible reference universe, specify the risk factors or exposure dimensions that could justify different expected outcomes, construct a benchmark from those assumptions, and interpret only the residual as candidate abnormal performance or inefficiency.

core_intuition: Raw outperformance is ambiguous. A result may look exceptional because the actor bore more risk, operated in a different environment, had a different case mix, used a different horizon, or was compared to the wrong reference set. A risk-adjusted benchmark removes the obvious compensation and exposure differences first, so the remaining claim is about what is genuinely unexplained.

canonical_formula: claim context + reference universe + risk-factor specification + benchmark construction + residual test + robustness check -> disciplined abnormal-performance judgment

structural_problem: Observed performance is often compared to a convenient raw baseline, causing risk compensation, exposure mix, benchmark mismatch, or model-selection artifacts to be mistaken for inefficiency, alpha, skill, anomaly, or failure.

trigger_conditions: A person, portfolio, program, policy, model, or organization is being judged against a benchmark or peer group.

trigger_conditions: Outcomes differ because units carry different risks, exposures, constraints, time horizons, or opportunity sets.

trigger_conditions: The claim being evaluated depends on whether the observed excess is abnormal after plausible adjustment.

trigger_conditions: Multiple plausible benchmarks or factor models exist, and conclusions could change depending on which one is chosen.

trigger_conditions: The evaluator must distinguish genuine inefficiency, skill, or anomaly from expected compensation for risk or exposure.

trigger_conditions: Stakeholders may have incentives to choose a flattering benchmark after seeing results.

symptoms: Raw returns or outcomes are presented as evidence of skill, inefficiency, or superiority without a clear exposure-matched comparator.

symptoms: A broad index, average peer, or historical baseline is used even though the evaluated unit has a different risk profile or opportunity set.

symptoms: The benchmark changes after the outcome is known, making the claim look more or less favorable.

symptoms: Factor exposures, case mix, leverage, duration, volatility, sector, geography, or regime conditions are treated as noise rather than modeled differences.

symptoms: Different analysts reach opposite conclusions because they use different reference universes or adjustment models.

symptoms: Reported abnormal performance disappears when obvious risk factors or selection effects are included.

root_tension: Fair comparison requires adjustment, but every adjustment embeds assumptions. Too little adjustment creates false anomalies; too much adjustment can explain away genuine anomalies or hide poor performance.

anti_signatures: The question is only whether a rule was followed, not whether performance exceeded a comparable baseline.

anti_signatures: All units are genuinely homogeneous in risk, exposure, horizon, and opportunity set, so raw comparison is adequate.

anti_signatures: The goal is to design a risk-pooling or transfer architecture rather than evaluate performance under risk.

anti_signatures: The benchmark is legally or contractually fixed and the task is only compliance with that benchmark.

anti_signatures: No plausible reference universe or exposure model can be specified, in which case the proper output may be uncertainty disclosure rather than adjusted judgment.

structural_intervention: Specify the benchmark, factor model, risk-adjustment logic, and residual-interpretation standard before judging performance, then test whether the conclusion survives reasonable alternative comparator choices.

action_logic: State the claim being evaluated: inefficiency, abnormal return, skill, underperformance, program effect, model superiority, or risk compensation.

action_logic: Define the eligible reference universe and opportunity set that the evaluated actor or strategy could reasonably have used.

action_logic: Identify exposure dimensions that can legitimately change expected performance, such as volatility, leverage, duration, liquidity, sector, geography, case mix, regime, or other domain-specific risk factors.

action_logic: Choose a benchmark construction rule: matched index, weighted peer set, factor model, stratified comparison, synthetic control, matched cohort, or another transparent comparator.

action_logic: Compute the adjusted expectation and residual; separate expected compensation from unexplained excess or shortfall.

action_logic: Run alternative-benchmark and factor-sensitivity checks to see whether the conclusion is stable or benchmark-dependent.

action_logic: Report the claim with its benchmark assumptions, residual magnitude, uncertainty, and model limitations.

decision_rules: The benchmark must match the claim horizon, investable or actionable opportunity set, and relevant risk exposures.

decision_rules: Risk factors should be chosen from pre-specified theory, domain knowledge, or documented pre-analysis rules rather than selected only to produce a desired conclusion.

decision_rules: If a conclusion reverses across reasonable benchmarks, classify it as benchmark-dependent rather than robust abnormal performance.

decision_rules: Do not treat residual abnormal performance as causal proof unless the design also supports causal identification.

decision_rules: If the benchmark is customized, disclose why a standard comparator is inadequate and what assumptions the custom comparator introduces.

decision_rules: When incentives to benchmark-shop are high, require precommitment, independent review, or version-controlled benchmark policy.

target_invariants: Comparable exposure is compared with comparable exposure.

target_invariants: Benchmark choice is transparent, pre-specified where possible, and reviewable after the fact.

target_invariants: The evaluation horizon of the benchmark matches the evaluation horizon of the claim.

target_invariants: Expected compensation for modeled risk is separated from residual abnormal performance.

target_invariants: Uncertainty, sensitivity, and model dependence remain visible rather than hidden in a single score.

target_invariants: Noncanonical terms such as benchmark, factor model, and risk adjustment remain components or mechanisms rather than unvalidated primes.

expected_outcomes: Fewer false claims of inefficiency, alpha, anomaly, superiority, or failure based on raw comparisons.

expected_outcomes: Clearer distinction between risk compensation and unexplained residual performance.

expected_outcomes: More defensible investment, policy, program, or performance-evaluation conclusions.

expected_outcomes: Reduced benchmark shopping and post-hoc comparator manipulation.

expected_outcomes: Improved communication about what a result does and does not prove.

prose_tradeoffs: Simple benchmarks are easier to explain, but they may be unfair. Rich factor models are more tailored, but they can become opaque, unstable, or overfit. Pre-specified benchmarks reduce manipulation, but they may become stale after regime shifts. Custom benchmarks may improve substantive fit, but they also create more room for benchmark shopping.

name: Asset-Pricing Factor Benchmark Selection

short_definition: Select a factor model and reference portfolio to test whether apparent returns are abnormal after recognized risk exposures are modeled.

distinctive_feature: The comparator is a risk-factor-implied expected return rather than a raw market average.

distinct_from_parent: It is the finance and market-efficiency implementation of the broader benchmark-selection archetype.

still_same_parent_because: The structural logic remains risk-adjusted comparator construction followed by residual interpretation.

use_when: A market anomaly, alpha claim, or inefficiency claim is being evaluated.

use_when: Observed returns could reflect exposure to systematic risk factors rather than exploitable mispricing.

example_domains:
asset pricing
market-efficiency research
portfolio analysis

domain: asset pricing

example: A return anomaly is evaluated after market, style, sector, and liquidity exposures are modeled.

failure_modes:
factor omission
post-hoc factor selection
sample-period overfitting

short_definition: Evaluate a manager or strategy against a benchmark matched to mandate, style, constraints, and risk exposure.

distinctive_feature: The benchmark is tied to the manager mandate and implementable opportunity set.

distinct_from_parent: It focuses on accountability and performance review rather than general inefficiency testing.

still_same_parent_because: It still requires a risk-adjusted comparator and residual claim discipline.

use_when: A manager is being judged for skill, underperformance, or mandate fit.

use_when: A broad index would penalize or flatter the manager because it has different exposures.

example_domains:
investment governance
manager review
portfolio policy

domain: investment governance

example: A small-cap manager is reviewed against a small-cap style benchmark rather than a broad large-cap index.

failure_modes:
benchmark shopping
style drift misclassification
non-implementable comparator

Case-Mix-Adjusted Program Benchmarking

short_definition: Compare programs, providers, schools, sites, or teams after adjusting for baseline differences in the populations they serve.

distinctive_feature: Risk adjustment is expressed as case-mix or population-exposure adjustment rather than market factor modeling.

distinct_from_parent: It is a cross-domain extension outside finance.

still_same_parent_because: The parent logic is still exposure-matched benchmark construction before residual interpretation.

use_when: Observed outcomes vary with the baseline severity or composition of served cases.

use_when: Raw peer comparison would reward easier case mix or punish harder case mix.

example_domains:
healthcare
education
public services
operations

domain: healthcare

example: Hospitals are compared after patient severity and case mix are modeled.

failure_modes: overadjustment that excuses avoidable poor performance

near_names: Factor-Model Selection for Alpha Testing


Risk Aversion Calibration

batch: First Iteration/Batch 39

aliases:
Risk Calibration
Calibrated Risk Posture
Loss-Salience Calibration

alias_records:
Risk Calibration
Calibrated Risk Posture
Loss-Salience Calibration

components:
Perceived Risk
Objective Risk Estimate
Downside Protection
Opportunity Cost Review
Hedge or Commitment Choice
Calibration Reference Class
Risk Appetite Boundary
Reversibility Assessment

mechanisms:
Risk Framing
Small Experiment
Downside Cap
Hedging or Insurance
Reversible Pilot
Expected-Value Review
Opportunity Cost Reflection
Risk Matrix

essence_one_line:
Calibrate risk avoidance so caution matches actual downside, uncertainty, and
opportunity cost.

compression_statement:
When fear of loss prevents beneficial action or when optimistic framing hides real
downside, calibrate risk aversion by separating perceived risk from evidence, estimating
probability and consequence, designing downside protection, comparing opportunity cost,
and choosing whether to commit, hedge, stage, learn, or decline.

core_intuition:
Risk aversion is not simply a bias to eliminate. It often protects against real
downside. The failure occurs when loss salience is either exaggerated into paralysis or
suppressed into recklessness. Calibration lets caution keep its protective function
while preventing it from blocking proportionate action.

canonical_formula: perceived_loss + objective_risk + protection + opportunity_cost -> calibrated_posture -> hedge_or_commitment_choice

structural_problem:
Actors avoid, delay, over-protect, or prematurely reject uncertain options because the
possibility of loss is more salient than the evidence warrants, or because they cannot
distinguish unacceptable risk from acceptable, bounded, reversible, or compensated risk.

trigger_conditions:
A promising option is repeatedly rejected because loss, embarrassment, variance, or
downside feels intolerable without a clear comparison to evidence.

trigger_conditions: A group treats the status quo as risk-free while action is treated as risky.

trigger_conditions:
Stakeholders disagree because one side emphasizes possible loss and another side
emphasizes possible gain without a shared risk ledger.

trigger_conditions:
The decision is uncertain but potentially learnable through a pilot, small experiment,
reference class, or staged commitment.

trigger_conditions:
Existing risk discussion jumps from vague fear directly to yes/no decisions without
identifying safeguards or opportunity cost.

symptoms:
Frequent requests for more certainty even when additional information has low decision
value.

symptoms:
Overweighting vivid worst-case stories while ignoring base rates, reversibility, or
containment.

symptoms:
Rejecting all uncertain options, including those with limited downside and high learning
value.

symptoms:
Using broad labels such as too risky or safe enough without decomposing likelihood,
consequence, exposure, and protection.

symptoms:
Treating inaction, delay, or over-control as neutral rather than as choices with their
own costs.

symptoms: Moving from risk analysis to risk avoidance without an explicit decision rule.

root_tension:
The system needs protection from loss and uncertainty, but it also needs learning,
adaptation, and opportunity capture. Uncalibrated risk aversion preserves safety by
shrinking action; uncalibrated optimism preserves action by hiding downside. The
archetype resolves the tension by making risk posture proportional to evidence and
protection.

anti_signatures:
The risk is already prohibited by law, ethics, safety standards, or hard constraints;
calibration cannot override a non-negotiable boundary.

anti_signatures: The actor lacks authority, resources, or consent to expose others to the downside.

anti_signatures:
The decision is simple and low-stakes enough that formal calibration would waste
attention.

anti_signatures:
The issue is primarily total decision volume or fatigue rather than risk posture;
Decision Load Management is closer.

anti_signatures:
The issue is probability weighting itself without the motivational pattern of avoidance;
Probabilistic Risk Weighting may be closer.

structural_intervention:
Create a shared calibration sequence that names the perceived loss, estimates objective
risk, identifies downside protection, compares the opportunity cost of inaction, and
translates the result into a posture-appropriate action: commit, hedge, stage, preserve
an option, collect targeted evidence, or decline.

action_logic:
Name the option and the feared loss without dismissing either the opportunity or the
anxiety around it.

action_logic:
Separate perceived risk from objective risk by asking what loss is imagined, how likely
it is, how severe it would be, and what evidence supports the estimate.

action_logic:
Locate the risk boundary: what downside is unacceptable, what is tolerable, what is
reversible, and what can be contained.

action_logic:
Design protections such as caps, staged exposure, fallback plans, hedges, reversible
pilots, insurance, governance review, or stop rules.

action_logic:
Compare the risk of action with the risk of inaction, including delay, missed learning,
resource lock-in, opportunity decay, and competitor or environmental movement.

action_logic:
Choose a posture that matches the calibrated risk: commit now, hedge, run a bounded
experiment, preserve the option, gather specific missing evidence, or decline with
reasons.

action_logic:
Review outcomes so future risk perception is updated rather than repeatedly reset by
fear or optimism.

decision_rules:
If the downside is unacceptable and cannot be bounded, decline or redesign the option
before proceeding.

decision_rules:
If downside is high but can be capped and learning value is high, prefer a staged or
reversible commitment.

decision_rules:
If probability and consequence are low but perceived threat is high, use evidence review
and reference cases to reduce over-avoidance.

decision_rules:
If the cost of delay exceeds the cost of a bounded trial, move from analysis to a
protected experiment.

decision_rules:
If probabilities are too uncertain to estimate but the option is reversible, design a
small experiment rather than demanding impossible certainty.

decision_rules:
If the decision exposes others to material harm, include consent, governance, and safety
boundaries before using calibration to justify action.

target_invariants: Caution remains legitimate; the goal is calibrated proportionality, not forced boldness.

target_invariants: Downside protection is explicit enough to be audited, not merely asserted.

target_invariants: The status quo and delay are included in the risk comparison.

target_invariants:
High-consequence or irreversible harms cannot be averaged away by expected-value
reasoning.

target_invariants:
The final decision records the reason for commit, hedge, stage, learn, decline, or
defer.

target_invariants:
Risk is calibrated structurally and contextually rather than attributed to individual
cowardice or recklessness.

expected_outcomes: Better distinction between acceptable uncertainty and unacceptable exposure.

expected_outcomes: Reduced paralysis caused by vague or exaggerated loss salience.

expected_outcomes: More appropriate use of pilots, hedges, and downside caps.

expected_outcomes: Improved willingness to pursue beneficial options when risk can be bounded.

expected_outcomes: Improved refusal or redesign of options whose downside remains disproportionate.

expected_outcomes: More reliable organizational learning from risk decisions over time.

prose_essence:
Risk Aversion Calibration is the pattern of making caution proportional. It does not say that people should take more risk, and it does not say that caution is irrational. It asks a more structural question: does the current level of avoidance match the actual downside, the available evidence, the protections that can be designed, and the cost of not acting?

The archetype becomes useful when loss feels so salient that a valuable option is blocked, delayed, or over-controlled, or when a system has swung the other way and is treating a real downside as if it were merely a matter of confidence. The intervention separates perceived risk from objective risk, designs safeguards, compares the status quo, and turns the result into an action posture: commit, hedge, stage, learn, preserve the option, decline, or redesign.

prose_when_to_use:
Use this archetype when a decision contains uncertainty and the response to that uncertainty seems miscalibrated. The miscalibration may be excessive caution, such as a team rejecting every uncertain proposal because any loss feels unacceptable. It may also be under-caution, such as a team treating an exciting option as safe because the upside is vivid and the downside is vague. In both cases, the core issue is not general decision fatigue or generic probability estimation. The issue is the relationship between loss salience, evidence, protection, opportunity cost, and action.

It is especially suitable when the feared loss can be named and bounded, when a reversible pilot or staged commitment is possible, when a risk estimate can be improved with reference cases or small experiments, or when the status quo is being treated as if it had no risk. It is weak or inappropriate when the possible harm is catastrophic, illegal, unethical, nonconsensual, or imposed on others without accountability.

prose_structural_problem:
The structural problem is a distorted risk posture. A person, group, or institution faces an uncertain option, but the possible loss occupies the whole decision field. That loss may be financial, reputational, operational, emotional, strategic, political, or safety-related. Because the loss feels vivid, the actor may treat avoidance as obviously prudent even when the actual probability is low, the downside can be capped, or inaction carries its own cost.

The opposite distortion is also possible. A group may become so attached to the potential gain that it treats risk concerns as obstruction. In that case, Risk Aversion Calibration protects against reckless framing by requiring the same discipline: name the perceived and actual downside, define non-negotiable boundaries, and design safeguards before committing.

The deep tension is that risk aversion has a real protective function. Removing it entirely would make systems fragile. But leaving it unexamined can make systems stagnant. Calibration keeps the protective signal while preventing it from becoming an automatic veto.

prose_intervention_logic:
The intervention starts by naming the risk that is being felt. Vague phrases such as “too risky” are translated into concrete possible losses: money lost, trust damaged, learning failure, safety breach, option closed, public criticism, wasted time, or harm to stakeholders.

Next, the draft separates perception from evidence. What is the likelihood? What is the consequence? What is unknown? What reference cases exist? What would change the estimate? This step should not force false precision. In many settings, qualitative tiers and uncertainty ranges are more honest than single numbers.

The third move is structural: change the exposure if possible. A risk that is unacceptable at full scale may be acceptable as a reversible pilot, a small experiment, a capped budget, a staged rollout, an insured exposure, or a commitment with stop rules. The question becomes not “is this risky?” but “what would make the risk proportionate?”

The fourth move is to compare inaction. Avoidance is also a choice. Delay can lose learning, customers, trust, capability, time, morale, or strategic position. A calibrated decision includes both the risk of acting and the risk of not acting.

Finally, the process chooses an action posture. The output may be full commitment, hedged commitment, staged commitment, a protected experiment, option preservation, targeted evidence gathering, redesign, or refusal. Refusal is a legitimate calibrated output when downside remains unacceptable.

prose_key_components:
### Perceived Risk

Perceived risk records what the option feels likely to cost. This component matters because risk aversion is often driven by vivid loss salience before formal analysis begins. The draft does not dismiss this perception. It captures it so the actor can ask whether the perception points to a real boundary, a missing safeguard, a distorted likelihood, or an unexamined fear.

### Objective Risk Estimate

The objective risk estimate tests perceived risk against evidence. It asks about likelihood, consequence, variance, reversibility, exposure, and unknowns. This component should be honest about uncertainty. A weak qualitative estimate is often better than a precise-looking number that hides ignorance.

### Downside Protection

Downside protection changes the shape of the decision. Instead of asking actors to tolerate an unbounded loss, it asks whether the downside can be capped, staged, insured, reversed, buffered, monitored, or contained. Protections make action possible only when they are real and enforceable.

### Opportunity Cost Review

Opportunity cost review makes non-action visible. A risk-averse system often scrutinizes the proposed action while treating the status quo as safe. This component asks what is lost by waiting, avoiding, over-testing, or keeping resources locked in the current state.

### Hedge or Commitment Choice

The hedge or commitment choice is the decision output. The archetype is incomplete if it only produces a risk discussion. The calibrated posture must say what happens next: commit, hedge, stage, run a trial, gather targeted evidence, preserve the option, redesign the proposal, or decline.

### Optional Components

A calibration reference class can improve judgment by comparing the current decision to similar past cases. A risk appetite boundary states the maximum acceptable exposure. A reversibility assessment asks whether the decision can be paused, rolled back, or contained. These optional components are especially useful in high-uncertainty or high-stakes environments.

prose_common_mechanisms:
### Risk Framing

Risk framing changes what is salient. It may show the same option as a full loss, a bounded trial, a reversible experiment, a portfolio bet, or a status-quo comparison. This mechanism implements the archetype only when the new frame is tied to evidence and safeguards, not when it simply makes risk sound smaller.

### Small Experiments

A small experiment creates evidence under limited exposure. It is useful when actors are stuck because they are trying to decide under uncertainty that could be reduced through action. The experiment must have a learning question, success criteria, downside cap, and next-decision rule.

### Downside Caps

A downside cap limits maximum exposure through a budget cap, stop-loss rule, eligibility limit, rollback condition, containment boundary, or safe-to-fail design. This mechanism is central when the feared loss is rationally tied to severity rather than only to probability.

### Hedging or Insurance

Hedging or insurance transfers, diversifies, or buffers exposure. It may be literal insurance in finance, redundancy in operations, mutual aid in community systems, contractual protection in partnerships, or a fallback route in implementation. It should not become a way to shift harm onto people who did not consent to bear it.

### Reversible Pilots

A reversible pilot lets a system learn before full commitment. It works when the decision can be contained and rolled back. It fails when the pilot has no measurement plan, no rollback condition, or no rule for deciding what evidence is enough.

### Expected-Value Reviews

Expected-value review compares outcomes by probability and consequence. It helps when vivid losses are overweighted or when upside is overstated. It must be used carefully: catastrophic harms, rights violations, and irreversible losses cannot simply be averaged away.

### Opportunity Cost Reflection

Opportunity cost reflection asks what is lost by avoiding the option. It prevents the status quo from becoming invisible. This mechanism is especially useful when a group keeps postponing action in search of certainty while the environment continues changing.

### Risk Matrices

A risk matrix is an artifact that organizes likelihood and consequence. It can support calibration, but it is not the archetype. A matrix becomes useful only when it informs safeguards, opportunity-cost comparison, and the final hedge or commitment choice.

prose_failure_modes:
One failure mode is calibration as pressure. A leader may call a cautious group “risk averse” and use the process to push through a preferred decision. The mitigation is to preserve refusal, redesign, and stronger protection as legitimate outcomes.

Another failure mode is false precision. Teams may invent probabilities because the framework asks for estimates. The mitigation is to use confidence ranges, qualitative tiers, explicit unknowns, and decision-relevant evidence thresholds.

A third failure mode is downside displacement. The decision-maker may be protected while users, workers, communities, or other stakeholders bear the risk. The mitigation is to ask who carries each downside and whether they have consent, representation, and recourse.

A fourth failure mode is analysis loop. The group keeps recalibrating but never acts or refuses. The mitigation is to define what information would change the decision and what threshold closes the analysis.

A fifth failure mode is pilot theater. A team runs a pilot to appear careful, but the pilot has no learning question or decision rule. The mitigation is to define stop/go criteria before the pilot starts.

prose_neighbor_distinctions:
Risk Aversion Calibration is distinct from Probabilistic Risk Weighting. Probabilistic Risk Weighting is mainly about estimating and weighting likelihoods and consequences. Risk Aversion Calibration includes those estimates but also addresses loss salience, downside protection, opportunity cost, and action posture.

It is distinct from Risk–Return Tradeoff. Risk–Return Tradeoff compares exposure with reward. Risk Aversion Calibration asks why the actor is avoiding or underplaying risk and how the option can be structurally changed through caps, hedges, staging, or refusal.

It is distinct from Option Preservation. Option Preservation keeps future possibilities open. In this archetype, preserving an option is one possible output after calibration, not the whole intervention.

It is distinct from Staged Commitment. Staging is one mechanism for changing exposure over time. Risk Aversion Calibration decides whether staging is warranted and what risk it is meant to bound.

It is distinct from Affect–Evidence Separation. Affect–Evidence Separation is broader: it separates feelings from evidential warrant across judgments. Risk Aversion Calibration is the risk-posture subcase where the affective signal is loss salience and the output is a hedge, experiment, commitment, or refusal.

It is distinct from Decision Load Management. Decision Load Management addresses degraded judgment caused by too many decisions or poorly sequenced choices. Risk Aversion Calibration can apply to a single major decision even when decision volume is low.

prose_cross_domain_examples:
In product management, a team considering a controversial feature can define the feared losses, run a limited launch, cap exposure to a user segment, monitor support signals, and set rollback criteria. The output is not reckless launch or indefinite delay; it is a protected experiment.

In organizational strategy, leaders considering a new market can compare entry risk with the risk of inaction. They may choose a partnership, option-preserving investment, capped budget, or staged commitment rather than full entry or complete avoidance.

In education, a learner who avoids public work because failure feels costly can be given a low-stakes draft, criteria-based feedback, and a path to revision. The risk is not denied; it is bounded so learning can proceed.

In operations, a team introducing a process change can use safety stop conditions, limited scope, monitoring, and escalation rules. The archetype supports change only where the downside can be responsibly controlled.

In governance, a public agency can test a new service policy with a sunset clause, eligibility boundaries, public reporting, and review criteria. The intervention makes legitimacy and downside protection part of the risk posture.

prose_non_examples:
A motivational speech telling people to take more risks is not Risk Aversion Calibration. It does not separate perception from evidence or design downside protection.

A spreadsheet with red, yellow, and green risks is not the archetype if it does not change the decision, safeguards, opportunity-cost comparison, or commitment posture.

A financial recommendation to buy or sell a specific asset is not the archetype. The archetype is a reusable decision pattern, not domain-specific advice.

A leader dismissing safety concerns as excessive caution is not the archetype. Calibration must preserve legitimate downside boundaries.

A pilot with no rollback conditions, no learning question, and no decision rule is not the archetype. It is pilot theater.

prose_tradeoffs:
The archetype adds process overhead. For routine low-stakes choices, formal calibration may be excessive. It is most valuable when the decision is important enough that distorted risk posture would create real loss.

Protections can reduce upside. A pilot may slow rollout; a cap may limit learning; insurance may cost money; a staged commitment may frustrate people who want decisive action. These tradeoffs are acceptable when they make action proportionate, but they should be named.

Expected-value tools bring clarity but can create false precision. A number can make a weak estimate look authoritative. High-quality calibration often requires ranges, caveats, reference cases, and sensitivity checks.

Opportunity-cost framing can prevent paralysis, but it can also be misused to rush people. That is why opportunity cost must be paired with downside boundaries and consent.

prose_invariants_to_preserve:
Caution must remain legitimate. The archetype should never turn into pressure to be bold. A calibrated refusal is a valid output when downside remains unacceptable or cannot be bounded.

Downside boundaries must be explicit. If the intervention claims that risk is protected, the protection should be visible, assignable, and testable. A vague assurance is not a downside cap.

The status quo must be included in the comparison. Risk is not only in change. Inaction can carry decay, missed learning, lost trust, opportunity cost, or hidden maintenance burdens.

Expected-value reasoning must not erase ethical or catastrophic constraints. Some harms should trigger redesign or refusal even when their probability appears low.

The final posture must be actionable. The output should say whether to commit, hedge, stage, experiment, gather evidence, preserve the option, redesign, or decline.

prose_target_outcomes:
The desired outcome is not more risk-taking. It is better proportionality. Beneficial but uncertain options should no longer be blocked by vague fear when downside can be bounded and evidence can be created. Dangerous or exploitative options should no longer proceed because upside language hides who bears the cost.

A successful implementation produces clearer decision records, better use of pilots and hedges, more credible safeguards, and more honest comparisons between action and inaction. Over time, it also improves learning: actors can compare feared outcomes with actual outcomes and update their future risk perception.

prose_parameter_dimensions:
The main tuning dimension is severity of downside. Low-severity downside can often be handled with fast experimentation; high-severity downside requires strong safeguards or refusal. Another dimension is reversibility. Reversible choices can usually move faster than irreversible ones, while irreversible choices need stronger evidence, governance, and boundary setting.

A third dimension is uncertainty quality. Some uncertainty can be reduced through research, reference cases, simulation, or pilots. Some cannot. When uncertainty is reducible, the archetype should create targeted evidence. When uncertainty is irreducible, it should focus on exposure limits and values-based boundaries.

A fourth dimension is opportunity decay. If delay is costly, the threshold for a bounded experiment may be lower. If delay is cheap and downside is high, more analysis or redesign may be justified. The point is not speed; the point is proportionality.

A fifth dimension is who bears the risk. A decision is not calibrated if one party receives the upside while another party absorbs the downside. Stakeholder exposure, consent, and accountability are tuning constraints, not optional ethics notes.

prose_variants_and_near_names:
Downside-Cap Calibration is the variant used when the main blocker is an open-ended worst case. It focuses on maximum loss, stop conditions, insurance, containment, and enforceable exposure limits.

Reversible Experiment Calibration is the variant used when the decision is being treated as all-or-nothing even though a bounded learning step is possible. It turns full commitment into a trial with rollback and evidence criteria.

Opportunity-Cost Calibration is the variant used when inaction is treated as safe. It brings delay, missed learning, lost opportunity, and status-quo decay into the same comparison as action risk.

Risk-Posture Rebalancing is a candidate variant for repeated decision classes or institutional norms. It may eventually deserve fuller treatment if future batches show a distinct portfolio-level pattern.

Near names include Risk Calibration, Calibrated Risk Posture, Loss-Salience Calibration, Downside Protection Design, Small-Bet Design, and Risk Framing. Risk Matrix should be treated as a mechanism or artifact, not as a separate archetype.

Downside-Cap Calibration

short_definition:
A variant that focuses risk calibration on making the maximum tolerable loss explicit
and designing protections that keep loss within that boundary.

distinctive_feature: The central move is bounding loss exposure, not merely estimating likelihood.

distinct_from_parent:
The parent calibrates overall risk posture; this variant specifically calibrates by
designing a credible maximum-loss boundary.

still_same_parent_because:
It still uses the parent logic of comparing perceived risk, objective risk, protection,
opportunity cost, and action posture.

use_when: Actors are blocked mainly because the worst-case loss feels open-ended or intolerable.

use_when:
The option may be useful if maximum exposure can be capped, staged, insured, contained,
or reversed.

use_when:
Stakeholders need to distinguish acceptable downside from unacceptable downside before
action can proceed.

Downside Protection

role: Caps or absorbs loss.

Risk Appetite Boundary

role: Defines maximum acceptable exposure.

Downside Cap

mechanism_type: guardrail

Hedging or Insurance

mechanism_type: institution

example_domains: strategy operations finance product_launch

example: Release a feature to a small segment with a rollback plan and support limits before broad rollout.

domain: operations

example: Authorize a process change only under a fixed budget cap and defined stop condition.

failure_modes:
A cap that is not enforceable can create false confidence and turn calibrated risk into
hidden exposure.

Reversible Experiment Calibration

short_definition:
A variant that lowers the barrier to action by converting a seemingly high-commitment
choice into a reversible learning step.

distinctive_feature:
The core move is changing commitment structure so learning can happen before
irreversible scale-up.

distinct_from_parent:
The parent can calibrate by evidence review alone; this variant calibrates through
designed action and observation.

still_same_parent_because:
The reversible experiment exists to recalibrate risk aversion and select a later
hedge/commitment choice.

use_when: Uncertainty is high, but a limited trial could produce decision-relevant evidence.

use_when:
Actors are avoiding action because they treat the first step as equivalent to full
commitment.

use_when:
The domain supports rollback, staging, sandboxing, or containment without unacceptable
external harm.

Reversibility Assessment

role: Checks what can be undone.

Objective Risk Estimate

role: Defines what evidence the experiment should produce.

Small Experiment

mechanism_type: method

Reversible Pilot

mechanism_type: procedure

domain: learning

example: Try a new assignment format with one module and defined feedback criteria before changing the whole course.

example: Pilot a new approval flow with one team and a rollback trigger before enterprise adoption.

failure_modes:
A pilot can become a symbolic delay if it lacks a decision rule or if stakeholders keep
demanding more proof after evidence is sufficient.

Opportunity-Cost Calibration

short_definition: A variant that corrects over-caution by making the risks and losses of inaction visible.

distinctive_feature:
The distinctive move is to put non-action into the risk ledger rather than evaluating
only the proposed action.

distinct_from_parent:
The parent covers full posture calibration; this variant concentrates on correcting the
invisibility of opportunity cost.

still_same_parent_because: It still ends with a calibrated hedge, commitment, delay, or decline decision.

use_when:
The current option feels risky while delay, avoidance, or the status quo is being
treated as neutral.

use_when:
A decision is repeatedly postponed in search of certainty, even though delay carries
real costs.

use_when:
Stakeholders need to compare downside of action with downside of missed opportunity,
decay, or competitive loss.

Opportunity Cost Review

role: Makes cost of inaction visible.

Hedge or Commitment Choice

role: Converts comparison into action.

Opportunity Cost Reflection

mechanism_type: checklist

example_domains: strategy hiring research product_management

domain: strategy

example: Compare the risk of entering a new market with the risk of ceding the market while waiting for perfect data.

domain: hiring

example: Compare the risk of hiring a capable but nontraditional candidate with the cost of leaving a critical role open.

failure_modes:
Opportunity-cost language can be misused to rush decisions if downside constraints and
evidence are ignored.

Risk-Posture Rebalancing

short_definition:
A variant that recalibrates a group or system whose default posture has drifted toward
excessive caution or excessive risk-seeking.

distinctive_feature:
The unit of change is a standing posture, norm, or portfolio pattern rather than one
option.

distinct_from_parent:
The parent can be used one decision at a time; this variant applies across a repeated
decision class.

still_same_parent_because:
It still uses perceived risk, objective risk, downside protection, opportunity cost, and
hedge/commit choices.

use_when:
A team repeatedly avoids beneficial uncertainty or repeatedly normalizes unacceptable
downside.

use_when: The problem is an institutional posture rather than a single decision.

Calibration Reference Class

role: Shows how prior risk choices behaved.

Risk Appetite Boundary

role: Defines allowed posture.

Risk Framing

mechanism_type: communication

domain: governance

example: Review a committee that rejects every novel proposal and create a protected category for reversible experiments.

failure_modes:
Rebalancing can become ideology if the system chooses a risk attitude before evaluating
the actual decision class.

near_names:
Risk Calibration
Risk Matrix
Downside Protection Design
Risk Framing
Small-Bet Design


name: Risk Pooling vs. Reinsurance Layering Strategy

aliases: Primary Pool with Secondary Risk Transfer

alias_records: Primary Pool with Secondary Risk Transfer

source_primes: risk_pooling uncertainty probability

slug: layering

slug: buffering

slug: resilience

slug: scale

slug: threshold

components:
Exposure Population Definition
Correlation Structure Profile
Primary Retention Layer
Attachment and Exhaustion Thresholds
Secondary Transfer Layer
Basis and Counterparty Risk Control
Incentive Compatibility Guardrail
Capital or Reserve Buffer
Layer Rebalancing Cadence
Member Assessment or Call Mechanism

mechanisms:
Excess-of-Loss Reinsurance Contract
Quota-Share Reinsurance Arrangement
Stop-Loss Cover
Hedging Overlay Contract
Catastrophe Bond or Parametric Cover
Contingent Supply or Capacity Contract

essence_one_line: Keep ordinary variance inside a primary risk pool while transferring capacity-breaking, correlated, or tail layers to secondary carriers, markets, or backstops.

compression_statement: This archetype turns risk pooling into a layered bearing-of-loss architecture: identify which exposures become more stable when pooled, define the pool retention layer, then attach secondary reinsurance, hedging, guarantee, or contingent contract layers for losses whose scale or correlation would threaten the primary pool.

core_intuition: Pooling works best when many losses are not perfectly correlated, but every pool has a boundary where shared variance becomes shared fragility. Layering preserves the cheap, stabilizing benefits of the pool for ordinary losses while moving rare or systemic loss bands to parties or instruments better able to absorb them.

canonical_formula: exposure set + correlation profile + primary retention layer + secondary transfer layers + attachment thresholds -> resilient pooled risk architecture

structural_problem: A pool of uncertain exposures can reduce ordinary volatility, but correlated shocks, catastrophic tail events, or loss concentrations can exceed the pool capacity and turn mutualization into collective failure.

trigger_conditions: Many participants, assets, claims, obligations, or contracts face uncertain losses that are not perfectly correlated.

trigger_conditions: Ordinary or moderate losses are frequent enough that direct pooling creates administrative or statistical efficiency.

trigger_conditions: Extreme or systemic losses would threaten solvency, service continuity, political legitimacy, or contractual performance if retained entirely in the pool.

trigger_conditions: External markets, institutions, guarantors, reinsurers, counterparties, or contingent contracts can absorb some layers more efficiently than the primary pool.

trigger_conditions: The design team must choose what to retain, what to transfer, and where each layer should attach.

symptoms: The pool performs well in ordinary periods but becomes fragile under catastrophe, systemic market movement, epidemic, or supply-chain disruption.

symptoms: Premiums, reserves, or member contributions are either too high for routine losses or too low for extreme losses.

symptoms: Decision-makers debate whether to self-insure, insure, reinsure, hedge, reserve, or seek a public backstop without a layer-by-layer model.

symptoms: Loss concentration, common-cause exposure, or correlated behavior makes naive pooling unreliable.

symptoms: Risk transfer contracts are purchased opportunistically rather than tied to explicit retention and attachment thresholds.

root_tension: A primary pool gains efficiency by retaining shared uncertainty, but it loses resilience when the retained layer includes losses whose scale, correlation, or timing exceed the pool capacity.

anti_signatures: There is only one exposure or participant, so no meaningful pool can be formed.

anti_signatures: All relevant losses are perfectly correlated and no external transfer layer can absorb or diversify them.

anti_signatures: The main problem is estimating risk rather than deciding who bears which layer of loss.

anti_signatures: The transfer layer would create unacceptable moral hazard, opacity, or regulatory evasion.

anti_signatures: A mandatory legal, ethical, or contractual rule already fixes who must bear the loss and no redesign is allowed.

structural_intervention: Design a layered risk architecture: aggregate compatible exposures into a primary pool, specify the retained loss layer, then attach secondary risk-transfer layers for loss bands that are too large, correlated, delayed, or volatile for the pool to bear alone.

action_logic: Define the exposure population and the loss events the pool is meant to absorb.

action_logic: Estimate the dependence structure: which losses diversify, which cluster, and which share common drivers.

action_logic: Set a primary retention layer for ordinary and moderate losses that the pool can finance without destabilization.

action_logic: Identify tail, catastrophe, systemic, or concentration layers that would exceed the pool capacity or risk appetite.

action_logic: Select secondary transfer instruments such as reinsurance, stop-loss cover, hedging contracts, guarantees, catastrophe bonds, reciprocal backstops, or contingent procurement contracts.

action_logic: Specify attachment points, limits, exclusions, collateral, counterparty standards, and review triggers for each layer.

action_logic: Monitor claims, correlations, capital adequacy, counterparty reliability, and behavioral incentives, then rebalance the layer structure when conditions change.

decision_rules: Retain losses inside the pool when the pool can diversify them and the retained volatility stays within capacity.

decision_rules: Transfer a layer when its expected retained impact would threaten solvency, continuity, covenant compliance, or stakeholder trust.

decision_rules: Attach secondary cover above the pool retention threshold rather than replacing the primary pool unless first-dollar transfer is structurally necessary.

decision_rules: Separate frequency risk, severity risk, correlation risk, and counterparty risk; each may require a different layer or instrument.

decision_rules: Do not buy transfer simply because a loss is large; buy it when the transfer layer is more resilient, more liquid, better diversified, or more capital-efficient than retention.

decision_rules: Revisit layers after material shifts in exposure composition, common-cause drivers, market liquidity, regulation, or counterparty quality.

target_invariants: Every material loss band has an identified bearer: pool, retained reserve, member assessment, reinsurer, hedge counterparty, public backstop, or other transfer layer.

target_invariants: The pool retains enough risk to preserve incentives for prevention, underwriting discipline, and member accountability.

target_invariants: The secondary layer is aligned to the actual exposure; basis risk and exclusions are visible rather than hidden.

target_invariants: Attachment thresholds and limits are explicit, reviewable, and linked to pool capacity.

target_invariants: The architecture remains understandable enough that members and governors know who absorbs loss under stress.

expected_outcomes: Lower ordinary volatility through primary pooling.

expected_outcomes: Reduced probability of pool insolvency or service collapse under extreme events.

expected_outcomes: More disciplined risk-transfer spending because transfer layers are tied to attachment thresholds.

expected_outcomes: Clearer governance over retention, premiums, reserves, reinsurance, hedging, and backstop activation.

expected_outcomes: Better resilience to correlated shocks without abandoning the efficiency of shared pooling.

prose_tradeoffs: Retaining more risk saves transfer cost and preserves prevention incentives, but increases stress exposure. Transferring more risk can protect solvency, but may add cost, opacity, basis risk, and counterparty dependence. Simple layer structures are easier to govern; complex structures may fit exposure better but can become difficult to audit.

Traditional Reinsurance Layering

short_definition: Use quota-share, excess-of-loss, or catastrophe reinsurance above a primary insurance pool.

distinctive_feature: Formal insurer-reinsurer layer structure with attachment points, limits, and exclusions.

distinct_from_parent: It is the canonical insurance-domain implementation of the broader layered risk pooling archetype.

still_same_parent_because: The structural logic remains primary pooling plus secondary transfer of selected loss bands.

use_when: An insurer or mutual pool retains primary claims but needs protection from severity spikes or catastrophe aggregation.

use_when: A reinsurer or capital market can absorb the layer more efficiently than the primary pool.

example_domains:
property insurance
casualty insurance
health reinsurance
catastrophe risk

domain: property insurance

example: A primary carrier retains ordinary storm claims and transfers the hurricane catastrophe layer to reinsurers.

failure_modes:
underestimated catastrophe correlation
reinsurer default
ambiguous exclusions

Hedging Overlay Pooling

short_definition: Retain diversifiable pooled exposure while hedging correlated market, currency, commodity, weather, or rate drivers.

distinctive_feature: Uses market instruments to transfer common-cause exposure while leaving the primary pool intact.

distinct_from_parent: It emphasizes financial or parametric hedging rather than insurance-style reinsurance.

still_same_parent_because: The primary logic remains retention of poolable loss plus transfer of a defined stress layer.

use_when: A pooled portfolio has idiosyncratic risks that diversify but also a common market driver that can move all losses together.

use_when: A hedge instrument closely matches the common driver and basis risk is acceptable.

example_domains:
banking
commodity procurement
energy finance
currency risk

domain: supply chain

example: A purchasing pool retains routine supplier variation but hedges fuel-price spikes affecting all members.

failure_modes:
basis risk
liquidity stress
counterparty failure

Public Catastrophe Backstop Layering

short_definition: Layer local, mutual, or private pooling beneath public catastrophe funds, sovereign guarantees, or emergency backstops.

distinctive_feature: Uses a higher-level institutional layer for extreme, systemic, or socially intolerable losses.

distinct_from_parent: It adds public governance, eligibility, accountability, and political constraints.

still_same_parent_because: It still allocates loss bands across primary pool and secondary support layers.

use_when: The catastrophic layer is too large or too correlated for private pools alone.

use_when: Public legitimacy or continuity requires a higher-level backstop after primary and market layers are exhausted.

example_domains:
flood insurance
earthquake pools
pandemic loss programs
agricultural disaster funds

domain: public disaster finance

example: Local reserves absorb small floods, regional pools absorb larger losses, and a national backstop activates after exhaustion.

failure_modes:
implicit bailout expectations
moral hazard
politicized eligibility

Contingent Supply-Chain Risk Transfer

short_definition: Use shared reserves plus contingent sourcing, logistics, or commodity contracts to transfer disruption layers.

distinctive_feature: Transfers operational disruption risk through contingent capacity rather than only insurance or finance contracts.

distinct_from_parent: It is an operations-domain implementation focused on supply availability, not only monetary claims.

still_same_parent_because: It still separates ordinary poolable disruption from tail or common-cause disruption layers.

use_when: Multiple organizations or units face partially independent disruptions but also common supply shocks.

use_when: Backup capacity or price-protection contracts can absorb the stress layer better than an internal pool.

example_domains:
critical materials
freight logistics
food systems
cloud capacity

domain: cloud operations

example: Several services share internal failover capacity but contract with an external provider for region-wide outage capacity.

failure_modes: backup provider shares the same outage driver

near_names:
Layered Risk Pooling
Pool-and-Reinsure Strategy
Stop-Loss Layering
Risk Retention and Transfer Layering