name: Artificial Diversity Introduction During Homogenization Pressure

batch: Second Iteration/Batch 03

aliases: Diversity Reintroduction Under Standardization Pressure

aliases: Variant Preservation Against Homogenization

alias_records: Diversity Reintroduction Under Standardization Pressure

alias_records: Variant Preservation Against Homogenization

slug: diversity

slug: resilience

slug: robustness

slug: adaptation

slug: groupthink

slug: interoperability

slug: modularity

slug: redundancy

slug: classification

essence_one_line: When a system is being driven toward sameness, deliberately seed, protect, or recover distinct options so adaptive capacity, resilience, and representational breadth do not collapse.

compression_statement: Artificial Diversity Introduction During Homogenization Pressure is the pattern of counteracting a collapse of meaningful variety by making diversity loss observable, defining what kinds of difference matter, and actively adding or protecting variants before a monoculture becomes self-reinforcing. It does not merely celebrate difference; it ties deliberate diversity introduction to a diagnosed risk such as common-mode failure, blind spots, path dependence, groupthink, loss of local fit, or innovation stagnation.

core_intuition: Sameness often looks efficient in the short term: fewer interfaces, fewer exceptions, fewer dissenting voices, fewer product variants, fewer educational models, and fewer competing standards. But when sameness becomes too strong, the system loses the raw material needed for adaptation. This archetype creates intentional, bounded variety before homogenization locks in fragility.

canonical_formula: introduce_diversity if measured_meaningful_variation < diversity_floor and homogenization_pressure > threshold; select diversity dimensions by risk_reduction × adaptive_value × legitimacy, bounded by coordination_cost and safety_constraints

structural_problem: A system is converging toward a narrow set of options, voices, standards, practices, suppliers, designs, narratives, or pathways. The convergence may reduce coordination costs, but it also removes alternative capacities, perspectives, experiments, and local adaptations. Once the dominant form becomes self-reinforcing, the system becomes more brittle, less representative, and less able to respond to shocks or novel opportunities.

condition: Standardization, optimization, consolidation, platform dominance, funding incentives, or social conformity is eliminating variants.

diagnostic_use: Check whether remaining variation is below a meaningful diversity floor.

condition: The system faces uncertainty, changing environments, or unknown future demands.

diagnostic_use: Preserve or introduce alternatives as adaptive capacity rather than treating all nonstandard forms as inefficiency.

condition: A dominant practice is producing blind spots, exclusion, common-mode failure risk, or stale innovation.

diagnostic_use: Seed alternatives that differ on the specific dimensions creating risk.

condition: Participants are self-censoring, copying winners, or converging on the same benchmarks.

diagnostic_use: Create protected lanes for independent voices, counterfactual proposals, or nonconforming experiments.

condition: Local contexts differ but a central template is being imposed uniformly.

diagnostic_use: Reintroduce local fit and plural implementation pathways.

condition: Legacy variants, minority approaches, or independent infrastructures are disappearing but may be valuable under future conditions.

diagnostic_use: Use archives, seed banks, minority-provider support, or option-preservation mechanisms.

symptom: Every team, supplier, school, model, or product begins to optimize for the same metric.

interpretation: A shared benchmark is becoming a homogenizing force.

symptom: Nonstandard approaches are dismissed before being tested because they are inconvenient to central process.

interpretation: Coordination efficiency is crowding out adaptive diversity.

symptom: A shock affects all units at once because they share the same design, assumption, source, or failure mode.

interpretation: The system lacks diverse pathways or independent variation.

symptom: Dissent, local practice, niche expertise, or minority perspective disappears from decision processes.

interpretation: The system is losing representational and epistemic diversity.

symptom: Innovation pipelines produce cosmetically different options that are structurally identical.

interpretation: Variety is performative rather than meaningful.

symptom: Dominant platforms or standards make alternatives progressively less viable.

interpretation: Path dependence and network effects are suppressing diversity.

root_tension: Homogenization improves coordination, predictability, and scale efficiency, while diversity improves adaptation, resilience, representation, and discovery. The archetype manages the tension by introducing diversity on dimensions that matter, not by maximizing variety everywhere.

case: The system already has abundant meaningful variation and the main problem is fragmentation or incompatibility.

reason: Use interoperability, standardization, consolidation, or coherence patterns before adding more diversity.

case: A safety-critical process requires strict uniformity and variation would create unacceptable risk.

reason: Use controlled experimentation outside the production boundary rather than live diversity injection.

case: The proposed diversity is purely cosmetic and does not change assumptions, capacities, pathways, sources, or perspectives.

reason: This is branding or token variation, not the archetype.

case: The target is a single deterministic specification with no meaningful range of acceptable implementations.

reason: Use compliance, quality control, or tolerance management instead.

case: Diversity is being introduced to avoid accountability for choosing or evaluating options.

reason: The archetype requires evaluation and stewardship, not indefinite pluralism.

structural_intervention: Detect dimensions where meaningful variation is collapsing, define a diversity floor or coverage requirement, seed or protect variants on those dimensions, and govern the added variety so it remains viable, evaluable, and connected to resilience, representation, discovery, or local-fit goals.

action: Map homogenization pressure.

purpose: Identify which incentives, standards, metrics, platforms, procurement rules, social signals, or optimization routines are eliminating variation.

action: Specify meaningful diversity dimensions.

purpose: Separate valuable variation in assumptions, sources, roles, methods, capacities, contexts, or pathways from superficial difference.

action: Set a diversity floor or representation requirement.

purpose: Define the minimum viable breadth needed for resilience, epistemic coverage, innovation, fairness, or adaptive capacity.

action: Seed, recover, or protect variants.

purpose: Introduce alternatives through protected pilots, option portfolios, minority-provider support, local autonomy, open interfaces, archives, seed banks, or independent review lanes.

action: Guard against uncontrolled fragmentation.

purpose: Use compatibility boundaries, safety constraints, evaluation criteria, and integration pathways so diversity does not destroy coordination.

action: Evaluate diversity value.

purpose: Track whether introduced variation improves resilience, discovery, representation, learning, or local fit rather than simply increasing countable variety.

action: Retain, recombine, or retire variants.

purpose: Sustain variants that provide value, integrate useful lessons, and discontinue forms that add cost without meaningful adaptive benefit.

decision_rules: Introduce diversity only after naming the homogenization pressure and the risk created by lost variation.

decision_rules: Prioritize diversity dimensions tied to common-mode failure, blind spots, local-fit mismatch, exclusion, innovation stagnation, or future uncertainty.

decision_rules: Prefer small protected variant portfolios when the system still needs coordination with a common core.

decision_rules: Use independence requirements when diversity is meant to protect epistemic coverage or prevent herding.

decision_rules: Use viability support when alternatives are valuable but disadvantaged by network effects, economies of scale, or legacy funding structures.

decision_rules: Do not count alternatives as meaningful diversity if they share the same dependency, incentive, data source, failure mode, or worldview.

decision_rules: Retire or recombine variants when they have been evaluated and no longer provide distinct adaptive value.

target_invariants: meaningful diversity is tied to an explicit risk or opportunity dimension

target_invariants: introduced variants remain viable long enough to be evaluated

target_invariants: safety and legitimacy boundaries are preserved

target_invariants: coordination costs stay bounded by shared interfaces or common evaluation frames

target_invariants: minority or nonstandard options are not exposed to immediate dominance by the homogenizing force

target_invariants: diversity metrics distinguish structural difference from superficial variety

target_invariants: learning from variants is captured rather than isolated

expected_outcomes: greater adaptive capacity under uncertainty

expected_outcomes: broader perspective, source, or pathway coverage

expected_outcomes: increased discovery of viable alternatives

expected_outcomes: more robust innovation and learning portfolios

expected_outcomes: better local fit in heterogeneous contexts

expected_outcomes: reduced path-dependent lock-in to a single dominant form

Option Portfolio Diversity Injection

short_definition: Reserve portfolio space for structurally different options when optimization pressure is converging all projects on the same template.

distinctive_feature: Diversity is introduced through portfolio allocation.

distinct_from_parent: Narrower because it focuses on portfolio slots and resource allocation.

still_same_parent_because: It still responds to homogenization pressure by protecting meaningful alternatives.

use_when: project, product, or policy portfolios are converging too tightly

use_when: future uncertainty makes one dominant pathway risky

example_domains:
innovation portfolios
public policy pilots
research funding

failure_modes:
quota theater
underfunded alternatives

Perspective Diversity Reintroduction

short_definition: Reintroduce independent perspectives when decision-making has converged on a single worldview, source base, or dominant narrative.

distinctive_feature: Diversity is primarily epistemic or deliberative.

distinct_from_parent: Narrower because it focuses on voices, viewpoints, evidence sources, and interpretive frames.

still_same_parent_because: It counters homogenization by replenishing meaningful variation in decision inputs.

use_when: critical decisions rely on a narrow source set

example_domains:
expert panels
journalism
strategy review

failure_modes:
token dissent
unintegrated advice

Local Practice Pluralism Protection

short_definition: Protect local practices, pedagogies, institutions, or cultural forms when central templates are erasing context-sensitive variation.

distinctive_feature: Diversity is tied to local contextual fit.

distinct_from_parent: Narrower because it focuses on local or institutional pluralism.

still_same_parent_because: It remains a counter-homogenization diversity intervention.

use_when: central standardization is reducing local fit

use_when: minority or regional practices are losing viability

example_domains:
education
community governance
cultural preservation

failure_modes:
romanticizing harmful local practice
fragmentation without shared safeguards

Open Interface Diversity Preservation

short_definition: Use stable interfaces or open standards to let diverse implementations coexist under platform or proprietary lock-in pressure.

distinctive_feature: Diversity is protected by interface governance.

distinct_from_parent: Narrower because it focuses on technical or procedural compatibility layers.

still_same_parent_because: The goal is still to prevent homogenization from eliminating alternatives.

use_when: a platform or dominant vendor is eliminating alternatives

use_when: coordination is needed but implementation variety should remain viable

example_domains:
software ecosystems
public infrastructure
data standards

failure_modes: fragmentation behind superficial compatibility

Genetic or Ecological Rediversification

short_definition: Recover or introduce biological, ecological, or seed diversity when monoculture creates fragility.

distinctive_feature: Diversity is biological or ecological and may use seed banks or restoration programs.

distinct_from_parent: Narrower because it is domain-specific.

still_same_parent_because: It exemplifies the same counter-homogenization structure.

use_when: commercial or ecological pressure has narrowed genetic or species diversity

use_when: future stress tolerance depends on broader variation

example_domains:
agriculture
ecosystem restoration
conservation

failure_modes:
maladapted introduction
biosecurity risk

near_names:
Artificial Diversity Seeding
Counter-Monoculture Option Design
Diversity Floor Intervention
Recommendation Diversity Injection


Aspect-Scoped Identity Projection

type: archetype

source: generated

batch: Batch 06

aliases:
Role-Scoped Identity Projection
Aspectual Identity Modeling
Qua-Object Modeling
Derived Role Identity
Context-Bound Identity Projection
Scoped Persona / Capacity Record

slug: role

slug: context

slug: boundary

slug: supersession

slug: representation

slug: category

slug: versioning

slug: authority

slug: indirection

slug: accountability

slug: provenance

slug: traceability

components:
Underlying Bearer Anchor
Aspect or Role Selector
Projection Scope Boundary
Aspect Persistence Criteria
Derived Bearer Record
Property Inheritance and Exclusion Map
Cross-Aspect Linkage
Scoped Identifier Rule
Aspect Lifecycle State
Authority and Review Rule
Aspect Visibility Label
Aspect Conflict Resolution Rule
Aspect Creation Evidence
Privacy or Purpose Limit

mechanisms: Work / Expression / Manifestation / Item Model

essence_one_line: Represent one underlying entity under a defined aspect or role as a linked derived bearer, so properties, rights, obligations, identifiers, and lifecycle rules attach only where they belong.

compression_statement: When a single entity must be reasoned about differently under different roles, capacities, expressions, phases, or contexts, project an aspect-scoped identity: a derived record linked to the underlying bearer, governed by its own scope boundary, persistence criteria, inherited-property map, identifier rule, lifecycle state, and remerge/supersession policy.

core_intuition: The same thing can legitimately count as different “things” for different purposes. Making the aspect explicit prevents both over-collapse, where role-specific facts pollute the whole entity, and over-fragmentation, where every contextual view becomes an unrelated object.

canonical_formula: underlying_entity + aspect_selector + persistence_criteria -> aspectual_individual linked_to underlying_entity

structural_problem: A system needs to attach properties, responsibilities, statuses, identifiers, or histories to one role/aspect of an entity, but the available model either collapses everything into the whole entity or splits the aspect so completely that its connection to the underlying bearer is lost.

trigger_conditions: The same underlying entity participates in multiple roles, capacities, phases, accounts, records, or expressions that have different properties or obligations.

trigger_conditions: Claims made about one aspect should not automatically apply to the whole entity or to other aspects.

trigger_conditions: The aspect has its own start, end, succession, suspension, or validity conditions.

trigger_conditions: Different systems need to refer to the aspect without losing the link to the underlying entity.

trigger_conditions: A role-specific record must survive changes in some underlying properties but not others.

symptoms: Role-specific data is incorrectly treated as universal data about the person, object, work, organization, or system.

symptoms: A change to the underlying entity wrongly retires every role/aspect record, or a role/aspect change wrongly creates a new whole entity.

symptoms: Permissions, obligations, classifications, or analytics leak from one capacity into another.

symptoms: Records cannot answer whether a claim applies to the underlying bearer, one aspect of it, or a historical aspect that has ended.

symptoms: Duplicate records proliferate because every contextual view is treated as an unrelated entity.

root_tension: The model needs both unity and differentiation: enough unity to preserve linkage to the underlying bearer, and enough differentiation to let aspect-scoped properties and lifecycle rules remain local.

anti_signatures: Use Durable Identifier Binding when the main task is assigning a stable handle to a referent rather than deciding what aspectual referent exists.

anti_signatures: Use Identity-Bounded Change when the main question is whether an entity remains the same through modification, not whether a role/aspect should be modeled as a derived bearer.

anti_signatures: Use Canonical Classification when class membership alone is enough and no aspect-specific bearer, lifecycle, or property boundary is needed.

anti_signatures: Use Context Anchor Design when references merely need metadata to resolve correctly and no separate aspectual identity should be created.

anti_signatures: Do not use this pattern to disguise arbitrary profiling, discriminatory segmentation, or unaccountable persona construction as ontology design.

structural_intervention: Introduce an aspect-scoped identity projection: define the underlying bearer, select the role/aspect that warrants separate treatment, create a derived bearer record linked to the underlying entity, and govern the aspect with explicit scope, persistence, inheritance, lifecycle, and reference rules.

action_logic: Name the underlying bearer and decide whether it already has a stable identifier or source-of-truth record.

action_logic: Define the aspect selector: role, legal capacity, account capacity, work expression, phase, context, status, or institutional relation.

action_logic: State which properties are inherited from the underlying bearer, which are local to the aspect, and which are intentionally excluded.

action_logic: Specify persistence criteria for the aspect: what changes it survives, what retires it, what supersedes it, and what creates a successor aspect.

action_logic: Assign a scoped identifier or reference rule that makes the aspect addressable without severing the underlying link.

action_logic: Add a cross-aspect linkage and conflict rule so facts, rights, obligations, and changes do not silently spill across boundaries.

action_logic: Record lifecycle, provenance, authority, and review triggers so the projection remains accountable and reversible when conditions change.

decision_rules: Create an aspectual individual only when the aspect has properties or obligations that must be attached locally and persist under narrower conditions than the underlying entity.

decision_rules: Do not create a projection for a mere filter, view, tag, or report unless downstream decisions treat the projected unit as a bearer of properties.

decision_rules: If the aspect has no independent lifecycle, use classification or context metadata instead of a derived identity.

decision_rules: If two aspects share all relevant properties, lifecycle rules, and obligations, collapse or alias them rather than maintaining separate projections.

decision_rules: When human subjects are involved, require purpose limitation, transparency, appeal, and anti-discrimination checks before creating persistent aspect identities.

target_invariants: The derived bearer remains explicitly linked to the underlying entity.

target_invariants: Aspect-scoped properties do not silently generalize to the whole entity or other aspects.

target_invariants: The aspect has declared persistence and ending conditions.

target_invariants: Identifiers and references reveal or preserve enough scope to prevent equivocation.

target_invariants: Cross-aspect changes are propagated only through declared inheritance and conflict rules.

expected_outcomes: Cleaner records where role-specific facts, rights, obligations, and histories are localized.

expected_outcomes: Fewer false merges between distinct capacities or contexts of the same entity.

expected_outcomes: Fewer false splits because derived identities retain explicit links to their underlying bearer.

expected_outcomes: Improved auditability for identity, status, and lifecycle decisions.

expected_outcomes: Better interoperability between systems that reason at different identity granularities.

Role-Scoped Identity Record

short_definition: Represents the same underlying entity as a distinct role-bearing record with local properties, obligations, and lifecycle state.

distinctive_feature: The selector is an institutional or social role.

distinct_from_parent: The parent covers any aspect; this variant emphasizes formal role and capacity.

still_same_parent_because: It uses the same projection scope, underlying bearer, persistence criteria, and linkage pattern.

use_when: Role-specific duties, permissions, or facts must not spill over to the whole entity.

use_when: The role can start, end, suspend, or transfer independently.

domain: university

example: One person has linked student and employee role records with different rights and dates.

Abstract-Work Expression Projection

short_definition: Separates an abstract work from expressions, manifestations, copies, or performances as linked aspectual bearers.

distinctive_feature: The underlying bearer is an abstract work and the aspect is a realized expression or embodiment.

distinct_from_parent: The parent is general identity projection; this variant uses work-expression-item style distinctions.

still_same_parent_because: It still relies on aspect selectors, derived bearer records, property inheritance, and cross-aspect linkage.

use_when: Different levels of a cultural or intellectual object have different properties.

use_when: Cataloging or rights decisions depend on which level is being referenced.

example_domains: libraries archives musicology publishing

example: A work and a translation are linked but not treated as the same bearer of all properties.

Phase-Scoped Identity Projection

short_definition: Treats an entity under a phase or status as a derived bearer while preserving underlying continuity.

distinctive_feature: The aspect selector is temporal state or phase.

distinct_from_parent: The parent is not necessarily temporal; this variant is state/phase-driven.

still_same_parent_because: It uses the same scope, persistence, lifecycle, and underlying linkage rules.

use_when: Phase-specific facts or obligations matter.

use_when: A phase has its own entry/exit conditions.

example: An episode-of-care record exists as a phase-scoped identity linked to a patient.

near_names:
Qua-Object Modeling
Capacity-Specific Identity
Context-Bound Persona Record


Assignment / Matching Optimization

batch: Third Sweep Candidate Batch 02

aliases:
relationship assignment design
constrained matching system
multi-sided placement optimization
compatibility-aware assignment

source_primes: optimization constraint coupling

related_primes: resource_management scheduling preference fairness feedback boundary consent complexity_time_space governance

slug: optimization

slug: constraint

slug: coupling

slug: scheduling

slug: preference

slug: fairness

slug: feedback

slug: consent

slug: governance

essence_one_line: Form defensible relationships among agents, tasks, resources, or slots by governing feasibility, multi-sided preferences, capacity, fit, fairness, stability, implementation, and rematching.

canonical_formula: participant_sets + feasible_relationship_edges + preferences_and_priorities + capacities + fit_and_fairness_objectives + stability_review + accountable_assignment_rule + consent_and_appeal + handoff + outcome_feedback + rematching -> feasible_legitimate_and_durable_relationship_set

structural_problem: A system must decide which discrete relationships form among agents, tasks, resources, or slots, but local, first-come, purely ranked, or generic bundle selection ignores counterpart-specific compatibility, multi-sided preference, capacity interaction, stability, fairness, and relationship change over time.

prose_essence:
Assignment / Matching Optimization is the intervention pattern for deciding which discrete relationships should form among agents, tasks, resources, roles, services, or slots. The central object is not a person, vacancy, or project in isolation. It is an edge between counterparts whose feasibility and value depend on who is paired with whom, under the other relationships selected at the same time. A highly qualified person may still be incompatible with a specific task; a desirable school may be inaccessible to a particular family; a clinically suitable provider may be unavailable at the needed time or language; a strong mentor may be overloaded by a globally popular profile.

The archetype therefore combines relationship feasibility, multi-sided preferences or priorities, capacity, complementary fit, distributional fairness, stability, implementation, and change over time. It does not require a mathematical solver or market metaphor. A structured panel can instantiate it when the same obligations are explicit and auditable. Its defining move is to govern a set of relationships as a system, including the unmatched state, rather than greedily filling each slot or ranking each participant alone.

This entry is promoted from the exact second-wave candidate reserved by Discrete Commitment Optimization. The parent remains authoritative for generic binary, integer, facility, and bundle commitments. Matching earns independence because bilateral or multi-sided acceptability, blocking pairs, preference strategy, relationship handoff, consent, and rematching repeatedly determine success across domains.

prose_when_to_use:
Use this archetype when the practical question has the form who or what should be paired with which counterpart, and when the answer cannot be obtained safely by ranking only one side. It is especially valuable when compatibility is sparse, when participants have meaningful preferences or unacceptable matches, when slots have different capacities or qualities, when popular counterparts can be overloaded, or when local placements create system-wide gaps and inequities.

The stakes need not be mathematical, commercial, or high volume. A mentoring program with forty participants may require the archetype because relationship quality, reciprocal willingness, privacy, and rematching dominate. A large warehouse may need it because certification, travel, workload, and coverage interact. A public placement system needs it when priorities, fairness, explanation, and appeal shape legitimacy as much as throughput.

Do not use the entry merely because the word assignment appears. Dividing money or hours is allocation when the resource is divisible. Choosing which independent projects proceed is discrete commitment. Scheduling owns when already fixed activities occur. Admission owns who crosses a boundary when no destination relationship is selected. Simple role filling is sufficient when every eligible occupant is interchangeable and no system-level preference, fit, fairness, stability, or revision logic is needed.

prose_structural_problem:
Naive assignment decomposes a relational decision into isolated rankings. A team selects the most qualified available worker for each job, a school admits the highest-priority student, or a platform recommends the highest-scoring counterpart. Each local choice can look defensible while the complete result overloads scarce agents, leaves essential needs uncovered, forms unacceptable relationships, or distributes undesirable placements to the same groups. The error is structural: relationship edges share capacity and their value is counterpart-specific.

A second problem is multi-sided agency. The allocator, participant, receiving institution, and affected public may hold different preferences, priorities, rights, and information. Treating an institutional score as neutral fit hides those conflicts. Preference collection can itself create inequity because knowledgeable actors rank strategically, incomplete lists are misread as indifference, and inaccessible elicitation suppresses refusal or accommodation needs.

A third problem is temporal. A feasible assignment at decision time may fail at handoff, unravel through blocking alternatives, or become obsolete when needs and capacities change. If no unmatched state, appeal, or rematching path exists, people defect informally and the official system loses both data and legitimacy. If the system reoptimizes continuously, it can destroy continuity and trust. The archetype must therefore govern the full relationship lifecycle, not only the initial calculation.

prose_intervention_logic:
Begin by defining every participating side, the granularity of relationships, and whether unmatched outcomes are legitimate. Construct the feasible relationship graph from eligibility, qualification, conflicts, timing, accessibility, safety, and categorical consent. Hard incompatibilities remain outside any benefit score. Then elicit the preferences, priorities, unacceptable pairs, and indifference that matter, making clear whose values govern and how missing or strategic reports are treated.

Model sustainable capacity and relationship value next. Capacity includes workload, travel, supervision, continuity, setup, and reserve, not just nominal vacancies. Value may include complementary skill, need, access, continuity, distance, learning, or service quality, but disputed proxies and weights stay visible. Add distributional obligations, coverage floors, burden caps, stability criteria, and protected priorities as explicit governance choices. Compare plausible assignment rules and shadow-test ordinary, missing-data, boundary, and adversarial cohorts.

The selected assignment set is then authorized, explained, and handed off. Verify current availability, preserve consent or refusal appropriate to the domain, disclose conflicts, provide accommodation and appeal, communicate mutual obligations, and protect sensitive preference data. Monitor whether relationships actually begin and produce acceptable outcomes. Feed failures, appeals, bypass, overload, inequity, and changed conditions into a governed rematching loop that balances adaptation against continuity.

prose_key_components:
### Participant and Slot Universe

Defines the agents, tasks, roles, services, resources, venues, or slots between which relationships may be formed.

Operationally, this component must be represented in a form that decision makers can inspect and update. State inclusion rules, time horizon, granularity, duplicate identities, opt-out status, and whether unmatched outcomes are allowed. Freeze a version for each decision round while retaining late-entry and withdrawal procedures. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by establishing the two or more sides of the relationship decision. It is required because the system quietly excludes eligible participants, invents unavailable slots, or treats an unmatched party as a data error Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Eligibility and Hard Compatibility Structure

Represents pairings that are legally, physically, clinically, ethically, temporally, or operationally possible.

Operationally, this component must be represented in a form that decision makers can inspect and update. Separate non-negotiable eligibility from soft fit. Record the basis and owner of each exclusion, test intersections of constraints, and provide a correction route for stale or erroneous records. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by defining the feasible edge set before preferences or scores are optimized. It is required because a high score can force an unsafe, unlawful, impossible, or conflict-laden match Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Preference and Priority Representation

Captures ordinal preferences, priorities, unacceptable matches, ties, and indifference for every side whose interests matter.

Operationally, this component must be represented in a form that decision makers can inspect and update. Use elicitation that participants can understand, permit partial rankings and refusal, distinguish stated preference from institutional priority, and document how missing preferences are handled. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by making relationship-specific desirability explicit rather than assuming a universal ranking. It is required because the assignment claims mutual fit while encoding only the allocator's convenience or a proxy score Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Capacity and Workload Model

Specifies how many relationships each participant or slot can sustain and how assignment load interacts across time.

Operationally, this component must be represented in a form that decision makers can inspect and update. Represent upper and lower quotas, skill mix, rest, travel, setup, supervision, reserve capacity, and nonlinear overload. Avoid equating headcount with usable capacity. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by preventing individually plausible matches from producing an infeasible or harmful assignment set. It is required because the solution overloads popular agents, strands necessary coverage, or meets nominal counts while failing operationally Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Relationship Value and Fit Model

Defines what makes a pairing useful and how complementary features, needs, distance, continuity, or learning potential affect value.

Operationally, this component must be represented in a form that decision makers can inspect and update. Keep categorical incompatibilities outside the score, expose disputed proxies and weights, validate against outcomes, and allow qualitative judgments where numeric precision is false. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by supporting comparison among feasible matches without reducing all values to one opaque number. It is required because the system optimizes a convenient score that is uncorrelated with actual relationship outcomes Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Fairness and Distribution Governance

States procedural and outcome protections for groups, individuals, neighborhoods, institutions, and successive cohorts.

Operationally, this component must be represented in a form that decision makers can inspect and update. Declare protected attributes, reserve or priority rules, burden limits, tie treatment, temporal fairness, and acceptable tradeoffs. Test both aggregate parity and concentrated harm. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by constraining optimization so fit is not purchased through exclusion or repeated sacrifice. It is required because the assignment is efficient in aggregate while systematically giving worse options, longer waits, or heavier loads to the same parties Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Stability and Blocking Review

Tests whether an assigned participant and an alternative counterpart would both prefer a permissible reassignment, or whether coalitions can undermine the result.

Operationally, this component must be represented in a form that decision makers can inspect and update. Define stability for the domain, including capacities, contracts, priorities, ties, and unacceptable pairs. Where stability is not the objective, document why blocking relationships are tolerable and how churn will be contained. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by addressing a relationship-specific failure absent from ordinary set selection. It is required because participants bypass, abandon, renegotiate, or strategically unravel formally feasible assignments Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Assignment Objective and Rule

Combines feasibility, preferences, fit, stability, coverage, fairness, and operational goals into an accountable selection rule.

Operationally, this component must be represented in a form that decision makers can inspect and update. Separate hard requirements from objectives, publish precedence among conflicting goals, record governance decisions, and compare credible alternative assignment sets rather than presenting a solver output as inevitable. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by turning the relationship model into an authorized assignment decision. It is required because hidden weights and tie rules determine life-affecting pairings without accountable justification Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Consent, Exception, and Appeal Path

Provides notice, refusal or consent where appropriate, conflict disclosure, accommodation, correction, and review of contested matches.

Operationally, this component must be represented in a form that decision makers can inspect and update. Calibrate rights to stakes and domain duties; specify deadlines, independent review, temporary coverage, retaliation protection, and remedies when the original assignment cannot simply be undone. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by preserving participant agency and correcting information or rule failures. It is required because people are trapped in unsafe or unsuitable relationships and must defect informally to obtain review Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Implementation and Handoff Record

Converts a computed or deliberated matching set into accepted schedules, introductions, contracts, responsibilities, access, and support.

Operationally, this component must be represented in a form that decision makers can inspect and update. Verify availability at commitment time, communicate rationale and obligations, protect sensitive preference data, confirm both ends, and track declined or failed handoffs separately from model feasibility. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by bridging the difference between a feasible relationship on paper and an enacted one. It is required because matches evaporate during handoff, double bookings appear, or parties begin with incompatible expectations Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Outcome and Experience Monitor

Measures coverage, fit, stability, fairness, burden, refusals, outcomes, and participant experience after assignments begin.

Operationally, this component must be represented in a form that decision makers can inspect and update. Disaggregate by group and assignment type, distinguish match quality from downstream support, watch gaming and attrition, and combine administrative evidence with confidential participant feedback. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by testing whether modeled compatibility produces real value and acceptable distribution. It is required because the system repeats poor pairings because successful placement counts mask harm, churn, or unequal outcomes Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Rematching and Rule Revision Loop

Governs changes when capacity, eligibility, preferences, needs, or outcomes evolve after the initial assignment.

Operationally, this component must be represented in a form that decision makers can inspect and update. Define trigger thresholds, continuity protections, reassignment costs, waiting-list effects, versioning, notice, and limits on churn. Feed validated failures back into data, constraints, elicitation, and policy. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by making assignment a lifecycle rather than a one-time optimization event. It is required because stale matches persist despite changed conditions or constant reoptimization destroys trust and continuity Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

prose_common_mechanisms:
### Deferred-Acceptance Matching

Iteratively processes proposals and tentative acceptances to produce a stable allocation under declared preferences and priorities.

Use it when participants or institutions have ordinal preferences and avoidance of blocking pairs is important Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. The proposing side, priority construction, ties, incomplete lists, and strategic incentives materially affect results; publish and govern them.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Deferred acceptance is one implementation family. The archetype also covers non-stable assignment, optimization, deliberative matching, and rematching governance.

### Minimum-Cost Bipartite Assignment

Selects a feasible set of edges that minimizes cost or maximizes value across two entity sets under capacity constraints.

Use it when relationship values can be credibly compared and feasibility is well specified Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Do not bury consent, categorical incompatibility, protected priorities, or contested values inside a scalar cost.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. The assignment model calculates a candidate set but does not establish legitimate objectives, elicitation, appeal, or implementation.

### Matching with Contracts

Represents a match together with terms such as role, shift, compensation, duration, service level, or training obligation.

Use it when acceptability depends on relationship terms rather than counterpart identity alone Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Control combinatorial complexity and make terms understandable; preserve labor, consent, and anti-discrimination protections.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Contracts extend the matching mechanism; contract drafting or discrete bundle selection alone is not this archetype.

### Preference-Elicitation Protocol

Collects rankings, acceptable sets, constraints, and indifference without forcing false precision or exhaustive comparison.

Use it when participant preferences influence quality, legitimacy, stability, or consent Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Explain consequences, allow partial lists and corrections, protect privacy, test framing effects, and avoid penalizing people with less strategic knowledge.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Preference elicitation supplies an input and is not a complete assignment intervention.

### Compatibility Matrix and Edge Audit

Builds and reviews the feasible relationship graph from qualification, conflict, timing, access, language, location, and safety evidence.

Use it when eligibility and compatibility are heterogeneous or frequently changing Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Every excluded edge needs an interpretable basis and owner; audit missing data, proxy discrimination, stale records, and intersections.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. A matrix is a representation component unless it is connected to the full assignment rule and lifecycle.

### Capacity, Reserve, and Quota System

Encodes upper and lower capacities, reserved places, group priorities, coverage floors, and workload limits.

Use it when scarce slots and distribution obligations must be reconciled Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Model real workload and opportunity cost, not nominal counts; review whether reserves correct inequity or create avoidable displacement.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Quotas constrain matching but do not by themselves decide which relationships should form.

### Transparent Tie-Breaking Lottery

Resolves genuine ties through an auditable random or rotating process rather than hidden discretion.

Use it when many participants are equivalent under legitimate criteria and deterministic ordering would create arbitrary advantage Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Use only after valid differences are exhausted, publish seed or procedure as appropriate, protect privacy, and test repeated-cohort effects.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. A lottery is a tie mechanism; using randomization for general scarcity without relationship-specific structure belongs elsewhere.

### Human-Reviewed Match Panel

Uses structured multi-party review for qualitative compatibility, exceptional cases, and contested data that cannot be safely reduced to scores.

Use it when stakes are high, cases are heterogeneous, and contextual judgment adds value Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Require conflict disclosure, common criteria, documented reasons, calibration, participant voice, and appeal; monitor bias and inconsistency.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. A panel becomes matching machinery only when it decides relationships within the explicit feasible, capacity, fairness, and revision structure.

### Rolling Rematch and Reoptimization

Reopens some relationships when arrivals, withdrawals, failures, needs, or capacity changes cross governed triggers.

Use it when the environment is dynamic and a static assignment would quickly become obsolete Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Penalize needless churn, preserve continuity, protect those displaced by changes, and prevent strategic timing from dominating access.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Continuous scheduling or generic recalibration is not enough; this mechanism serves a relationship-specific assignment lifecycle.

### Assignment Shadow Test

Runs a proposed rule against historical, simulated, boundary, and counterfactual cohorts before live commitment.

Use it when changes to objectives, priorities, or data could create systemic redistribution or instability Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Do not treat historical incumbency as truth; include new populations, missing data, adversarial reports, and alternative policies.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Shadow testing validates a rule but does not authorize or implement the relationships.

prose_failure_modes:
### Compatibility Oversimplification

A scalar fit score hides categorical conflicts, missing data, accessibility needs, or context-specific incompatibility. Detect it through edge-level appeals, subgroup mismatch, boundary-case review, and disagreement between numeric fit and experienced outcomes Respond by separate hard eligibility, expand qualitative compatibility evidence, and revise proxy features

### One-Sided Objective Laundering

The allocator's cost or throughput objective is labeled overall fit while participant interests are omitted. Detect it through objective provenance review, preference attainment gaps, refusals, and burden concentration Respond by declare each side, introduce consent and preference evidence, and govern objective precedence

### Blocking and Bypass Instability

Participants discover mutually preferred alternatives and swap, defect, or create informal markets. Detect it through transfer requests, side deals, churn, unmatched vacancies, and simulated blocking pairs Respond by revise priorities or stability rule, provide legitimate swap paths, and correct hidden incompatibilities

### Strategic Reporting Inequity

Participants with better information manipulate rankings while others report sincere or incomplete preferences. Detect it through sensitivity to truncation or rank changes, advisor disparities, repeated institutional gaming, and cohort anomalies Respond by simplify elicitation, explain incentives, test strategy exposure, and redesign manipulable rules

### Capacity Illusion

Nominal slots ignore workload, setup, travel, complexity, supervision, or correlated demand. Detect it through overtime, delays, cancellations, quality decline, and early relationship failure despite full placement Respond by replace counts with sustainable load, preserve reserve capacity, and rematch before overload compounds

### Fairness Hidden by Aggregate Score

A high total objective masks systematically worse relationships or heavier burdens for a subgroup. Detect it through disaggregated preference attainment, distance, delay, quality, workload, appeals, and long-run opportunity Respond by add explicit fairness constraints, protected review, and cohort-level distribution governance

### Forced Match Failure

The rule treats every participant as matchable and suppresses unacceptable or no-match outcomes. Detect it through rapid refusal, no-show, grievance, safety incident, silent disengagement, and coerced acceptance Respond by allow unacceptable edges and unmatched states, preserve coverage contingencies, and strengthen consent and appeal

### Handoff Attrition

A computed relationship is never accepted or activated because availability, expectations, or contact fails at implementation. Detect it through gap between assignment and start, repeated no-response, double bookings, and early dissolution Respond by verify at commitment time, facilitate introductions, communicate obligations, and route failed handoffs into rematching

### Rematching Churn

Frequent reoptimization improves current scores while destabilizing learning, care, trust, and operational plans. Detect it through relationship half-life, displacement cascades, repeated waiting-list movement, and participant fatigue Respond by raise triggers, penalize churn, protect continuity windows, and limit the reopened assignment neighborhood

prose_neighbor_distinctions:
### Discrete Commitment Optimization

Selects indivisible projects, quantities, sites, or bundles under constraints.

Boundary rule: Use Assignment / Matching Optimization when the decision object is a relationship and the design must govern preferences on multiple sides, compatibility edges, blocking pairs or stability, consent, relationship fairness, failed handoffs, and rematching. Use Discrete Commitment Optimization when generic whole-option selection is sufficient.

Hybrid cases should assign each design obligation to its proper owner rather than using Assignment / Matching Optimization as an umbrella for all adjacent work.

### Constrained Resource Allocation

Distributes divisible resources across competing uses under constraints.

Boundary rule: Use allocation for quantities that can be split; use matching for discrete relationships or slots whose value depends on which counterpart receives them. A system may allocate hours after it matches a worker to a role, but the obligations remain separable.

Hybrid cases should assign each design obligation to its proper owner rather than using Assignment / Matching Optimization as an umbrella for all adjacent work.

### Priority-Based Admission

Orders or admits applicants to scarce opportunities by eligibility and priority.

Boundary rule: Admission determines who crosses an access boundary; matching determines which particular participant-slot relationships form across an assignment set. Use both when admissions and placement are distinct stages.

Hybrid cases should assign each design obligation to its proper owner rather than using Assignment / Matching Optimization as an umbrella for all adjacent work.

### Role-Slot Filling

Ensures named roles or shifts receive qualified occupants.

Boundary rule: Use simple filling when any eligible occupant is equivalent and local placement suffices. Use matching when bilateral preferences, complementary fit, stability, distributional fairness, or rematching materially change the decision.

Hybrid cases should assign each design obligation to its proper owner rather than using Assignment / Matching Optimization as an umbrella for all adjacent work.

### Mechanism Design Protocol

Designs rules so strategic behavior and private information produce desired collective outcomes.

Boundary rule: Mechanism design may govern incentives and reporting in a matching market. Assignment / Matching Optimization owns the relationship model, feasible edges, capacities, selection set, implementation, outcome review, and rematching even when incentive analysis is modest.

Hybrid cases should assign each design obligation to its proper owner rather than using Assignment / Matching Optimization as an umbrella for all adjacent work.

### Scheduling and Time-Window Coordination

Places activities in time while respecting sequence, duration, and temporal capacity.

Boundary rule: Scheduling owns when activities occur. Matching owns which agents, tasks, or resources are paired. A staff roster often needs both, but temporal feasibility does not replace compatibility, consent, stability, or relationship fairness.

Hybrid cases should assign each design obligation to its proper owner rather than using Assignment / Matching Optimization as an umbrella for all adjacent work.

### Objective Weighting Governance

Makes contested objectives and weights visible and accountable.

Boundary rule: Weight governance is a reusable component when matching goals conflict. Matching remains distinct because it adds multi-sided preferences, edge feasibility, capacities, stability, handoff, appeal, and rematching.

Hybrid cases should assign each design obligation to its proper owner rather than using Assignment / Matching Optimization as an umbrella for all adjacent work.

### Impedance Matching and Coupling Optimization

Adjusts interfaces so signals, energy, or transfers couple effectively across a boundary.

Boundary rule: The shared word matching does not establish identity. Use impedance matching for interface compatibility and transfer efficiency; use Assignment / Matching Optimization for choosing discrete relationships among agents, slots, tasks, or resources.

Hybrid cases should assign each design obligation to its proper owner rather than using Assignment / Matching Optimization as an umbrella for all adjacent work.

prose_cross_domain_examples:
### Graduate Medical Education

A national process matches applicants to residency programs using submitted preferences, program priorities, eligibility, capacities, couples constraints, and a governed stable-matching rule, followed by verification, appeal, and vacancy remediation.

Why it fits: the decision forms multi-sided relationships with preferences, capacity, stability concerns, strategic incentives, and high transition costs The design is evaluated by fill rate, blocking or bypass behavior, applicant and program outcomes, distribution by group and specialty, appeals, and later attrition

### Public School Placement

A district assigns students to schools from family preferences, eligibility, travel, sibling rules, program needs, priorities, reserves, and capacities, then publishes tie rules and operates appeals and wait-list rematching.

Why it fits: particular student-school relationships matter and fairness cannot be reduced to admitting the highest-ranked applicants The design is evaluated by access, travel burden, segregation, preference attainment, stability, appeals, and outcomes across successive cohorts

### Clinical Appointment and Care-Team Assignment

A health network matches patients to appointment slots and care teams using urgency, specialty, language, accessibility, continuity, location, workload, and patient constraints while preserving refusal and escalation paths.

Why it fits: the useful service depends on a compatible patient-slot-team relationship rather than a divisible allocation alone The design is evaluated by timely access, continuity, no-shows, clinician load, patient experience, inequities, and rematch frequency

### Field Service Operations

A utility assigns technicians to jobs using certification, hazard authorization, travel, tools, shift capacity, local knowledge, and fairness of undesirable assignments, then updates the roster after outages or absences.

Why it fits: feasible pairings, whole assignments, workload interactions, and dynamic rematching determine operational success The design is evaluated by coverage, travel, overtime, first-time resolution, safety, burden distribution, and schedule churn

### Mentoring Network

A professional association forms mentor-mentee relationships from goals, expertise, lived experience, communication preferences, conflicts, capacity, and reciprocal acceptability, with facilitated introduction and governed rematching.

Why it fits: relationship quality, bilateral willingness, handoff, and failure recovery are more important than simply filling program seats The design is evaluated by match activation, trust, developmental progress, burden, early exits, rematches, and equity of access to high-demand mentors

### Organ Donation and Exchange

A kidney exchange selects compatible donor-recipient cycles and chains under clinical constraints, timing, consent, fairness, and operational feasibility, with contingencies for withdrawal.

Why it fits: the assignment object is a set of interdependent biological relationships whose feasibility and value depend on exact counterparts The design is evaluated by transplants completed, clinical outcomes, waiting-time equity, chain failure, withdrawals, and protected access

### Interview Panel Assignment

An employer assigns trained interviewers to candidates while avoiding conflicts, distributing workload, ensuring required expertise and representation, and preserving candidate accommodations.

Why it fits: the relationships have categorical compatibility and fairness requirements beyond simple calendar placement The design is evaluated by conflict-free coverage, panel calibration, workload, candidate experience, cancellations, and distributional patterns

prose_non_examples:
### Divisible Budget Allocation

A committee divides a budget among departments by need and marginal return. It fails the archetype boundary because the decision is how much fungible resource each use receives, not which counterpart relationship forms Route the case to Constrained Resource Allocation when that is the actual design object.

### Capital Project Portfolio

A city selects a feasible bundle of bridge and park projects under a fixed capital budget. It fails the archetype boundary because project inclusion is a whole-option commitment without multi-sided preferences, blocking pairs, consent, or rematching Route the case to Discrete Commitment Optimization when that is the actual design object.

### Fixed-Team Shift Scheduling

A manager places already assigned staff on a calendar while honoring rest and coverage constraints. It fails the archetype boundary because counterpart assignment is settled and only timing remains open Route the case to Scheduling and Time-Window Coordination when that is the actual design object.

### Eligibility Admission

A program ranks applicants and admits the first fifty without assigning them to distinct providers, classes, or placements. It fails the archetype boundary because the intervention governs access across a boundary, not relationship selection among multiple destinations Route the case to Priority-Based Admission when that is the actual design object.

prose_tradeoffs:
### Fit versus Legibility

Richer relationship models may predict fit better but become harder for participants and reviewers to understand. The design should not pretend this tension disappears. Use layered explanations, interpretable hard constraints, simple baselines, shadow comparisons, and qualitative review for disputed signals and make the selected position visible to affected actors.

### Stability versus Welfare and Choice

A stable result can sacrifice aggregate value or favor the side whose proposals drive the mechanism. The design should not pretend this tension disappears. Use explicit domain rationale, comparison of proposer sides and alternative rules, appeal, and monitoring of exits and bypass behavior and make the selected position visible to affected actors.

### Efficiency versus Distribution

Maximizing total score can concentrate bad assignments, travel, waits, or workload in already disadvantaged groups. The design should not pretend this tension disappears. Use hard distribution guardrails, burden caps, reserves, group and individual audits, and accountable tradeoff decisions and make the selected position visible to affected actors.

### Responsiveness versus Continuity

Rematching can improve current fit while breaking trust, learning, care continuity, or team cohesion. The design should not pretend this tension disappears. Use trigger thresholds, churn penalties, protected periods, participant consent, displacement support, and staged rematching and make the selected position visible to affected actors.

### Preference Respect versus Strategy Resistance

More preference-sensitive rules can invite strategic rankings, while strategy-proof rules may limit other objectives. The design should not pretend this tension disappears. Use clear incentives, low-burden truthful elicitation, manipulation testing, and sanctions focused on institutional gaming rather than participant sophistication and make the selected position visible to affected actors.

prose_invariants_to_preserve:
### Relationships Are the Decision Object

The design compares sets of counterpart-specific relationships, not merely independent people or options. Preserve it by representing explicit edges and verifying both endpoints at handoff A violation is indicated by the same ranking would be used regardless of who is paired with whom

### Hard Incompatibility Is Not a Soft Penalty

Safety, legality, qualification, consent, and categorical feasibility cannot be traded away for objective score. Preserve it by separating edge eligibility from scoring and auditing every exception A violation is indicated by a sufficiently high benefit score overrides an impermissible edge

### Affected Preferences and Priorities Are Visible

Every side whose preference or institutional priority governs outcomes is declared and distinguishable. Preserve it by publishing elicitation, missing-data, precedence, and tie rules A violation is indicated by allocator convenience is presented as neutral fit or participant preference

### Capacity Means Sustainable Relationship Load

Capacity represents workload and interaction effects rather than nominal headcount alone. Preserve it by including time, complexity, supervision, travel, reserve, and nonlinear overload A violation is indicated by filled slots coexist with predictable burnout, delay, or unusable service

### Fairness and Stability Are Tested at Set Level

Distribution and blocking risks are evaluated across the complete assignment set and over cohorts. Preserve it by running disaggregated, counterfactual, and successive-round audits A violation is indicated by individual matches look plausible while system-wide inequity or unraveling grows

### Assignment Remains Contestable and Revisable

High-stakes decisions have explanation, correction, appeal, and governed rematching paths. Preserve it by retaining decision records, independent review, trigger rules, and continuity protections A violation is indicated by participants can obtain relief only by informal defection, political access, or silent attrition

prose_target_outcomes:
A successful design produces a feasible and enacted set of relationships, not merely a high model score. Hard compatibility and capacity constraints hold at handoff and over time. Participants understand the relevant rule and can correct material data. Preference attainment, service quality, workload, and opportunity are acceptable in aggregate and do not hide concentrated harm. Necessary roles and needs receive coverage without repeatedly sacrificing the same people or communities.

Durability is also an outcome. Blocking, bypass, refusal, early dissolution, and informal swaps remain within the range justified by the domain. Appeals identify real errors and can produce remedy without relying on private influence. Rematching responds to changed needs and failures while preserving valuable continuity. The assignment process accumulates knowledge: outcomes refine compatibility evidence, capacity assumptions, elicitation, priorities, and fairness safeguards rather than merely retraining an opaque score.

Evaluation should compare credible rules and unmatched baselines. Measures include feasible-edge violations, fill and activation rates, preference attainment on all relevant sides, coverage, travel and wait burden, sustainable workload, stability, appeal and correction outcomes, distribution across protected and operational groups, relationship quality, attrition, rematch frequency, and downstream service or developmental outcomes.

prose_parameter_dimensions:
### Preference Influence

Sets how strongly stated preferences affect the assignment relative to institutional objectives and priorities. Low settings create participants receive formally efficient but unwanted or unstable relationships; high settings create strategic reporting and popular-counterpart concentration can overwhelm coverage or fairness. Tune against stakes, preference reliability, alternatives, stability tests, and participant outcome evidence and record the rationale.

### Compatibility Strictness

Sets the boundary between prohibited, discouraged, neutral, and preferred edges. Low settings create unsafe or predictably poor matches remain feasible; high settings create stale or proxy-based exclusions shrink options and reproduce inequity. Tune against domain standards, observed outcomes, appeals, missing-data tests, and protected-group edge audits and record the rationale.

### Stability Requirement

Sets how much the rule seeks to prevent mutually preferred alternative relationships. Low settings create bypass, swapping, renegotiation, and rapid attrition can unravel the assignment; high settings create stable outcomes can privilege one side or preserve undesirable priorities. Tune against domain exit costs, strategic incentives, participant rights, and comparison with welfare and fairness alternatives and record the rationale.

### Fairness Constraint Strength

Sets mandatory reserves, parity ranges, burden caps, or priority protections. Low settings create efficiency scores concentrate poor outcomes and scarce access; high settings create rigid constraints may strand capacity or ignore meaningful need and fit differences. Tune against legal duties, distributional history, causal fairness analysis, stakeholder governance, and successive-cohort simulation and record the rationale.

### Rematching Sensitivity

Sets when changed conditions or poor outcomes justify reopening relationships. Low settings create harmful or obsolete matches persist and new capacity remains unavailable; high settings create constant churn destroys continuity and invites strategic timing. Tune against relationship duration, reversibility, failure severity, displacement cost, waiting-list effects, and monitored outcomes and record the rationale.

prose_variants_and_near_names:
### Stable Two-Sided Matching

Forms relationships between two sides using preferences or priorities while reducing blocking pairs.

Its load-bearing distinction is stability and which side proposes materially affect outcomes, incentives, and legitimacy. Preserve it as a named variant when that difference changes design or failure analysis. Collapse it when preferences are absent and the task is ordinary minimum-cost assignment

### Capacitated Task Assignment

Pairs workers, machines, teams, or resources with tasks under skills, workload, time, and travel constraints.

Its load-bearing distinction is operational compatibility and workload are central while participant preference may be limited. Preserve it as a named variant when that difference changes design or failure analysis. Collapse it when only the timing of already fixed relationships is being scheduled

### Mentor and Team Complementarity Matching

Builds developmental or collaborative relationships from goals, skills, working style, accessibility, and reciprocal willingness.

Its load-bearing distinction is relationship quality is emergent and qualitative, making handoff, feedback, and rematching especially important. Preserve it as a named variant when that difference changes design or failure analysis. Collapse it when participants are merely placed into interchangeable vacancies

### Public-Service and School Matching

Assigns people to schools, clinics, housing, appointments, or service providers under eligibility, preferences, priorities, capacity, and public fairness duties.

Its load-bearing distinction is procedural legitimacy, protected access, explanation, appeal, and successive-cohort effects are load bearing. Preserve it as a named variant when that difference changes design or failure analysis. Collapse it when the decision is only whether an applicant is admitted at all

### Matching with Terms

Forms relationships whose acceptability depends on a package of role, shift, compensation, location, or service conditions.

Its load-bearing distinction is the edge is a counterpart-plus-terms contract rather than a bare pairing. Preserve it as a named variant when that difference changes design or failure analysis. Collapse it when the main decision is selection of a generic contract bundle without reciprocal relationship preferences

raw_bundle_path: solution_archetypes/_Batches2/Third Sweep Candidate Batch 02/assignment_matching_optimization_solution_archetype_output_bundle.zip


Assimilation Ceiling Guarding

batch: Batch 10

canonical_family: Overextension & Load Fragility

slug: eutrophication

slug: threshold

slug: hysteresis

slug: bioaccumulation

slug: nonlinearity

slug: homeostasis

slug: resilience

slug: boundedness

essence_one_line: Limit a beneficial input before the receiver’s assimilation ceiling turns the input into a self-amplifying degrading load.

compression_statement: A system can absorb and benefit from some input, but only within its assimilation capacity. When input crosses the ceiling, the surplus becomes load: it consumes buffers, clearance paths, attention, oxygen, liquidity, repair capacity, trust, or physiological resilience; then feedback makes the damaged state persist even after input is reduced. Assimilation Ceiling Guarding maps the beneficial range, monitors secondary-resource depletion, gates or diverts input before inversion, and uses stronger exit/recovery rules once hysteresis appears.

core_intuition: More of a good thing is still good only while the receiving system can metabolize it. Once the receiver spends more capacity handling the input than benefiting from it, the input changes category from resource to burden.

canonical_formula: allow_input ⇔ input_rate ≤ assimilation_capacity − protected_margin AND d(net_benefit)/d(input) > 0; trigger_guarding when surplus_load > clearance_rate or secondary_resource_slope < −threshold; recovery_exit_threshold < entry_threshold under hysteresis

structural_problem: An initially beneficial input crosses the system’s assimilation ceiling, becomes a degrading load, depletes a secondary resource, and can lock the system into a worse regime that does not automatically reverse when the input is removed.

trigger_conditions: An input, exposure, subsidy, nutrient, demand stream, training load, information flow, credit flow, or stimulation level is beneficial at low or moderate levels.

trigger_conditions: The receiving system has bounded assimilation, clearance, repair, attention, governance, liquidity, oxygen, recovery, or coordination capacity.

trigger_conditions: Performance metrics reward gross input, expansion, or short-term output more than net absorption and recovery capacity.

trigger_conditions: Excess input can consume a secondary resource that is not measured as the primary constraint.

trigger_conditions: Feedback is delayed enough that the ceiling is crossed before damage is visible.

trigger_conditions: The damaged state has hysteresis: the exit path requires lower input, active cleanup, or different interventions than the entry path.

symptoms: Additional input continues or accelerates even after marginal benefit has flattened or turned negative.

symptoms: The system appears productive briefly, then begins to require emergency buffers, cleanup, rework, bailouts, recovery time, or external support.

symptoms: Secondary resources such as oxygen, trust, liquidity, attention, immune reserve, staff capacity, soil quality, or technical maintainability decline while primary throughput looks high.

symptoms: Reducing the input does not quickly restore prior function because accumulated load, regime shift, or depleted buffers persist.

symptoms: Actors defend more input by pointing to earlier beneficial response while ignoring the ceiling shift and delayed collapse signal.

symptoms: Downstream systems absorb hidden cleanup costs after upstream gains have been recorded.

root_tension: The same input that creates value inside an assimilation range becomes destructive beyond that range, and incentives often measure the input or early benefit before they measure receiver overload and delayed regime damage.

anti_signatures: The input is harmful at every level rather than beneficial within a bounded range.

anti_signatures: The issue is only diminishing returns without degradation, secondary-resource depletion, or hysteresis.

anti_signatures: The system is merely saturated, but surplus input can be queued or rejected without persistent damage.

anti_signatures: The problem is a repeated oscillation around a setpoint rather than one-way degradation after over-input.

anti_signatures: The dominant issue is support-tail reach or aggregate carrying capacity rather than input inversion within a receiving system.

structural_intervention: Define the assimilation ceiling, monitor the secondary resources consumed by excess input, and enforce input gating, diversion, tapering, cleanup, and recovery rules before beneficial input inverts into degrading load.

action_logic: Name the input and the receiving subsystem whose assimilation capacity is being tested.

action_logic: Map the response curve: ineffective zone, beneficial range, saturation zone, harmful inversion zone, and hysteretic recovery zone.

action_logic: Identify the secondary resources that excess input consumes or depletes.

action_logic: Instrument both primary output and secondary-resource depletion, with faster feedback near the ceiling.

action_logic: Create a protected margin below the assimilation ceiling rather than running at the estimated ceiling.

action_logic: Rate-limit, stage, buffer, divert, or reject input when marginal net benefit declines or secondary resources deteriorate.

action_logic: Define stronger recovery and cleanup actions once the system crosses into a degraded regime.

action_logic: Recalibrate the ceiling after stress, damage, adaptation, seasonality, or context change.

decision_rules: Stop escalation when marginal net benefit is nonpositive even if gross output is still rising.

decision_rules: Switch from optimization to protection when secondary-resource depletion exceeds the warning threshold.

decision_rules: Use the lower hysteresis exit threshold, not the original entry threshold, when returning from a degraded regime.

decision_rules: Do not accept additional input unless clearance, recovery, repair, or assimilation capacity has also increased.

decision_rules: Treat uncertain ceiling estimates as upper bounds, not targets.

target_invariants: Net benefit remains positive after assimilation and cleanup costs.

target_invariants: Secondary resource reserves remain above protected floors.

target_invariants: Clearance and recovery capacity are not cannibalized to sustain apparent throughput.

target_invariants: The system can reduce input quickly enough to avoid irreversible or expensive regime shift.

target_invariants: Actors cannot use early low-dose benefits as proof that high-dose escalation is safe.

expected_outcomes: Input levels remain within a durable beneficial range.

expected_outcomes: Degrading surplus load is detected before collapse becomes self-amplifying.

expected_outcomes: Recovery and cleanup are triggered early enough to avoid hysteretic lock-in.

expected_outcomes: Stakeholders see the true cost of excess input rather than only its initial productivity.

expected_outcomes: Domain-specific overshoot patterns such as eutrophication, leverage blowups, overload, tolerance, and burnout become governable through the same structural logic.

Eutrophication Load Guarding

short_definition: Limit nutrient input and restore clearance capacity before beneficial nutrient load depletes oxygen and flips an ecosystem into a degraded regime.

distinctive_feature: Ecological nutrient assimilation and oxygen reserve are explicit.

distinct_from_parent: Domain-specific to ecological nutrient and oxygen dynamics.

still_same_parent_because: It follows the same beneficial-input inversion and secondary-resource depletion pattern.

use_when: Nutrient enrichment initially boosts productivity but threatens oxygen depletion, algal blooms, or regime shift.

Organizational Absorptive Load Control

short_definition: Pace growth inputs such as hiring, funding, customers, mandates, or projects so coordination and culture can absorb them.

distinctive_feature: Assimilation capacity is managerial, cultural, operational, or cognitive.

distinct_from_parent: Specializes the input ceiling to organizations and institutions.

still_same_parent_because: The causal chain remains input inversion into degrading load.

use_when: Organizational growth inputs create early gains but overwhelm onboarding, management, support, or trust.

Leverage Assimilation Guarding

short_definition: Constrain credit or leverage growth before liquidity, collateral quality, or refinancing capacity is depleted.

distinctive_feature: Secondary resource is financial resilience or liquidity.

distinct_from_parent: Domain-specific to finance/economics.

still_same_parent_because: More input becomes fragility once the receiver cannot assimilate it.

use_when: Credit or leverage fuels expansion but creates fragility beyond repayment and liquidity capacity.

Traffic or Alert Overload Guarding

short_definition: Rate-limit useful traffic, logs, alerts, or feature rollout before the operations system spends more capacity handling input than maintaining service.

distinctive_feature: Input appears useful but consumes platform or human response capacity.

distinct_from_parent: Technical-operations specialization.

still_same_parent_because: It follows the same ceiling, surplus load, and recovery pattern.

use_when: Demand or telemetry is valuable until processing and human response capacity is saturated and degraded.

near_names:
Overshoot Collapse Prevention
Beneficial Input Inversion Guardrail
Eutrophication Prevention