Proportional Response Design

batch: First Iteration/Batch 17/Wave 2

slug: linearity

slug: proportionality

slug: nonlinearity

slug: threshold

slug: constraint

slug: invariance

slug: optimization

essence_one_line: Match response intensity proportionally to input magnitude so intervention is predictable, explainable, and resistant to overreaction or underreaction.

compression_statement: When inputs differ by magnitude but responses are arbitrary, flat, excessive, or inconsistent, design a proportional response by defining the input measure, response variable, scaling function, bounds, exceptions, and review path.

core_intuition: A system becomes easier to reason about when similar cases receive similar responses and larger inputs receive larger responses for stated reasons. Proportional response design turns magnitude into calibrated action rather than leaving each case to improvisation, panic, favoritism, or blunt uniformity. Its danger is false precision: not every important difference is measurable, and not every response should remain linear near thresholds, rights boundaries, safety limits, or nonlinear failure zones.

canonical_formula: valid input magnitude + explicit response variable + calibrated response function + proportionality rule + floors/ceilings + exception/review path -> predictable scaled response without arbitrary overreaction

structural_problem: A system receives inputs of differing magnitude—severity, demand, use, risk, need, harm, exposure, workload, or error—but responds with inconsistent, arbitrary, flat, excessive, or underpowered action.

trigger_conditions: The input varies by degree rather than by simple yes/no category, and response intensity should vary with it.

trigger_conditions: Equal response to unequal cases creates waste, unfairness, overload, insufficient protection, or loss of legitimacy.

trigger_conditions: Ad hoc discretion produces case-to-case inconsistency, escalation bias, favoritism, or unpredictable burden.

trigger_conditions: The system needs a stable rule that operators, affected parties, or downstream systems can understand and anticipate.

trigger_conditions: The input-output relation is expected to remain mostly proportional within the relevant operating range.

trigger_conditions: The response can be bounded by minimum guarantees, maximum caps, rights constraints, safety limits, or exception review.

trigger_conditions: Measurement is good enough to support calibration, or uncertainty can be represented explicitly.

symptoms: Small cases receive the same heavy response as severe cases, causing resentment, waste, or avoidable harm.

symptoms: Serious cases receive only ordinary response because the system lacks a scaling rule.

symptoms: Two similar cases receive very different treatment because the rule is informal or opaque.

symptoms: Decision makers argue about whether a response is too much or too little without shared anchors.

symptoms: Users, teams, or affected parties cannot predict how much resource, penalty, support, attention, or price will follow from a given magnitude.

symptoms: A rule appears objective but has hidden cliffs, arbitrary bands, or caps that distort proportionality.

symptoms: Operators overfit the response to the latest salient incident rather than to the measured size of the case.

root_tension: The system needs both consistency and context. A proportional rule creates predictability by scaling response with input magnitude, but real cases contain uncertainty, nonlinearities, minimum obligations, maximum tolerances, and contextual factors that can make a purely mechanical rule unjust or unsafe.

anti_signatures: The correct response changes regime at a threshold, tipping point, emergency boundary, or saturation point; nonlinear_threshold_response is likely closer.

anti_signatures: The goal is to keep an input within a safe/effective range rather than scale response with magnitude; therapeutic_window_management may be closer.

anti_signatures: The input is not meaningfully measurable, rankable, or comparable, and forced scaling would create false precision.

anti_signatures: Every case requires deliberative judgment because values, rights, identity, or context dominate the magnitude relation.

anti_signatures: The primary problem is scarcity allocation under hard constraints rather than response calibration; resource_rationing or priority rules may be closer.

anti_signatures: A uniform minimum response is required for dignity, safety, legal compliance, or baseline service regardless of input magnitude.

anti_signatures: The system is in crisis or runaway amplification where proportional response would underreact.

structural_intervention: Define a calibrated mapping from input magnitude to response intensity, then bound and review that mapping so it remains predictable, proportionate, and safe under real conditions.

action_logic: Name the input dimension that should drive response intensity, such as severity, demand, use, risk, need, load, exposure, duration, or impact.

action_logic: Name the response variable that will be scaled, such as resource, staffing, dose, penalty, scrutiny, price, support, restriction, or remedy.

action_logic: Determine the relevant operating range where proportional scaling is appropriate and where another archetype may be needed.

action_logic: Create a response function: linear, monotonic, piecewise-linear, weighted, banded, or ratio-based as appropriate to the evidence and operational need.

action_logic: Set calibration anchors using reference cases, empirical observations, normative commitments, historical baselines, or benchmark scenarios.

action_logic: Add floors, ceilings, caps, minimum obligations, maximum tolerances, and safety bounds.

action_logic: Define exception rules for nonlinear zones, rights constraints, uncertainty, contextual inequity, severe outliers, or measurement failure.

action_logic: Make the rule visible enough that affected parties and operators can understand, contest, and improve it.

action_logic: Review outcomes, compare edge cases, test for gaming or burden inequity, and recalibrate the response function over time.

decision_rules: Use proportional response design when input magnitude is meaningful and response intensity should vary predictably with it.

decision_rules: Prefer the simplest scaling rule that captures the important magnitude relation without hiding uncertainty or context.

decision_rules: Keep the response monotonic unless a cap, floor, saturation point, or exception explicitly justifies a non-monotonic relation.

decision_rules: Add lower bounds when baseline duty, safety, dignity, or service quality must be preserved even for small inputs.

decision_rules: Add upper bounds when unchecked proportional scaling would become punitive, unaffordable, unsafe, or operationally impossible.

decision_rules: Route cases to nonlinear_threshold_response when the response must change regime, not merely increase by degree.

decision_rules: Route cases to review when measurement confidence is low, contextual burden is unusual, or the rule would create a disproportionate practical impact.

decision_rules: Do not use the appearance of mathematical proportionality to launder bias, hidden values, weak evidence, or arbitrary weights.

target_invariants: Similar inputs receive similar responses unless a stated exception applies.

target_invariants: Larger inputs receive larger or more intensive responses within the intended operating range.

target_invariants: The response remains bounded by minimum obligations and maximum tolerable effects.

target_invariants: The input measure remains valid enough to justify the response magnitude.

target_invariants: Affected parties can understand the basis for the response at the level appropriate to the domain.

target_invariants: Exceptions are visible, reviewable, and constrained rather than hidden discretion.

target_invariants: The rule is recalibrated when outcomes, incentives, measurements, or operating conditions drift.

expected_outcomes: Reduced arbitrary overreaction and underreaction.

expected_outcomes: More predictable scaling of resources, sanctions, support, prices, capacity, or attention.

expected_outcomes: Better legitimacy because response magnitude can be explained and compared across cases.

expected_outcomes: Lower waste from applying high-intensity responses to low-magnitude cases.

expected_outcomes: Lower neglect from applying weak responses to high-magnitude cases.

expected_outcomes: Clearer handoff between ordinary proportional scaling and thresholded emergency or nonlinear response.

expected_outcomes: Improved auditability and recalibration of response rules over time.

prose_essence:
Proportional Response Design is the pattern of matching response intensity to the magnitude of the input. The input might be need, severity, demand, use, exposure, load, risk, duration, or harm. The response might be staffing, dose, price, quota, restriction, remedy, scrutiny, support, penalty, or attention.

The central move is to replace arbitrary response with a calibrated relation: more input should normally produce more response, less input should normally produce less response, and the reason for the scaling should be inspectable. The archetype is not simply “be fair” or “use a formula.” It requires an input definition, a response variable, a response function, calibration anchors, bounds, exception rules, and review.

The pattern is useful because it reduces overreaction to small cases and underreaction to serious ones. Its main danger is false precision: a mathematically neat rule can hide weak measurement, contextual inequity, nonlinear risk, or values that deserve open debate.

prose_when_to_use:
Use this archetype when the thing arriving at the system varies by degree and the response should vary by degree too. Typical triggers include variable workload, variable severity, variable use, variable need, variable exposure, or variable risk. The pattern is especially useful when repeated decisions need to be predictable across cases: similar inputs should receive similar responses, and larger inputs should receive justifiably larger responses.

It is a good fit when equal treatment would be structurally unequal. Giving every learner the same support can neglect large skill gaps. Giving every incident the same escalation can waste resources on low-impact cases and underreact to major ones. Charging every user the same price can subsidize heavy use and penalize light use. Applying the same penalty to minor and severe cases can be both wasteful and illegitimate.

Do not use this archetype when the response should change regime at a threshold. If a system approaches collapse, emergency, saturation, tipping, or irreversible harm, `nonlinear_threshold_response` is probably closer. Do not use it when the input cannot be measured or ranked without creating false precision. Do not use it when a baseline obligation, dignity standard, safety rule, or rights constraint requires the same minimum treatment regardless of magnitude.

prose_structural_problem:
The structural problem is a mismatch between graded inputs and uncalibrated responses. The system sees cases of different size, seriousness, demand, or need, but it responds through blunt uniformity, improvised discretion, symbolic escalation, or arbitrary tables.

This mismatch shows up in two opposite ways. In one direction, the system overreacts: a small mistake receives a severe sanction, a small load increase receives a large process change, or a minor support request consumes scarce expert attention. In the other direction, the system underreacts: major incidents are handled like routine issues, severe needs receive generic support, or large users consume far more capacity than the rule accounts for.

A second problem is legitimacy. When there is no visible scaling rule, people cannot tell whether a response is fair, biased, accidental, or politically driven. Operators also cannot learn from experience because each case looks like an exception rather than evidence for recalibration.

prose_intervention_logic:
The intervention begins by naming the input magnitude. What exactly varies? Severity, risk, demand, use, need, workload, exposure, duration, cost, impact, or something else? The input definition should include units or categories, evidence sources, and uncertainty.

Next, name the response variable. What changes as the input changes? Staffing, capacity, dose, scrutiny, price, service level, response time, review depth, penalty, subsidy, or support? This prevents a vague promise of “more response” from hiding the actual design choice.

Then build the response function. It may be a simple ratio, a linear formula, a monotonic table, a weighted score, or a piecewise-linear schedule. The exact mathematics matters less than the design property: similar inputs should map to similar responses, and larger inputs should map to larger responses unless an explicit cap, floor, or exception applies.

Calibration anchors make the rule discussable. The designer chooses reference cases or benchmarks and asks, “What response should this case receive?” Those anchors set the slope and shape of the rule. Bounds prevent absurd extrapolation. Exception rules tell the system when proportional scaling is no longer appropriate because of uncertainty, contextual burden, rights constraints, or nonlinear risk. Finally, review closes the loop by comparing outcomes with the rule’s purpose and recalibrating over time.

prose_key_components:
### Input Magnitude Definition

The input magnitude definition identifies the variable that will drive response intensity. It might be number of users, severity of harm, duration of outage, measured exposure, queue length, assessed skill gap, resource use, or risk level. This component keeps the archetype from becoming vague. If the input is poorly measured or politically chosen, the whole proportional rule inherits that weakness.

### Response Variable

The response variable names what will actually be scaled. It might be dollars, staff hours, dose, inspection frequency, escalation level, support time, quota, restriction, review depth, or service credit. A proportional design can fail when the input is real but the chosen response does not address it.

### Response Function

The response function maps input magnitude to response magnitude. It can be linear, monotonic, ratio-based, banded, weighted, or piecewise. The important thing is that the relation is explicit enough to be explained, applied consistently, and revised.

### Proportionality Rule

The proportionality rule explains why this mapping is appropriate. It states what counts as proportionate and what would count as too weak or too strong. This component links the technical scaling relation to the purpose of the intervention.

### Calibration Anchor Set

Calibration anchors are reference cases used to set the rule. Anchors might be historical baselines, benchmark cases, representative scenarios, expert judgments, or empirical results. They prevent a table or formula from looking objective while actually being arbitrary.

### Upper and Lower Bound

Bounds prevent proportional scaling from producing absurd or harmful extremes. A lower bound may preserve minimum service, safety, or dignity. An upper bound may prevent punitive response, runaway cost, or unsafe dose. Bounds should be visible because they are places where pure proportionality intentionally stops.

### Exception Rule

Exception rules define when the proportional response should be overridden or supplemented. Exceptions may be needed for nonlinear risk, rights constraints, low measurement confidence, unusual context, or outliers. The key is to make exceptions accountable rather than turning every hard case into hidden discretion.

### Review Path

The review path gives the rule a way to learn. It can include appeal, audit, calibration meetings, retrospective comparison, or outcome monitoring. Without review, a proportional rule can ossify after its evidence, costs, or values change.

### Evidence Calibration

Evidence calibration connects response intensity to evidence quality. Stronger or more burdensome responses should generally require stronger measurement or confidence. When evidence is weak, the design may choose reversible response, lower intensity, review, or uncertainty bands.

### Context Adjustment Guard

The context adjustment guard prevents nominal proportionality from becoming substantive unfairness. A fine, delay, dose, workload, or restriction may be equal as a ratio but unequal in practical burden. This component allows relevant context to matter without dissolving the rule into favoritism.

prose_common_mechanisms:
### Proportional Penalty Schedule

A proportional penalty schedule maps severity, recurrence, duration, or harm to sanction or remedy. It implements the archetype only when the schedule is calibrated, bounded, and reviewable. A list of punishments is not enough.

### Usage-Based Pricing Table

Usage-based pricing maps consumption or service use to cost. It is a mechanism under this archetype when the price response is tied to measured use and bounded by transparency, caps, warnings, or affordability considerations.

### Dose Scaling Protocol

Dose scaling adjusts treatment, training, support, or intervention intensity by measured size, need, tolerance, or response. It must be bounded by safety and review, because dose is often constrained by therapeutic windows or overload limits.

### Load-Scaled Staffing Rule

A load-scaled staffing rule links staffing or coverage to demand. It works when the load measure is valid and the response remains in a proportional operating range. If queues or failures become nonlinear, the system should hand off to threshold response.

### Resource Allocation Formula

Resource allocation formulas distribute budget, time, capacity, or attention by weighted need, demand, population, or use. They are mechanisms, not the archetype itself. The archetype is the calibrated response architecture behind the formula.

### Risk-Based Response Matrix

A risk-based matrix scales scrutiny, controls, inspection, or intervention by estimated likelihood, impact, confidence, and exposure. It needs special care because estimated risk can encode bias, uncertainty, and model drift.

### Sliding-Scale Fee Policy

Sliding-scale fees scale cost or contribution by income, need, or ability to pay. They illustrate that proportionality can mean proportional practical burden, not merely proportional measured use.

### Graduated Enforcement Ladder

A graduated enforcement ladder uses increasing warning, correction, restriction, or sanction levels. It fits this archetype when the ladder is scaled by seriousness and recurrence. It fits `nonlinear_threshold_response` when steps represent regime changes or emergency authority.

### Service Credit Formula

A service credit formula calculates compensation or remedy by outage duration, missed service level, affected scope, or contractual impact. It creates a predictable response instead of ad hoc refund decisions.

### Calibration Review Meeting

A calibration review meeting compares recent cases with anchors and outcomes. It is a mechanism for keeping the rule alive: adjusting slope, weights, bands, exceptions, and bounds when reality changes.

prose_failure_modes:
Proportional response design fails through false precision when a table or formula makes weak measurement look exact. It fails through metric capture when people learn to optimize the input measure rather than the underlying purpose. It fails through rigid linearity when smooth scaling continues into a region where the system needs thresholded or emergency response.

It also fails through hidden cliffs. A rule may call itself proportional while using bands, caps, or floors that create abrupt jumps. Some discontinuities are necessary, but they should be named and justified.

Another failure mode is substantive disproportionality. A mathematically equal penalty or workload can impose unequal burden. This is common when the response affects money, time, access, medicine, discipline, surveillance, or essential services.

Finally, the rule can ossify. Calibration anchors that were reasonable at launch may become stale as costs, risks, behavior, technology, or values change. Review cadence and recalibration triggers are therefore part of the archetype.

prose_neighbor_distinctions:
The most important neighbor is `nonlinear_threshold_response`. Proportional response scales by degree; nonlinear threshold response changes regime. If a system crosses an emergency boundary, saturation point, tipping point, or hard constraint, proportionality may underreact.

`therapeutic_window_management` is another neighbor. It focuses on staying within safe and effective ranges. Proportional response may scale a dose, but safe-window constraints decide where scaling must stop or reverse.

`resource_rationing` allocates scarce resources under shortage. Proportional response can distribute resources by need or use, but rationing has a different center: scarcity, priority, exclusion, and triage under hard limits.

`proportionality_calibration` is a global governance/fairness neighbor. It appears to focus on scaling sanctions, restrictions, remedies, or obligations to severity and necessity. This draft preserves that boundary rather than absorbing the future governance-specific archetype.

`procedural_fairness_design` concerns the legitimacy of the decision process. A proportional rule may need procedural safeguards, but process fairness and response magnitude are not the same pattern.

prose_cross_domain_examples:
In operations, a support organization can scale staffing and escalation depth with ticket volume and severity mix. The proportional rule reduces both overload and idle capacity, while nonlinear surge criteria handle exceptional backlog.

In software services, usage-based pricing or quotas can scale with compute, storage, bandwidth, or API calls. Caps, warnings, and review prevent surprise harm.

In medicine or training, dose can scale with body size, severity, tolerance, or demonstrated response. Safety windows and contraindications keep the rule from becoming naïvely linear.

In education, practice volume and tutoring can scale with assessed skill gaps. Review prevents the proportional rule from becoming stigma, fatigue, or endless remediation.

In compliance, inspection frequency and corrective action can scale with risk and violation severity. Appeal and calibration review protect against opaque scoring and inconsistent enforcement.

In service contracts, remedy or credit can scale with outage duration and affected scope. The response becomes predictable instead of improvised after each incident.

prose_non_examples:
A hard emergency shutdown after a single threshold is crossed is not proportional response design; it is threshold or circuit-breaker logic. A flat service guarantee given equally to everyone is not this archetype, though proportional additions may be layered on top. A manager’s intuitive case-by-case judgment is not this archetype unless it is turned into anchors, rules, and review.

A risk dashboard alone is not proportional response design. The dashboard becomes relevant only when response intensity is calibrated to the risk signal and bounded by evidence quality. A revenue-maximizing price increase is also not automatically proportional response; the response must be justified by a stated magnitude relation and bounded by the purpose of the rule.

prose_tradeoffs:
The major tradeoff is predictability versus nuance. A clear response function reduces arbitrary discretion, but it can miss facts that matter. Another tradeoff is transparency versus gaming. People can understand visible rules, but they can also optimize around them.

There is also a tradeoff between mathematical neatness and practical fairness. Equal ratios are not always equal burdens. A proportional fee, penalty, or workload can affect people differently depending on capacity, income, vulnerability, or baseline constraints.

Finally, there is a tradeoff between ordinary proportional scaling and exceptional response. If exceptions are too rare, the rule becomes rigid. If exceptions are too common, the rule loses coherence. A strong design makes exception criteria explicit and audits their use.

prose_invariants_to_preserve:
The first invariant is comparability: similar cases should receive similar responses unless an explicit exception applies. The second is monotonicity: larger inputs should generally receive larger responses within the intended range. The third is boundedness: the response should not extrapolate into absurd, unsafe, or unjust extremes.

The fourth invariant is measurement integrity. The input should continue to represent the thing the rule claims to scale against. The fifth is reviewability. People should be able to ask why the response was this large, what evidence drove it, and how the rule can be corrected.

Finally, the design must preserve substantive proportionality, not merely mathematical proportionality. A rule can be formally equal while imposing radically unequal burdens. That is why context adjustment and review are structural components, not afterthoughts.

prose_target_outcomes:
A successful proportional response design produces predictable and explainable action. Operators know how much response is appropriate for a given magnitude. Affected parties can understand the basis of the response. Resources are less likely to be wasted on small cases and less likely to be withheld from serious ones.

The archetype also improves learning. When the rule is explicit, outcomes can be compared with expectations. If the slope is too steep, too shallow, too costly, too punitive, or too easy to game, the system can recalibrate instead of merely blaming individual judgment.

prose_parameter_dimensions:
Important tuning dimensions include the input granularity, response slope, response floor, response ceiling, rule shape, and review cadence. A fine-grained rule can be more accurate but harder to use. A coarse banded rule is easier to operate but may create cliff effects. A steep slope responds strongly to magnitude differences but increases the risk of overreaction. A shallow slope reduces volatility but can underreact to serious cases.

The designer also tunes evidence requirements. Some domains can tolerate approximate scaling; others require high confidence before imposing burden. Measurement confidence bands, appeal triggers, and reversible responses help handle uncertainty.

Another tuning dimension is the handoff boundary. Proportional response works inside a range. At the edges, the system may need minimum guarantees, maximum caps, nonlinear thresholds, emergency escalation, or therapeutic-window constraints. Good proportional design names those boundaries rather than pretending that one formula applies everywhere.

prose_variants_and_near_names:
`severity_scaled_response` applies the pattern to sanctions, scrutiny, remedy, restriction, or support scaled by seriousness. It is especially relevant in governance-like domains because legitimacy and appeal matter.

`usage_based_scaling` applies the pattern to price, quota, capacity, or cost allocation based on measured use or load. It often appears as usage-based pricing, chargeback, capacity rules, or service limits.

`dose_scaled_response` applies the pattern to treatment, training, support, or intervention dose. It needs safety bounds and review because too much response can be harmful, not merely wasteful.

`load_scaled_capacity_response` applies the pattern to staffing, throughput, coverage, or capacity. It should be monitored for nonlinear queueing or overload behavior.

`risk_weighted_response` is a candidate variant for cases where estimated risk drives response intensity. It is useful but sensitive because risk estimates can be biased, uncertain, or overtrusted.

Near names include `linear_response_design`, `scaled_response_rule`, `response_function_design`, `calibrated_scaling_rule`, and `graduated_response`. Treat these as retrieval names unless they develop distinct component sets and failure modes.

Severity-Scaled Response

short_definition: Scale sanction, remedy, scrutiny, or support to the assessed severity of a case while preserving review for context and error.

distinctive_feature: The input variable is severity or seriousness, and the response often affects rights, obligations, penalties, remedies, or scrutiny.

distinct_from_parent: The parent is the general pattern of calibrated scaling; this variant emphasizes severity, legitimacy, and contextual review in governance-like settings.

still_same_parent_because: It still uses a response function, proportionality rule, bounds, exceptions, and review path rather than a distinct intervention architecture.

use_when: Cases differ by severity, exposure, harm, urgency, or culpability and equal treatment would overreact to minor cases or underreact to serious ones.

use_when: The decision setting requires legitimacy, explainability, and a visible path for appeal or correction.

use_when: A response must remain bounded by rights, minimum standards, or ethical limits even while scaling with severity.

Input Magnitude Definition

role: Component reference used by the Severity-Scaled Response variant to preserve proportional scaling, bounds, review, or calibration.

Proportional Penalty Schedule

role: Mechanism reference that can implement the Severity-Scaled Response variant when combined with input definition, calibration, bounds, exceptions, and review.

domain: compliance

example: Inspection frequency and corrective action scale with demonstrated risk and violation severity.

example: Warnings, temporary restrictions, and suspensions scale with verified harm and recurrence rather than with public outrage alone.

failure_modes: Severity metric becomes politicized or opaque.

failure_modes: Nominally proportional penalties impose unequal real burdens.

failure_modes: Exception handling swallows the rule or becomes arbitrary.

Usage-Based Scaling

short_definition: Scale price, quota, service level, capacity, or contribution by measured use or load.

distinctive_feature: The input variable is consumption, load, demand, or service use rather than severity or risk.

distinct_from_parent: This variant specializes the parent for measured use and often appears as pricing, quota, capacity, or cost-allocation policy.

still_same_parent_because: The logic remains calibrated scaling from input magnitude to response magnitude with bounds and exceptions.

use_when: Use, demand, or consumption can be measured reliably enough to support a scaling rule.

use_when: Equal charges, equal quotas, or equal resource shares would create underuse, overuse, unfair burden, or capacity mismatch.

use_when: The scaling rule needs predictable incentives without turning into punitive surprise.

Input Magnitude Definition

role: Component reference used by the Usage-Based Scaling variant to preserve proportional scaling, bounds, review, or calibration.

Usage Based Pricing Table

role: Mechanism reference that can implement the Usage-Based Scaling variant when combined with input definition, calibration, bounds, exceptions, and review.

example: Charges or quotas scale with compute, storage, or network usage while caps prevent runaway bills.

domain: operations

example: Support staffing scales with ticket volume rather than with a fixed headcount assumption.

failure_modes: Measurement overhead dominates the value of scaling.

Dose-Scaled Response

short_definition: Adjust treatment, support, training, or intervention dose according to measured size, need, exposure, or tolerance.

distinctive_feature: The response variable is an intensity or dose that accumulates effects over time.

distinct_from_parent: This variant stresses safety, tolerance, accumulation, and adjustment over repeated application.

still_same_parent_because: It still depends on a response function that maps measured need or exposure to response intensity.

source_primes: proportionality scale constraint

use_when: The response is a dose, dosage, intensity, practice volume, or support level that can be scaled gradually.

use_when: Too little response is ineffective and too much response creates overload, harm, waste, or resentment.

use_when: There are known constraints, floors, ceilings, or review thresholds that bound safe scaling.

Response Variable

role: Component reference used by the Dose-Scaled Response variant to preserve proportional scaling, bounds, review, or calibration.

Dose Scaling Protocol

role: Mechanism reference that can implement the Dose-Scaled Response variant when combined with input definition, calibration, bounds, exceptions, and review.

domain: education

example: Practice volume scales with demonstrated skill gap while fatigue and frustration are monitored.

example: Medication or therapy intensity is adjusted by weight, renal function, tolerance, or measured response.

failure_modes: Safe-window constraints are mistaken for simple proportionality.

failure_modes: Contextual contraindications are ignored.

failure_modes: Dose is adjusted from noisy measurements without review.

Load-Scaled Capacity Response

short_definition: Scale staffing, capacity, throughput, inspection, or support level according to measured load.

distinctive_feature: The response variable is capacity provision, often in operations or infrastructure.

distinct_from_parent: The variant specializes proportional response for capacity management and load matching.

still_same_parent_because: It still uses calibrated mapping between input load and response capacity with bounds and exception rules.

use_when: Demand or load changes predictably enough that capacity can be scaled without constant improvisation.

use_when: Fixed response capacity causes waste at low load or overload at high load.

use_when: The goal is smooth scaling rather than a thresholded surge regime.

Input Magnitude Definition

role: Component reference used by the Load-Scaled Capacity Response variant to preserve proportional scaling, bounds, review, or calibration.

Load Scaled Staffing Rule

role: Mechanism reference that can implement the Load-Scaled Capacity Response variant when combined with input definition, calibration, bounds, exceptions, and review.

example: Staffing bands scale with ticket volume and severity mix before backlogs become unstable.

example: Autoscaling rules add capacity proportional to sustained load while guardrails catch nonlinear failure regions.

failure_modes: Linear scaling is applied in a region where queues or failures become nonlinear.

failure_modes: Capacity is scaled by the wrong load measure.

failure_modes: Staffing granularity makes the response lumpy despite a smooth rule.

Risk-Weighted Response

short_definition: Scale scrutiny, controls, reserves, or support according to measured risk while preserving false-positive and false-negative safeguards.

distinctive_feature: The input variable is estimated risk, not directly observed load, use, or severity.

distinct_from_parent: This variant adds uncertainty, model calibration, error tradeoffs, and review pressure to the general scaling pattern.

still_same_parent_because: The intervention still maps input magnitude to response intensity with bounds and exceptions.

use_when: Risk varies by observable indicators and uniform response would waste attention or miss high-risk cases.

use_when: The model or rule can be reviewed for error, bias, confidence, and drift.

use_when: The response should scale with risk but not become an unaccountable risk score dictatorship.

Evidence Calibration

role: Component reference used by the Risk-Weighted Response variant to preserve proportional scaling, bounds, review, or calibration.

Risk Based Response Matrix

role: Mechanism reference that can implement the Risk-Weighted Response variant when combined with input definition, calibration, bounds, exceptions, and review.

domain: cybersecurity

example: Alert triage, review depth, and containment urgency scale with risk score confidence and blast-radius estimates.

domain: inspection

example: Inspection frequency scales with risk indicators while random audits test model drift.

failure_modes: Risk score bias is laundered into a seemingly neutral proportional rule.

failure_modes: Uncertainty is hidden rather than exposed.

failure_modes: Risk estimates cross into nonlinear regimes without switching archetypes.

near_names:
Linear Response Design
Scaled Response Rule
Calibrated Scaling Rule
Response Function Design
Graduated Response
Proportionality Calibration
Usage-Based Pricing
Dose Scaling


Proportionality Calibration

type: archetype

source: generated

batch: First Iteration/Batch 48

notes: Emphasizes designing a response that fits severity, necessity, burden, and context.

Response Fit Calibration

notes: Plain-language near name for matching intervention strength to case conditions.

Scaled Response Design

notes: Emphasizes explicit response levels and escalation thresholds.

Least-Burdensome Effective Response

notes: Important component and variant phrase; not the full parent archetype by itself.

Sanction Fit Design

notes: A sanction-focused domain name under the broader response-fit pattern.

related_primes: procedural_fairness equity accountability adjudication constraint balance threshold cost_benefit_analysis uncertainty probability margin_of_safety

proposed_primes: fairness risk

slug: equity

slug: accountability

slug: adjudication

slug: balance

slug: uncertainty

slug: probability

components:
Severity Assessment
Necessity Test
Burden Assessment
Affected Interest Map
Least-Burdensome Effective Response
Response Scale
Escalation Threshold
Proportionality Review
Context Adjustment Factor
De-escalation Path
Equity Cross-Check
Precedent Comparator

mechanisms:
Graduated Sanctions
Response Matrix
Least-Restrictive Alternative Screen
Risk-Based Enforcement Protocol
Proportional Remedy Menu
Moderation Strike System
Disciplinary Guideline
Sanction Matrix
After-Action Proportionality Review

essence_one_line: Scale response, restriction, remedy, or sanction to the severity, necessity, risk context, and affected interests of the case.

compression_statement: When a system may overreact, underreact, or apply a mismatched intervention, proportionality calibration matches the response to severity, necessary purpose, burden, context, and reviewable outcome.

core_intuition: A response is not fair merely because it follows a rule, and it is not effective merely because it is strong. The response has to fit: serious enough to address the problem, limited enough to avoid unnecessary burden, and revisable when conditions change.

canonical_formula: severity_assessment + necessity_test + affected_interest_map + burden_assessment + response_scale + least_burdensome_effective_response + escalation_threshold + proportionality_review -> calibrated response fit

structural_problem: A governance or decision system applies responses that are too severe, too weak, too automatic, too symbolic, or poorly matched to the harm, risk, need, or context of the case.

condition: Response strength varies arbitrarily

explanation: Similar cases receive very different burdens or sanctions because no response scale, severity assessment, or review standard exists.

condition: A strong intervention creates collateral burden

explanation: A restriction, sanction, shutdown, exclusion, or remedial demand may solve one problem while imposing unnecessary harm on affected parties or bystanders.

condition: A weak response fails to address real severity

explanation: The system uses warnings, symbolic repair, delays, or education when the case requires containment, remedy, accountability, or escalation.

condition: Decision-makers face pressure to overreact

explanation: Public anxiety, reputational risk, moral outrage, rare catastrophic cases, or blame avoidance can push responses beyond what the case requires.

condition: Decision-makers face pressure to underreact

explanation: Convenience, cost avoidance, status protection, uncertainty, or normalization of harm can keep the response below the level needed.

condition: Context matters but discretion is risky

explanation: Intent, capacity, recurrence, vulnerability, urgency, and systemic impact are relevant, but unstructured discretion can become bias, favoritism, or inconsistency.

symptom: One-size-fits-all punishment or restriction

diagnostic_meaning: The response does not distinguish low-severity, high-severity, first-time, repeated, accidental, intentional, reversible, and irreversible cases.

symptom: Escalation without release

diagnostic_meaning: The system can move to harsher responses but has no clear way to step down once severity, risk, or noncompliance falls.

symptom: Symbolic accountability without repair

diagnostic_meaning: The response looks serious but does not restore access, correct the problem, compensate harm, or reduce recurrence.

symptom: Disproportionate collateral impact

diagnostic_meaning: The selected response burdens people or functions beyond what is necessary for the objective.

symptom: Mechanical sanction tables

diagnostic_meaning: A matrix or guideline replaces judgment, context, and review instead of supporting them.

symptom: Unequal application disguised as context

diagnostic_meaning: Decision-makers invoke context selectively to justify harsher or softer treatment without a recorded basis.

root_tension: Governance needs responses strong enough to address harm, risk, or noncompliance, but unchecked response strength can itself become unfair, costly, legitimacy-damaging, or harmful.

case: Pure priority ranking

why_not: If the task is only to rank work by value or urgency without selecting a burdening response, prioritization or triage patterns are closer.

case: Pure resource optimization

why_not: If the central problem is efficient allocation under constraints rather than matching response burden to severity, optimization or cost-benefit patterns may be primary.

case: Pure adjudication

why_not: If the main work is resolving a dispute through evidence and a decision rule, adjudication_process_design is primary; proportionality may guide remedy selection.

case: Pure equity adjustment

why_not: If the decisive intervention is adapting rules or supports to different starting conditions, equity_adjustment is primary; proportionality may check burden fit.

case: Artifact-only sanction matrix

why_not: A table of sanctions, strikes, or penalties is a mechanism. It becomes this archetype only when embedded in severity, necessity, burden, threshold, and review logic.

structural_intervention: Create a reviewable response-fit system that assesses severity and necessity, maps affected interests and burdens, chooses the least-burdensome effective response, defines escalation thresholds, and reviews outcomes for over- or under-response.

step: Define the response object

logic: Clarify what kind of response is being calibrated: restriction, remedy, sanction, containment, support, monitoring, escalation, or no action.

step: Assess severity and purpose

logic: Determine the scale, reversibility, recurrence, likelihood, urgency, and legitimate objective that make any response necessary.

step: Map affected interests

logic: Identify who is protected, burdened, repaired, deterred, excluded, watched, delayed, or otherwise affected.

step: Compare response options

logic: Place available actions on a scale and evaluate their likely effectiveness, burden, collateral effects, and reversibility.

step: Select the least-burdensome effective response

logic: Choose the response that plausibly achieves the necessary objective without unnecessary restriction, punishment, cost, or spillover.

step: Set escalation and de-escalation thresholds

logic: Define when stronger action, weaker action, restoration, or closure is justified by changed severity, recurrence, cooperation, or outcome evidence.

step: Review and adjust

logic: Check whether the response was too strong, too weak, unevenly applied, or no longer necessary; revise the scale or case decision when needed.

rule: No response without a necessary objective

meaning: A response should not be imposed merely for optics, anger, habit, or symbolic control.

rule: No stronger response when a weaker effective one suffices

meaning: If a less burdensome action would achieve the objective, the stronger action needs a specific justification.

rule: Severity changes response range, not just response label

meaning: Higher severity, recurrence, irreversibility, urgency, or exposure can justify stronger response, but only inside a documented scale.

rule: Context can adjust but not erase standards

meaning: Intent, capacity, barriers, vulnerability, and past conduct may matter, but the reason must be recorded and reviewable.

rule: Escalation requires a threshold and release requires a path

meaning: Systems should define both movement upward and movement back down when the case no longer justifies strong intervention.

invariant: Fit between severity and response

explanation: The response should be neither excessive nor insufficient relative to the case.

invariant: Necessity of burden

explanation: Every meaningful burden should serve a legitimate purpose that cannot be achieved as well by a less burdensome option.

invariant: Reviewability

explanation: A reviewer should be able to reconstruct why this response level was chosen.

invariant: Context with consistency

explanation: Relevant context may affect calibration, but similar cases should not diverge arbitrarily.

invariant: De-escalation possibility

explanation: Responses should be reduced, ended, or converted when severity, risk, or need falls.

invariant: No artifact substitution

explanation: Matrices, guidelines, and policies should support judgment rather than replace proportionality reasoning.

outcome: Reduced overreach

description: Fewer cases receive restrictions, sanctions, or controls stronger than necessary.

outcome: Reduced under-response

description: Serious or recurring cases receive enough intervention to protect affected interests and prevent recurrence.

outcome: Improved legitimacy

description: Affected parties can see why a response level was selected and how it can be reviewed or adjusted.

outcome: More consistent discretion

description: Decision-makers can use context without turning judgment into arbitrary divergence.

outcome: Better learning from responses

description: After-action review reveals where the scale, thresholds, or burden assumptions should change.

prose_essence:
Proportionality calibration is the discipline of making a response fit the case. It asks whether a restriction, remedy, sanction, containment step, support package, or escalation is strong enough to address the problem but no stronger than necessary. The central move is not simply to be lenient or strict; it is to make the selected response reviewably appropriate to severity, purpose, burden, context, and outcome.

The archetype is especially useful in governance systems where both overreaction and underreaction are harmful. A platform can silence too much or tolerate abuse too long. A school can punish a minor mistake too severely or ignore a serious pattern. A safety team can shut down a whole operation unnecessarily or leave a serious hazard unmanaged. Proportionality calibration gives these systems a structured way to choose the response level rather than defaulting to panic, habit, optics, or administrative convenience.

prose_when_to_use:
Use this archetype when a decision imposes a meaningful burden or remedy and the appropriate response depends on the case. Typical triggers include enforcement, discipline, moderation, safety intervention, benefits administration, model governance, access restriction, and corrective action. It is particularly useful when cases vary by harm, recurrence, uncertainty, intent, vulnerability, reversibility, or public impact.

Do not use it as a substitute for adjudication when the facts are unresolved, or as a substitute for equity adjustment when the core issue is unequal starting conditions. It can support those archetypes, but its own center is response fit.

prose_structural_problem:
The structural problem is a mismatch between case conditions and response strength. In one direction, the system overreacts: a minor or uncertain case receives a severe restriction, public sanction, shutdown, or exclusion. In the other direction, the system underreacts: a serious, recurring, or high-exposure problem receives a symbolic warning, delayed process, or inadequate repair.

The mismatch often arises because response tools are easier to name than response criteria. A policy may contain sanctions, a workflow may contain escalation steps, or a dashboard may contain risk categories, yet no one has defined how severity, necessity, burden, affected interests, and context should be weighed. The result is either automatic punishment, improvisational discretion, or a table that looks objective while hiding the real judgment.

prose_intervention_logic:
The intervention starts by defining what kind of response is being selected. A warning, remedy, content removal, access limit, investigation, shutdown, apology, restitution, retraining, or suspension all impose different burdens and serve different purposes.

Next, the system assesses severity and necessity. What harm or risk is being addressed? Is the objective prevention, repair, containment, deterrence, learning, restoration, or accountability? Without a legitimate objective, a response can become theater. Without a severity assessment, a response scale has no anchor.

Then the system maps affected interests and burdens. A proportional response must consider not only the person or unit receiving the response, but also protected parties, bystanders, implementers, downstream users, and institutional trust. The least-burdensome effective response is then selected from an explicit scale. Escalation thresholds define when stronger action is justified, and de-escalation or review prevents stronger responses from persisting after they are no longer needed.

prose_key_components:
### Severity Assessment

**Role:** Determines the scale of the triggering harm, violation, risk, need, or problem before any response is selected.

Severity should combine magnitude, reversibility, affected-party impact, likelihood where relevant, and recurrence. It prevents both reflexive escalation and casual underreaction.

### Necessity Test

**Role:** Asks whether a response is needed at all and, if so, what purpose the response must serve.

A proportional response must be connected to a legitimate objective: prevention, repair, learning, containment, deterrence, restoration, or risk reduction. If no necessary purpose exists, the response should not proceed.

### Burden Assessment

**Role:** Estimates the costs, restrictions, disruption, stigma, delay, surveillance, or opportunity loss imposed by each possible response.

The burden side of proportionality is often invisible. This component forces designers to count impact on affected parties, implementers, bystanders, and trust in the system.

### Affected Interest Map

**Role:** Identifies whose interests, rights, safety, access, autonomy, resources, or welfare are changed by the response.

A response can be proportionate for one party and excessive for another. Mapping interests keeps the calibration from focusing only on the target of enforcement.

### Least-Burdensome Effective Response

**Role:** Selects the minimum response that can plausibly achieve the necessary objective without imposing unnecessary cost or restriction.

This does not mean choosing the weakest response. It means choosing the least intrusive response that still works, with escalation reserved for insufficient or riskier cases.

### Response Scale

**Role:** Defines the available response levels, from no action or warning through support, remedy, restriction, sanction, escalation, or containment.

Without a scale, proportionality becomes ad hoc. The scale should include de-escalation and restoration options, not only harsher steps.

### Escalation Threshold

**Role:** Specifies when the system should move to a stronger response because severity, recurrence, urgency, noncompliance, or spillover risk has increased.

Thresholds make proportionality auditable. They also protect against escalation drift by requiring reasons when the response jumps to a higher level.

### Proportionality Review

**Role:** Checks whether the selected response remained matched to severity, necessity, burden, and outcome after implementation.

Review catches over-response, under-response, unequal application, and changed conditions. It turns proportionality from a one-time judgment into a feedback process.

prose_common_mechanisms:
Mechanisms are implementation forms. They can make proportionality easier to apply, but they are not the archetype unless they preserve the full calibration loop: severity, necessity, affected interests, burden, response selection, threshold, and review.

### Graduated Sanctions

This is a **procedure** mechanism. It implements the archetype by helping decision-makers calibrate response fit: Implements proportionality by sequencing responses from warning or coaching through stronger sanctions as severity or recurrence increases. It should not be confused with the archetype itself; it is useful only when it preserves severity, necessity, burden, threshold, and review logic.

### Response Matrix

This is a **decision_table** mechanism. It implements the archetype by helping decision-makers calibrate response fit: Maps severity, recurrence, intent, impact, and context to response ranges so decision-makers can see the proportional response band. It should not be confused with the archetype itself; it is useful only when it preserves severity, necessity, burden, threshold, and review logic.

### Least-Restrictive Alternative Screen

This is a **review_screen** mechanism. It implements the archetype by helping decision-makers calibrate response fit: Requires reviewers to consider whether a less restrictive but still effective response would achieve the same purpose. It should not be confused with the archetype itself; it is useful only when it preserves severity, necessity, burden, threshold, and review logic.

### Risk-Based Enforcement Protocol

This is a **protocol** mechanism. It implements the archetype by helping decision-makers calibrate response fit: Allocates enforcement intensity according to severity, likelihood, exposure, and urgency while preserving checks against bias and overreach. It should not be confused with the archetype itself; it is useful only when it preserves severity, necessity, burden, threshold, and review logic.

### Proportional Remedy Menu

This is a **remedy_catalog** mechanism. It implements the archetype by helping decision-makers calibrate response fit: Offers repair options calibrated to harm type and scale, such as apology, correction, restitution, access restoration, remediation, or redesign. It should not be confused with the archetype itself; it is useful only when it preserves severity, necessity, burden, threshold, and review logic.

### Moderation Strike System

This is a **domain_workflow** mechanism. It implements the archetype by helping decision-makers calibrate response fit: Implements scaled platform responses for repeated or severe violations, provided it preserves context, appeal, and de-escalation. It should not be confused with the archetype itself; it is useful only when it preserves severity, necessity, burden, threshold, and review logic.

### Disciplinary Guideline

This is a **policy_guidance** mechanism. It implements the archetype by helping decision-makers calibrate response fit: Provides recommended response ranges for workplace, school, professional, or community discipline without replacing judgment and review. It should not be confused with the archetype itself; it is useful only when it preserves severity, necessity, burden, threshold, and review logic.

### Sanction Matrix

This is a **decision_table** mechanism. It implements the archetype by helping decision-makers calibrate response fit: Lists sanction levels by conduct category or severity. It is a mechanism under this archetype, not the archetype itself. It should not be confused with the archetype itself; it is useful only when it preserves severity, necessity, burden, threshold, and review logic.

### After-Action Proportionality Review

This is a **review_process** mechanism. It implements the archetype by helping decision-makers calibrate response fit: Reviews whether the response produced intended safety, fairness, deterrence, repair, or learning without excessive collateral burden. It should not be confused with the archetype itself; it is useful only when it preserves severity, necessity, burden, threshold, and review logic.

prose_failure_modes:
**Over-response** occurs when the system exaggerates severity, reacts to public pressure, or defaults to the strongest tool. Mitigate it with necessity tests, least-burdensome alternatives, burden assessment, and after-action review.

**Under-response** occurs when the system minimizes harm, avoids cost, protects powerful actors, or waits for perfect evidence despite serious risk. Mitigate it with severity rubrics, affected-interest mapping, recurrence tracking, and independent review of serious cases.

**Mechanical proportionality theater** occurs when a sanction matrix or score appears objective but simply automates hidden assumptions. Mitigate it by treating tools as decision support, not decision replacement.

**Escalation drift** occurs when the system can move upward but cannot step down. Mitigate it with sunset rules, de-escalation thresholds, restoration criteria, and closure review.

**Context as favoritism** occurs when discretionary factors are invoked selectively. Mitigate it by defining legitimate context factors, documenting evidence, comparing similar cases, and auditing distribution of response levels.

prose_neighbor_distinctions:
Proportionality calibration is close to procedural fairness, but it is not the same. Procedural fairness asks whether affected parties receive notice, voice, impartiality, reasons, and review. Proportionality asks whether the selected response level fits the case.

It is close to equity adjustment, but equity adjustment focuses on different starting conditions, barriers, and supports. Proportionality focuses on the relationship between response burden and severity or necessity. The two can combine when a formally equal response imposes unequal burden.

It is close to adjudication, but adjudication resolves disputes by applying standards to evidence. Proportionality often enters after that decision, when selecting remedy, sanction, or restriction.

It is close to rights/freedoms obligation mapping because limits on claims or liberties often need proportionality review. The mapping clarifies the interests; proportionality calibrates the response or limit.

It is also adjacent to error tradeoff calibration and risk-based response scaling. Error tradeoff calibration focuses on false positives and false negatives; proportionality focuses on response fit. Risk-based variants may deserve future review if their probabilistic structure becomes distinct enough.

prose_cross_domain_examples:
In **platform moderation**, proportionality calibration distinguishes education, label, removal, temporary limit, suspension, and permanent removal by harm, recurrence, intent, audience, and appeal outcome.

In **workplace discipline**, it distinguishes coaching, retraining, warning, reassignment, suspension, and termination by severity, recurrence, safety relevance, and repair possibility.

In **safety operations**, it distinguishes monitoring, partial containment, full shutdown, redesign, and restart review by severity, uncertainty, exposure, and reversibility.

In **public services**, it distinguishes clarification requests, temporary holds, fraud review, restoration, or sanctions by evidence strength and burden on legitimate users.

In **AI governance**, it distinguishes monitoring, guardrail updates, human-review gating, rollback, throttling, and decommissioning according to harm severity, user exposure, and reversibility.

prose_non_examples:
A penalty table copied into a policy manual is not proportionality calibration by itself. It becomes relevant only when severity, necessity, burden, context, and review are actually used.

Maximum enforcement in every case is not proportionality calibration, even if it deters some misconduct. It ignores burden and case fit.

A pure risk score that automatically denies access is not proportionality calibration. It may supply one input, but the response still needs necessity, burden, alternative, and review analysis.

A restorative response in every case is also not proportionality calibration. Restoration is valuable, but some cases require containment, restriction, escalation, or stronger remedy.

prose_tradeoffs:
Proportionality always trades predictability against context. A clear matrix supports consistency, but can become mechanical. Open discretion supports nuance, but can become arbitrary. It also trades speed against deliberation: urgent cases may need immediate temporary action, while durable sanctions or restrictions deserve more review.

There is also a tradeoff between deterrence and restoration. Stronger sanctions may communicate seriousness, but they may fail to repair harm or encourage reintegration. Conversely, restorative responses may be appropriate for some cases but inadequate for severe, repeated, or urgent harm.

prose_invariants_to_preserve: The first invariant is fit: the response should be neither excessive nor insufficient for the case. The second invariant is necessity: each burden should serve a legitimate objective. The third is reviewability: someone should be able to reconstruct why this response was selected. The fourth is consistency-with-context: similar cases should not diverge arbitrarily, but materially different cases should not be forced into identical treatment. The fifth is reversibility or release where possible: proportional systems should know how to step down as well as escalate.

prose_target_outcomes: A successful proportionality system reduces overreach without normalizing inaction. It creates more credible and consistent discretion. Affected parties can understand why the response was selected, what would justify escalation or de-escalation, and how review can correct mismatch. The organization learns where its response scale is too blunt, where burden assumptions are wrong, and where certain groups or cases are being treated unevenly.

prose_parameter_dimensions:
The main tuning dimension is the **severity scale**: how finely the system distinguishes low, moderate, high, urgent, repeated, reversible, and irreversible cases. A scale that is too coarse creates over- and under-response; a scale that is too detailed can create false precision.

A second dimension is **burden tolerance**. Some domains can tolerate mild delay or review, while others must minimize restriction, stigma, surveillance, or access loss. Burden tolerance should be explicit rather than smuggled into vague phrases like “appropriate action.”

A third dimension is **context sensitivity**. Intent, capacity, recurrence, vulnerability, cooperation, and systemic impact may matter. The tuning question is which context factors are legitimate and what evidence is required to use them.

A fourth dimension is **escalation speed**. Some risks require immediate containment, while other cases should move through education, support, or warning before stronger action. The matching de-escalation dimension is equally important: when should the system step down, restore access, or close the case?

Finally, tune **review granularity**. Low-burden cases may need lightweight review; high-burden, irreversible, or contested cases need stronger explanation, appeal, sampling, or after-action audit.

prose_variants_and_near_names:
The strongest recognized variant is **Graduated Response Calibration**, where response levels unfold over repeated or worsening cases. This includes graduated sanctions, strike systems, and staged compliance responses, but only when escalation thresholds and de-escalation criteria remain visible.

**Least-Burdensome Restriction Selection** focuses on choosing the least intrusive effective response when several options could achieve the objective. It is a variant because the decisive move is alternative comparison by burden and effectiveness.

**Proportional Remedy Calibration** focuses on repair rather than punishment. It asks what apology, correction, restitution, restoration, redesign, or compensation matches the harm.

**Risk-Based Response Scaling** is held as a merge-review variant. It may belong here when preventive response is calibrated to severity, likelihood, exposure, and uncertainty, but it may also belong to a broader risk-governance family.

Near names include proportional response design, response fit calibration, scaled response design, minimum sufficient response, sanction fit design, and graduated sanctioning. Artifact names such as sanction matrix, response matrix, disciplinary guideline, and strike system should collapse into mechanisms unless a broader intervention pattern is being drafted.

Graduated Response Calibration

short_definition: Uses staged response levels so repeated, worsening, or unresolved problems receive stronger interventions only when thresholds justify escalation.

distinctive_feature: The response is organized as a sequence rather than a one-time fit judgment.

distinct_from_parent: The parent calibrates any response to severity and necessity; this variant focuses on staged escalation and release over repeated episodes.

still_same_parent_because: The governing question remains whether the response level is matched to severity, necessity, burden, and changed conditions.

use_when: The same kind of problem can recur or worsen over time.

use_when: A single severe response would be excessive for early or low-impact cases.

use_when: Decision-makers need predictable escalation bands and de-escalation criteria.

Response Scale

role: Defines the sequence of levels.

Escalation Threshold

role: Controls movement to stronger levels.

De-escalation Path

role: Defines release or step-down.

Graduated Sanctions

role: Implements response stages.

domain: platform moderation

example: A first low-harm violation receives a warning and education; repeated or severe violations trigger temporary limits and review.

domain: workplace discipline

example: A policy breach moves from coaching to written warning to suspension only when recurrence and impact justify escalation.

failure_modes: Escalation becomes automatic and ignores context.

failure_modes: The system lacks a credible de-escalation or restoration path.

Least-Burdensome Restriction Selection

short_definition: Chooses the least intrusive effective restriction when several responses can achieve the legitimate objective.

distinctive_feature: The decisive comparison is not simply severity, but whether a less burdensome effective option exists.

distinct_from_parent: The parent balances response and severity generally; this variant specifically screens alternatives by burden and effectiveness.

still_same_parent_because: It still depends on matching response strength to the necessary objective and avoiding excessive burden.

source_primes: proportionality constraint

use_when: Several possible responses could reduce the problem or risk.

use_when: Restrictions impose meaningful burden on autonomy, access, privacy, opportunity, or trust.

use_when: A decision-maker must justify why a stronger response is necessary.

Necessity Test

role: Defines the objective.

Burden Assessment

role: Compares burdens.

Least-Burdensome Effective Response

role: Selects the response.

Least-Restrictive Alternative Screen

role: Operationalizes comparison.

Response Matrix

role: Makes options visible.

example_domains: safety policy data_governance

domain: data governance

example: A system masks sensitive fields rather than blocking all access when masking sufficiently controls exposure.

failure_modes: Weak responses are selected because they look less burdensome even though they do not work.

failure_modes: Burden is counted for only one party and ignores affected bystanders.

Proportional Remedy Calibration

short_definition: Matches repair, compensation, correction, or restoration to the type and scale of harm rather than to punishment alone.

distinctive_feature: The calibrated object is the remedy, not only the restriction or sanction.

distinct_from_parent: The parent covers responses broadly; this variant focuses on matching repair to harm and closure requirements.

still_same_parent_because: The core structure is still fit between harm, necessary objective, burden, and selected response.

source_primes: proportionality accountability

use_when: A decision or action caused harm that needs repair.

use_when: The response should restore access, trust, status, resources, safety, or accuracy.

use_when: Punishment would not address the actual loss or injury.

Affected Interest Map

role: Identifies what needs repair.

Least-Burdensome Effective Response

role: Selects repair without excess.

Proportionality Review

role: Verifies remedy adequacy.

Proportional Remedy Menu

role: Lists calibrated repair options.

After-Action Proportionality Review

role: Checks outcome fit.

example: A grading error is corrected, the student receives missed opportunity restoration, and the process is reviewed to prevent recurrence.

failure_modes: Remedy becomes symbolic and fails to repair the actual burden.

failure_modes: Remedy overcorrects and imposes unrelated costs on others.

Risk-Based Response Scaling

short_definition: Scales response intensity to plausible severity, likelihood, exposure, and uncertainty when harm has not yet fully occurred.

distinctive_feature: It calibrates to prospective risk as well as actual harm.

distinct_from_parent: The parent covers proportional response generally; this variant may require probabilistic reasoning and uncertainty handling.

still_same_parent_because: It still asks whether the response burden is justified by the severity and necessity of the objective.

source_primes: proportionality probability uncertainty

use_when: The response is preventive or precautionary rather than retrospective.

use_when: Likelihood, exposure, and uncertainty are central to the fit judgment.

use_when: The system must avoid both panic-driven overreaction and negligence-driven underreaction.

Severity Assessment

role: Estimates impact scale.

Necessity Test

role: Determines whether preventive action is warranted.

Escalation Threshold

role: Defines preventive escalation.

Risk-Based Enforcement Protocol

role: Operationalizes risk tiers.

Response Matrix

role: Maps risk bands to responses.

example_domains: safety cybersecurity public_policy

domain: safety

example: A high-severity low-probability hazard receives monitoring and contingency controls before a full shutdown is justified.

failure_modes: Speculative risk justifies unlimited restriction.

failure_modes: Low-probability catastrophic cases are ignored because current harm is absent.

near_names:
Proportional Response Design
Response Fit Calibration
Minimum Sufficient Response
Least Restrictive Alternative
Sanction Fit Design
Graduated Sanctioning


Propositional Mode Governance

batch: Batch 07

related_primes: epistemic_humility evidence uncertainty validation inductive_reasoning abductive_reasoning modal_reasoning classification transparency traceability accountability data_integrity constraint completeness provenance context boundary formalization representation frame_of_reference decision normativity threshold

slug: assumption

slug: axiom

slug: evidence

slug: validation

slug: classification

slug: transparency

slug: traceability

slug: completeness

slug: provenance

slug: context

slug: boundary

slug: formalization

slug: representation

slug: decision

slug: normativity

components:
Propositional Unit Inventory
Epistemic Mode Taxonomy
Mode Assignment Basis
Licensed Operation Matrix
Mode Transition Protocol
Obligation Register
Communication Label Standard
Misuse and Drift Guardrail
Dependency and Scope Trace
Formal Proof or Model Binding
Stakeholder Mode Review

mechanisms:
Epistemic Status Ledger
Mode-Operation Matrix
Claim Status Labeling Standard
Assumption Expiry Timer
Hypothesis Promotion Gate
Premise Discharge Checklist
Axiom Boundary Statement
Proposition Lifecycle Board
Epistemic Mode Linter
Decision Memo Epistemic-Mode Section

essence_one_line: Keep propositions in the right epistemic mode and permit only the operations that mode licenses.

compression_statement: Propositional Mode Governance is a reasoning-governance archetype for systems where the same proposition may be treated as a fact, assumption, axiom, hypothesis, premise, belief, conjecture, estimate, norm, or open question. It separates proposition content from epistemic mode, records why the mode was assigned, defines which operations that mode allows, and controls transitions between modes. The output is not just a label; it is an operating discipline for what may be relied on, tested, proved, challenged, communicated, updated, or discharged.

core_intuition: Reasoning errors often arise not because a proposition is badly worded, but because people perform the wrong operation on it. They rely on a hypothesis as if it were a fact, refute an axiom from inside the system that adopts it, let a temporary premise leak into an unconditional conclusion, or communicate an assumption as settled knowledge. This archetype works by making the proposition’s mode explicit and by attaching obligations and permissions to that mode.

canonical_formula: proposition + epistemic_mode + assignment_basis + operation_license + transition_rule + obligation_record -> disciplined_reasoning_use

structural_problem: A system contains propositions whose epistemic modes are implicit, unstable, inconsistently communicated, or collapsed into generic claim text, causing actors to rely on, test, prove, challenge, or communicate them under the wrong operating rules.

condition: Claims, assumptions, hypotheses, premises, axioms, estimates, and beliefs appear together in one artifact.

notes: Examples include strategy decks, research memos, mathematical proofs, model cards, risk registers, policies, and requirements documents.

condition: A proposition needs to be used before it is fully warranted.

notes: The system must know whether it is using the proposition as an assumption, a working hypothesis, a decision premise, or a provisional belief.

condition: A proposition is accepted inside one system but challengeable from outside it.

notes: Axioms, model primitives, policy values, definitions, standards, and institutional premises often have this inside/outside structure.

condition: A conclusion depends on temporary premises, scope conditions, or unresolved tests.

notes: The dependency must remain visible so the conclusion is not reported as unconditional.

condition: Different modes imply different obligations.

notes: Hypotheses require tests; assumptions require monitoring; premises require discharge or scope; asserted facts require warrant; axioms require boundary declaration.

condition: Teams or tools move propositions across documents, models, databases, or decisions.

notes: Mode labels and transition history must travel with the proposition, not remain in the original author’s head.

symptoms: Untested assumptions become “facts” through repetition.

symptoms: Hypotheses are written as conclusions before discriminating evidence is collected.

symptoms: Axioms, model primitives, or policy values are argued over as if they were ordinary empirical claims inside the system that adopts them.

symptoms: Temporary premises in proofs, analyses, or legal arguments leak into final conclusions without discharge or scope.

symptoms: Decision documents mix evidence, estimates, norms, assumptions, and open questions in the same prose style.

symptoms: Reviewers cannot tell which propositions may be challenged, tested, relied on, revised, or bracketed.

symptoms: Confidence labels exist, but they do not say what operations the reader is licensed to perform.

symptoms: A change in evidence updates confidence but leaves unresolved whether the proposition should change mode.

root_tension: Reasoning needs propositions to be usable before all questions are settled, but use without explicit epistemic mode turns provisional, formal, normative, or conditional commitments into false certainty.

anti_signatures: The only issue is estimating degree of uncertainty or confidence, not distinguishing proposition modes; use uncertainty_explicitness or domain_specificity_of_confidence.

anti_signatures: The task is only to audit evidential support for beliefs or claims; use knowledge_warrant_audit unless mode-specific operations and transitions are central.

anti_signatures: The only hidden item is an assumption that should be surfaced; use implicit_assumption_surfacing unless multiple modes must be governed.

anti_signatures: The task is a specific null/alternative statistical test; use hypothesis_testing_frame unless the broader proposition lifecycle matters.

anti_signatures: The task is only checking deductive validity from already-stated premises; use deductive_chain_validation.

structural_intervention: Create an explicit proposition-mode layer that assigns each proposition a mode, records its assignment basis, maps allowed operations, attaches obligations, governs transitions, and carries the mode label through communication and downstream use.

action_logic: Inventory the propositions that matter before reasoning, decisions, models, or communications depend on them.

action_logic: Define a local but reviewable taxonomy of epistemic modes and the obligations each mode creates.

action_logic: Assign each proposition a current mode and record the basis for that assignment.

action_logic: Build a licensed-operation matrix so users know what may be relied on, tested, proved, challenged, discharged, updated, or communicated.

action_logic: Attach transition rules that specify how propositions move between modes and what evidence, proof, authority, or decision is required.

action_logic: Track obligations and unresolved dependencies until they are discharged, tested, scoped, or escalated.

action_logic: Label communications and downstream artifacts so the mode survives copying, summarization, automation, and decision reuse.

action_logic: Audit for mode drift, opportunistic relabeling, hidden assumption hardening, premature promotion, and ambiguous scope.

decision_rules: Do not permit a proposition to support irreversible action as an asserted fact unless its mode and warrant basis are recorded.

decision_rules: Allow assumptions to be relied on only within a stated scope, expiry condition, owner, and monitoring rule.

decision_rules: Require hypotheses to identify tests, discriminating evidence, or update criteria before promotion.

decision_rules: Treat axioms and primitives as non-refutable only inside their declared system; outside that boundary, allow comparative or foundational critique.

decision_rules: Require premises used conditionally to be discharged, scoped, or explicitly carried forward into the conclusion.

decision_rules: Require a transition record when a proposition changes mode.

decision_rules: When in doubt, label a proposition with the less-permissive mode until review clarifies the allowed operations.

target_invariants: The content of a proposition is separable from the mode in which it is currently held.

target_invariants: Every mode has explicit operation licenses and obligations.

target_invariants: Mode transitions are recorded and justified.

target_invariants: Labels travel with propositions into downstream artifacts.

target_invariants: Axioms and assumptions retain scope boundaries.

target_invariants: Conditional conclusions remain visibly conditional until premises are discharged.

target_invariants: Confidence and mode are related but not collapsed into one number.

expected_outcomes: Fewer assumptions harden into facts without review.

expected_outcomes: Research and decision artifacts distinguish hypotheses, premises, facts, estimates, values, and unknowns more reliably.

expected_outcomes: Reviewers can see which operations are licensed for each proposition.

expected_outcomes: Reasoning chains preserve scope and dependency information.

expected_outcomes: Mode-specific obligations are tracked instead of forgotten.

expected_outcomes: Communication becomes more honest without becoming paralyzed by uncertainty.

prose_essence:
Propositional Mode Governance prevents a common collapse in reasoning: treating every sentence-like thing as the same kind of claim. A proposition can have the same content while occupying different epistemic modes. “This mechanism causes the effect” can be a hypothesis in a research plan, a belief in a decision meeting, an assumption in a model, a premise in an argument, a conjecture in a theory, or an asserted finding in a report. The content alone does not tell users what they are allowed to do with it.

The archetype therefore adds a status layer over propositions. The layer says: this proposition is currently an assumption, hypothesis, axiom, premise, conjecture, belief, asserted finding, estimate, value premise, or open question; here is why; here is what you may do with it; here is what must happen before it can change status.

Assumption-Hypothesis Separation

variant_type: subtype

short_definition: A variant that prevents propositions provisionally relied on for action from being confused with propositions actively under test.

distinctive_feature: It focuses on the operational difference between relying under constraint and testing under an evidence plan.

distinct_from_parent: The parent governs all proposition modes; this variant concentrates on the recurring assumption-versus-hypothesis boundary.

still_same_parent_because: It uses the same mode assignment, operation license, transition, and obligation logic as the parent.

use_when: A plan, model, research design, or decision contains both operating assumptions and testable hypotheses.

use_when: The system is tempted to treat untested assumptions as findings or to treat hypotheses as already licensed premises.

use_when: Different review obligations apply to assumptions and hypotheses.

example: Separating “users need offline mode” as a hypothesis from “launch must happen before Q4” as a planning assumption.

domain: modeling

example: Keeping distributional assumptions separate from empirical hypotheses the model is designed to test.

Axiom Boundary Management

short_definition: A variant that declares which propositions are treated as primitive inside a formal or institutional system and how they can be critiqued from outside it.

distinctive_feature: It makes the inside/outside boundary of axiomatic commitment explicit.

distinct_from_parent: The parent governs all proposition modes; this variant specializes in axioms and primitives.

still_same_parent_because: It still depends on mode assignment and licensed operations: derive within, challenge from outside, version under governance.

use_when: A reasoning system, proof, model, policy, contract, or institution rests on primitives that are not refuted within the system.

use_when: Reviewers need to distinguish internal derivability from external critique of foundational commitments.

use_when: Different audiences may operate inside and outside the same axiom boundary.

domain: mathematics

example: Marking a proposition as an axiom of one theory while allowing comparison against alternative axiom sets.

domain: policy

example: Declaring values that the procedure treats as foundational rather than empirical findings.

Premise Discharge Tracking

short_definition: A variant that tracks temporary premises or conditional assumptions until they are discharged, scoped, or carried forward explicitly.

distinctive_feature: It treats the premise as a mode with an obligation to discharge or scope it.

distinct_from_parent: The parent governs the broader proposition lifecycle; this variant focuses on the proof or argument obligation attached to premises.

still_same_parent_because: The logic is still mode-specific operation licensing and transition recording.

use_when: Arguments, proofs, legal analyses, or decision chains contain temporary premises used for a bounded inference.

use_when: Downstream conclusions risk inheriting assumptions that were supposed to be discharged.

use_when: Reviewers need to know which conclusions are unconditional and which remain premise-dependent.

example: Flagging a conclusion that still depends on a temporary supposition.

example: Showing whether an argument depends on a stipulated fact or a proven fact.