name: Reversibility-Horizon Detection and Commitment Gating

slug: observability

slug: hysteresis

slug: accountability

components: Current Forward and Return-State Boundary and Outcome Frame

components: Reversal Stage Lead-Time and Execution Path

components: Time-Varying Reversal Cost Feasibility and Damage Model

components: Commitment Dependency Threshold Hysteresis and Loss Map

components: Horizon Scenario Uncertainty Distribution and Margin Set

components: Leading-Indicator Tripwire Review and Authority Gate

components: Return-Path Preservation Mitigation Fallback and Emergency Plan

components: Horizon Drift Trigger Performance and Outcome Review

mechanisms: Reversal-Cost and Feasibility-Curve Estimation

mechanisms: Decision–Execution Lead-Time and Margin Calculation

mechanisms: Threshold Hysteresis Dependency and Lock-In Stress Test

mechanisms: Return-Path Readiness and Rollback Rehearsal

mechanisms: Pre-Horizon Commitment Gate and Independent Challenge

mechanisms: Horizon Forecast-Error Trigger and Adaptation Audit

essence_one_line: Detect the approaching point where reversal becomes harder than continued commitment and act while a credible return path still exists.

compression_statement: Reversibility usually degrades gradually rather than disappearing at an obvious instant. Time, compounding investment, ecological damage, mobilization, dependencies, legal obligations, information loss, coordination, and opponent or market response can make return increasingly slow, costly, incomplete, or harmful. This archetype defines forward and reversal states; decomposes reversal cost and feasibility over time; models uncertainty and affected-party burden; identifies leading indicators and alternative horizons; establishes a margin before the point of no return; gates commitments, preserves return paths, and triggers stop, pivot, mitigation, or emergency action; then recalibrates as conditions change.

core_intuition: A point of no return is governable only if it is treated as a forecast interval with warning signals and decision lead time, not a retrospective label.

canonical_formula: forward_and_return_state_definition + time_dependent_reversal_cost_and_feasibility_model + commitment_dependency_damage_and_information_loss_map + uncertainty_distribution_and_scenario_bounds + leading_indicator_and_margin_set + pre_horizon_decision_and_authority_gate + return_path_mitigation_and_emergency_plan + horizon_drift_and_outcome_review -> action_before_reversal_closes

structural_problem: Actors continue along a path while the option to reverse silently degrades; by the time evidence is undeniable, reversal requires more cost, time, coordination, damage, or risk than moving forward.

trigger_conditions: Reversal cost grows with time or accumulated commitment

trigger_conditions: A physical ecological legal strategic or coordination threshold may close the return path

trigger_conditions: Delay between decision and effective reversal is material

trigger_conditions: Multiple groups bear unequal reversal costs

trigger_conditions: Forecast uncertainty is high but waiting is itself a commitment

symptoms: Exit estimates omit cleanup restoration or coordination

symptoms: Point-of-no-return language appears only after failure

symptoms: Options look reversible on paper but dependencies prevent execution

symptoms: Sunk investment dominates despite worsening forward value

root_tension: Waiting improves information but consumes maneuver time and option value; early action preserves reversibility but may incur unnecessary cost or abandon upside.

anti_signatures: The question is whether a transition is reversible at all without a temporal closure boundary; use Reversibility-Aware Transition Design.

anti_signatures: The issue is continuation bias after sunk costs; use Escalation Exit Gate.

anti_signatures: The task is discounted valuation only; use Temporal Discounting and Present-Value Framework Selection.

anti_signatures: The task is loss across a repeated cycle; use Cycle Efficiency and Reversibility Assessment.

structural_intervention: Build an uncertainty-aware horizon model and place accountable decision and preservation controls early enough to keep reversal feasible.

action_logic: Define current state, intended forward state, acceptable return state, boundary, stakeholders, horizon, and what counts as reversal.

action_logic: Decompose reversal into decision, mobilization, execution, stabilization, restoration, compensation, and verification stages with lead times.

action_logic: Model how financial, physical, ecological, informational, legal, social, strategic, and coordination costs change with time and commitment.

action_logic: Map dependencies, thresholds, hysteresis, lock-in, degraded resources, lost records, opponent or market response, and irreversible third-party harm.

action_logic: Compare the total cost, time, feasibility, uncertainty, and distribution of reversal with continued commitment across scenarios.

action_logic: Estimate earliest plausible, expected, and latest plausible horizon boundaries; include model error and observation delay.

action_logic: Select observable leading indicators, tripwires, safety margin, decision lead time, review cadence, and independent challenge authority.

action_logic: Preserve return paths through modular stages, reversible contracts, backups, reserves, interoperability, rollback artifacts, and bounded commitments.

action_logic: Gate new commitments before the safety margin; choose proceed, slow, pause, pivot, mitigate, reverse, or accept irreversibility explicitly.

action_logic: If a horizon is crossed, switch from impossible rollback claims to containment, adaptation, compensation, recovery, or forward redesign.

action_logic: Monitor conditions, forecast error, distributed burdens, trigger performance, and horizon drift; update owners and decisions.

decision_rules: Treat the horizon as an interval or scenario-dependent surface, not a falsely precise date.

decision_rules: Include the lead time needed to decide and execute reversal, not only the instant a condition crosses a threshold.

decision_rules: Count restoration, compensation, coordination, and third-party harm in reversal cost, not only direct expenditure.

decision_rules: Use a safety margin when observation is delayed, consequences are severe, or reversal capacity is uncertain.

decision_rules: Do not preserve nominal rollback while destroying data, skills, access, reserves, relationships, or legal rights required to execute it.

decision_rules: After the credible return path closes, acknowledge irreversibility and govern containment and adaptation rather than pretending the original option remains.

target_invariants: Forward current and acceptable return states are explicit

target_invariants: Reversal stages and lead times are modeled

target_invariants: Cost feasibility uncertainty and burden change over time

target_invariants: Multiple horizon scenarios and margins remain visible

target_invariants: Indicators triggers owners challenge and fallback precede closure

target_invariants: Return-path resources and artifacts are maintained

target_invariants: Crossed horizons trigger honest adaptation and remedy

expected_outcomes: Fewer retrospective point-of-no-return surprises

expected_outcomes: Better preservation of viable rollback paths

expected_outcomes: More transparent irreversibility acceptance

expected_outcomes: Faster adaptation when reversal is no longer credible

prose_essence:
Reversibility-Horizon Detection and Commitment Gating identifies when a still-available return option is likely to become too slow, costly, incomplete, harmful, or infeasible to use. It treats the “point of no return” as a forecast interval or scenario-dependent surface, not a dramatic date discovered after the fact. The intervention combines leading indicators, observation delay, decision and execution lead time, uncertainty margins, and accountable commitment gates so action occurs while maneuver remains.

The pattern does not design every detail of a reversible transition. Reversibility-Aware Transition Design owns the return-state contract, effect classification, restoration, rehearsal, and residue management of the transition itself. This archetype consumes evidence about that return path and asks a different question: how quickly is its feasibility degrading, what can be observed early enough, and what decision must occur before delay becomes commitment?

A horizon is crossed before the last physical act when the remaining time is shorter than observation, deliberation, authorization, mobilization, execution, and stabilization. It can also differ by group: capital may still be recoverable while a community right, ecosystem, supplier skill, or strategic option has already passed its safe boundary. Good governance therefore publishes multiple horizons, confidence, burden, and margin rather than one aggregate clock.

prose_when_to_use:
Use this archetype when reversal feasibility degrades with time, accumulated commitment, damage, dependency, legal obligation, information loss, coordination, public reliance, market or opponent response, resource depletion, or a nonlinear threshold. It is especially useful when waiting improves evidence but consumes the lead time required to act on that evidence.

Typical settings include decommissioning legacy infrastructure, capital projects with cancellation thresholds, habitat or climate intervention, clinical enrollment and exposure, strategic mobilization, supplier migration, public policy that creates reliance, destructive data transformation, and negotiations where disclosure or performance progressively narrows exit.

Prerequisites are a defined current, forward, and acceptable return state; at least partial evidence about reversal stages and degradation mechanisms; observable signals or proxies; decision authority able to act before closure; and an executable return, mitigation, or adaptation path. Where evidence is weak, the archetype remains useful if uncertainty is represented and the margin is conservative. False precision is not a prerequisite for control.

Do not use it when the main question is whether and how a transition can be restored independent of temporal closure; use Reversibility-Aware Transition Design. Do not use it for ordinary deadlines whose lateness raises inconvenience but does not degrade a meaningful return option. Sunk expenditure, an expiring preference, or a project milestone alone does not establish a horizon. If the credible return path has already closed, declare crossing and move to containment, adaptation, compensation, recovery, or forward redesign.

prose_structural_problem:
Reversibility usually erodes rather than vanishes. Each additional contract, dependency, exposure, demolition step, data overwrite, public promise, treatment dose, ecosystem disturbance, or mobilization decision changes the cost and feasibility of return. The organization sees forward progress clearly because it is tracked as delivery; return capacity is distributed across artifacts, people, rights, reserves, external actors, and environmental conditions and is rarely monitored as one declining option.

Observation is delayed. Damage may be latent, ecological signals noisy, market reactions nonlinear, data loss discovered only during restore, or distributed harms reported through slow channels. Decision is delayed by analysis, sponsorship, consultation, legal review, or conflict. Execution adds mobilization, sequencing, coordination, stabilization, restoration, and verification. A gate placed at the predicted physical threshold therefore fires too late.

The horizon can be multidimensional and asymmetric. One jurisdiction may lose legal exit earlier than another. A low-resource group may face an earlier practical horizon because it cannot wait for compensation. A pilot may retain rollback while full-scale decommissioning removes staff and interoperability. A strategic actor's disclosure can provoke a response that closes de-escalation even though material deployment remains reversible.

The core tension is information gain versus maneuver time. Waiting narrows uncertainty and avoids premature exit, but waiting is itself a commitment. Early action preserves options but can sacrifice benefit and create false alarms. The archetype makes that intertemporal trade governable through scenarios, margins, signals, and authority rather than optimism or retrospective blame.

prose_intervention_logic:
### 1. Frame the horizon question

Name the current state, intended forward state, acceptable return state, material boundary, affected parties, decision, and time horizon. Specify which return capability is at risk: technical restoration, legal exit, ecological recovery, strategic de-escalation, financial cancellation, social repair, or a combination. Reuse the transition-design return contract where one exists rather than redesigning it here.

### 2. Decompose total action lead time

Map observation, confirmation, deliberation, consultation, authorization, mobilization, execution, stabilization, restoration, compensation, and verification. Measure parallel and sequential stages, coordination bottlenecks, calendar constraints, and adverse-condition delay. The latest safe decision time is the projected closure time minus total action lead time and a justified uncertainty margin.

### 3. Model degradation mechanisms

Identify what narrows return: cumulative investment, decommissioning, lost skills, contract liability, dependency growth, threshold and hysteresis, contamination, information overwrite, resource depletion, public reliance, adversary response, coordination complexity, or third-party harm. Separate the original cause of commitment from the present mechanism closing the path.

### 4. Estimate reversal cost and feasibility through time

For several future points and scenarios, estimate cost, duration, completion probability, residual damage, required capacity, and burden by affected group. Compare reversal with continuation, pause, narrowing, or redesign, but never let favorable aggregate economics erase a fatal rights, safety, ecological, or strategic constraint. Use curves and bands rather than a single exit price.

### 5. Construct horizon scenarios and margins

Publish earliest plausible, expected, and latest plausible closure for each material dimension. Include parameter uncertainty, model error, observation lag, execution variability, correlated failure, and unequal access to remedy. Choose safety margins from consequence and controllability. The governing horizon is not automatically the average; it may be the earliest fatal boundary.

### 6. Select leading indicators and tripwires

Choose observable signals causally linked to degradation: restoration time, dependency count, irreversible spend, legacy staffing, habitat indicator, treatment exposure, contract termination, data divergence, opponent mobilization, reliance cases, reserve drawdown, or rollback rehearsal failure. Define source, latency, quality, cadence, owner, threshold, trend rule, missing-data treatment, and anti-gaming checks.

### 7. Place lead-time-aware gates

Trigger review before the physical or economic boundary. State what commitments are frozen while evidence is assessed, who can order pause or reversal, how independent challenge enters, and what affected parties can contest. Decisions include proceed within a bounded stage, slow, preserve capacity, pause, narrow, pivot, mitigate, reverse, or explicitly accept defined irreversibility.

### 8. Preserve decision-time maneuver

This archetype does not own full return design, but it must verify that the relied-upon path remains executable. Require current readiness evidence for artifacts, authority, staff, reserves, contracts, interoperability, restoration, and coordination. Delay decommissioning, maintain staged commitments, or purchase capacity when those actions preserve margin. Route detailed redesign to Reversibility-Aware Transition Design.

### 9. Escalate on uncertainty and signal conflict

Escalation should occur when margin shrinks below tolerance, indicators diverge, data are missing, model error grows, rehearsal exceeds the window, the sponsor disputes a tripwire, or burden concentrates on groups without decision power. Use an independent reviewer with authority to impose a temporary hold. Silence or missing data near a severe horizon is adverse evidence, not permission to proceed.

### 10. Declare crossing and change objectives

When credible return closes, record which horizon crossed, evidence, confidence, affected parties, and remaining options. Remove rollback claims and stop spending resources on impossible restoration theater. Shift to containment, harm reduction, compensation, adaptation, recovery, or forward redesign, with legitimate authority and communication.

### 11. Recalibrate from performance

Compare forecasts with actual signals, gate timing, decisions, reversal execution, false alarms, missed warnings, distributed harm, and post-crossing outcomes. Diagnose parameter error, structural model error, observation failure, authority delay, gaming, or ignored dissent. Update signals, margins, stage rules, owners, and models while preserving forecast versions for accountability.

A useful control state machine is **green** (margin comfortably exceeds total action time), **amber** (margin shrinking or uncertainty rising; new commitment bounded), **red** (latest safe decision window reached; pause, reverse, or accept irreversibility), and **crossed** (credible return infeasible; activate adaptation and remedy). State should be dimension- and group-specific, not one reassuring dashboard color.

prose_key_components:
### Current, Forward, and Return-State Boundary and Outcome Frame

This frame identifies the return capability whose closure matters, the affected boundary, and outcome tolerance. It imports detailed reversibility evidence rather than duplicating transition design. A horizon is meaningless without a specific return state and consequence.

### Reversal Stage, Lead-Time, and Execution Path

This component decomposes observation through verification and distinguishes decision time from effect time. It captures sequencing, parallelism, bottlenecks, coordination, and rehearsal measurements. It is the bridge between a forecast threshold and the earlier moment at which authority must act.

### Time-Varying Reversal Cost, Feasibility, and Damage Model

The model estimates how cost, duration, success probability, residual harm, capacity, and burden evolve. It includes continuation alternatives and shows when return ceases to satisfy the contract. Ranges and sensitivity replace a falsely exact crossover.

### Commitment, Dependency, Threshold, Hysteresis, and Loss Map

The map explains why the horizon moves. It connects commitments and signals to mechanisms such as decommissioning, adaptation, legal lock-in, information loss, resource degradation, threshold crossing, and external response. Causal linkage keeps tripwires from becoming arbitrary dashboard metrics.

### Horizon Scenario, Uncertainty, Distribution, and Margin Set

This component holds earliest, expected, and latest boundaries, observation and execution uncertainty, group-specific horizons, model limitations, and selected margins. It exposes value judgments about acceptable false alarms and missed crossings.

### Leading-Indicator, Tripwire, Review, and Authority Gate

The gate connects signals to action before closure. It defines data quality, cadence, thresholds, trend rules, missing-data behavior, owner, challenger, escalation, temporary hold, decision options, and evidence record. A warning without decision authority is only notification.

### Return-Path Preservation, Mitigation, Fallback, and Emergency Plan

This component verifies that the option cited by the horizon model remains usable and identifies minimum capacity to preserve. It also defines containment and adaptation after crossing. Detailed rollback engineering belongs to the neighboring transition-design archetype.

### Horizon Drift, Trigger Performance, and Outcome Review

The review maintains calibration. It compares forecast and observed closure, indicator latency, false alarms, misses, gate delay, burden, real reversal, and adaptation. It changes model and authority, not merely documents a postmortem.

Together the components form a forecasting-control loop: the state frame defines the option; the execution path defines lead time; models and maps project degradation; scenarios set margin; indicators and gates act; readiness preserves maneuver; and outcome review recalibrates the loop.

prose_common_mechanisms:
**Reversal-Cost and Feasibility-Curve Estimation** models full return cost, time, completion probability, residual damage, and burden across scenarios and dates. Curves may combine empirical restore data, contracts, ecological models, staffing, simulations, expert judgment, and market response. The output is a band with assumptions and sensitivity, not one authoritative number.

**Decision–Execution Lead-Time and Margin Calculation** combines observation, decision, mobilization, execution, stabilization, and verification distributions, then subtracts them plus safety margin from scenario closure. It produces an action window and identifies which delay dominates.

**Threshold, Hysteresis, Dependency, and Lock-In Stress Test** perturbs thresholds, restoration rates, correlated dependencies, response, and coordination. It searches for plausible early closure and discontinuity, including cases where crossing a threshold prevents return even after the original pressure is removed.

**Return-Path Readiness and Rollback Rehearsal** supplies measured execution time and detects missing capacity. Its role here is horizon evidence: a slower rehearsal moves the safe decision window earlier. Detailed repair of the return path routes to Reversibility-Aware Transition Design.

**Pre-Horizon Commitment Gate and Independent Challenge** freezes the next commitment, reviews signals and model uncertainty, hears affected parties, tests alternatives, and records proceed, slow, pause, pivot, reverse, or accept. The challenger must be able to impose a temporary hold when sponsor incentives favor delay.

**Horizon Forecast-Error, Trigger, and Adaptation Audit** compares versioned forecasts with actual closure, warning latency, gate action, burden, and post-crossing outcomes. It separates noisy-but-useful false alarms from badly designed triggers and distinguishes model error from governance delay.

These mechanisms do not become the archetype individually. A deadline calculator, early-warning dashboard, rollback drill, scenario model, or review board can exist without an integrated forecast, margin, authority, escalation, and crossing protocol.

prose_failure_modes:
### Retrospective point-of-no-return label

The organization names a horizon only after return fails. Require prospective versioned scenarios, leading signals, owners, and action windows before commitment.

### False precision

One date appears without model error, observation lag, or scenario spread. Publish early, expected, and late bounds, sensitivity, confidence, and dimension-specific surfaces.

### Execution lead time omitted

The decision is made before the threshold but reversal completes after it. Model and rehearse the full path; trigger at the latest safe decision time, not the physical boundary.

### Lagging indicator masquerades as warning

The signal confirms damage only after meaningful intervention is impossible. Trace indicator latency to the degradation mechanism and add earlier proxy, margin, or precautionary hold.

### Hidden restoration and third-party cost

Direct exit looks cheap while cleanup, compensation, reliance, external action, or residual harm is excluded. Use full-boundary evidence from transition design and disaggregate burden.

### Nominal return path not executable

The model assumes backups, rights, staff, reserves, or coordination that no longer exist. Require current readiness and move the horizon earlier when rehearsal slows or prerequisites decay.

### Pilot horizon extrapolated to scale

Small exposure suggests ample margin, but scale changes coordination, public response, or restoration time. Stress nonlinear stages before decommissioning or broad commitment.

### Trigger gaming or suppression

Sponsors redefine metrics, delay data, split commitments, or invoke discretion to avoid amber and red states. Freeze definitions, audit data latency, combine signals, preserve dissent, and give independent challengers stop authority.

### Analysis paralysis consumes margin

Repeated model refinement delays decision. Set evidence deadlines, default actions by state, and temporary holds when uncertainty remains high near a severe boundary.

### Crossed-horizon denial

Plans keep promising restoration to an infeasible state. Declare crossing, retire the claim, notify affected parties, and switch objectives to containment, adaptation, compensation, or forward redesign.

### Premature fatalism

A sponsor calls a horizon crossed to avoid funding a difficult but credible return. Require evidence, independent review, and comparison with preserved-capacity and narrower alternatives.

Recovery after a miss begins by stopping further commitment, preserving forecast and signal evidence, locating which horizon crossed, and protecting affected parties. Distinguish model error, observation failure, authority delay, execution failure, and deliberate suppression. Activate the remaining containment or adaptation plan, correct false public claims, assign remedy, revise indicators and margins, update gate authority, and monitor recurrence. If return remains feasible through redesign, hand that work to Reversibility-Aware Transition Design rather than expanding this archetype into general transition engineering.

prose_neighbor_distinctions:
**Reversibility-Aware Transition Design** defines whether and how state and outcomes can return, provisions dependencies, rehearses the path, and audits residue. This archetype observes how that capability degrades, forecasts its closure, subtracts decision and execution lead time, and gates commitment before the window closes. It may request readiness evidence or mitigation but should not duplicate the neighbor's full transition lifecycle.

**Temporal Discounting and Present-Value Framework Selection** values future costs and benefits. It can value early action but does not identify a feasibility boundary, warning signal, or latest safe decision time.

**Option Preservation** maintains multiple choices. A horizon model forecasts when one return option will cease to satisfy requirements; it can justify preservation but does not own the whole portfolio.

**Escalation Exit Gate** counters continuation bias and sunk-cost reasoning. Horizon gating applies even when actors reason without bias and physical, ecological, legal, or strategic closure is objective.

**Structural Constraint Identification and Lock-In** explains current constraints and history. Horizon detection is prospective: it models how an open path will close and acts beforehand.

**Cycle Efficiency and Reversibility Assessment** concerns repeated-cycle dissipation and regeneration, not the warning and authority lifecycle around a one-way boundary. Early-warning systems, dashboards, scenario plans, rollback drills, and deadlines are mechanisms unless integrated with lead time, margin, gate, and crossing response.

prose_cross_domain_examples:
### Legacy platform decommissioning

A migration remains technically reversible while old schemas, skills, vendor support, and data compatibility survive. The team measures rollback rehearsal time after each stage, tracks legacy staffing and write divergence, and models early, expected, and late closure. When execution time plus margin approaches vendor termination, an independent gate freezes decommissioning. Transition designers repair reconciliation and preserve staffing before the gate authorizes scale.

### Habitat fragmentation

A project monitors habitat connectivity, species response, hydrology, invasive spread, and restoration capacity. Because ecological signals lag, the latest safe intervention precedes the modeled damage threshold by observation, decision, and restoration lead time. Group-specific analysis includes community access and livelihood. Crossing shifts the plan from promised restoration to containment, stewardship, and compensation.

### Capital project and cancellation

Cancellation cost grows through procurement, site work, financing covenants, supplier commitments, and public reliance. The model separates sunk expenditure from future avoidable cost and legal exit. Tripwires track noncancelable orders, liability, and alternative capacity. Gates choose bounded continuation, renegotiation, pause, or acceptance before termination rights expire.

### Clinical trial exposure

Enrollment, cumulative dose, delayed adverse events, data uncertainty, and participant alternatives create different horizons. A leading-safety signal, monitoring latency, independent board schedule, contact time, and treatment discontinuation time determine the action window. Missing data near a severe signal triggers a hold. After biological reversibility closes, follow-up and remedy replace claims that stopping treatment undoes exposure.

### Strategic mobilization

Logistics can be recalled, but disclosure changes opponent behavior and alliance expectations. The map treats information exposure and response as closure mechanisms. Indicators combine observable response, force posture, negotiation channel, and mobilization time. A red gate requires de-escalation or explicit acceptance before the next public commitment.

### Public policy reliance

A temporary subsidy creates household and supplier reliance. The legal repeal date is later than the practical horizon at which abrupt withdrawal would cause severe harm. Indicators track enrollment, committed investment, alternative availability, and notice lead time. Gates either preserve a phased exit or explicitly fund transition and remedy.

### Destructive data transformation

A pipeline overwrites source detail while downstream models and reports proliferate. Snapshot retention suggests reversibility, but privacy rules, key expiry, schema divergence, and external exports narrow it. Rehearsal time becomes a leading indicator. The gate blocks source deletion until the current return window and downstream correction plan pass.

### Supplier transition

Old-supplier qualification, inventory, tooling, contracts, and workforce skill decay while the new supplier scales. The horizon surface differs for low volume and peak demand. A stress test moves the expected boundary earlier; a gate maintains dual qualification until return can be executed inside the required continuity window.

prose_non_examples:
A routine project deadline is not a horizon unless delay progressively closes a meaningful return option.

Sunk expenditure alone does not determine whether to continue or reverse. The model considers future reversal feasibility, future continuation value, burden, and closure mechanisms.

A backup timestamp does not reveal the latest safe decision time. Readiness, execution time, dependencies, external effects, and divergence must be measured.

A permanent one-way intervention with no credible return path has no remaining pre-horizon gate. It needs irreversibility acceptance and adaptation, not fictional forecasting.

A risk dashboard that reports current damage without observation latency, lead time, margin, authority, or action is monitoring, not this archetype.

A kill switch can stop forward operation but may not restore state or outcomes. It is one mechanism whose effective action time belongs in the model.

A sponsor's intuition that “we can always cancel later” is not evidence. Neither is declaring a point of no return solely to justify continued investment.

prose_tradeoffs:
**Information versus maneuver time.** Waiting improves estimates but consumes the ability to use them. Sequential evidence plans should define what can be learned before each margin boundary and what decision follows incomplete evidence.

**Early action versus lost upside.** Conservative gates may abandon a beneficial path. Bounded stages, pause, narrowing, and option-preserving mitigation can buy information without full continuation or full exit.

**Reserve capacity versus current efficiency.** Maintaining staff, contracts, interoperability, backups, and slack is costly. The cost should be compared with option value and explicitly funded; unfunded nominal readiness is worse than honest irreversibility.

**Wide margin versus false alarm.** Larger buffers protect against model error but produce more early interventions. Evaluate false alarms by avoidable cost and learning, not as automatic evidence that the trigger was poor.

**Simple dashboard versus plural horizons.** One date and color aid attention but can hide an earlier ecological, rights, or subgroup boundary. Layered interfaces can show an executive state while preserving dimension-specific evidence and dissent.

**Central speed versus distributed legitimacy.** Emergency authority acts quickly but can overlook local burden. Pre-authorized affected-party channels, clear stop powers, and review after emergency use reduce the tradeoff.

**Transparency versus strategic or market sensitivity.** Public thresholds support accountability but can be gamed or reveal plans. Publish decision logic and access-equivalent oversight while protecting legitimately sensitive operational detail.

prose_invariants_to_preserve:
- The specific return option and consequence boundary remain explicit.
- Observation, decision, mobilization, execution, stabilization, and verification time are included.
- Horizons remain intervals or scenario-dependent surfaces, not unsupported dates.
- Cost, feasibility, residual damage, uncertainty, and burden evolve through time and remain disaggregated.
- Threshold, hysteresis, dependency, information loss, and external response mechanisms remain visible.
- Group-specific or dimension-specific earlier horizons cannot be averaged away.
- Leading indicators are causally linked, observable, latency-aware, owned, and resistant to gaming.
- Tripwires and authority act before the latest safe decision window.
- Missing or stale evidence near a severe boundary does not authorize new commitment.
- Return readiness is current enough to support the forecast; general transition design remains separately owned.
- Crossing removes false rollback claims and activates containment, adaptation, compensation, or redesign.
- Forecast versions, dissent, gate decisions, and outcomes remain auditable for calibration.

prose_target_outcomes:
Decisions should occur while the chosen return, pause, or pivot remains executable. Leaders and affected parties should see how much margin remains, which assumptions control it, what signals can move it, and who can act. Reversal should no longer fail because the organization waited for certainty that arrived after mobilization time expired.

Expected system outcomes are fewer retrospective points of no return, earlier preservation or narrowing actions, less unanticipated lock-in, more credible rollback claims, and faster transition to adaptation after crossing. Distributional outcomes include visible group-specific horizons, earlier notice, legitimate participation, and less transfer of late-exit burden to those with least authority.

Metrics include decisions completed before the latest safe window; remaining margin by dimension; forecast interval coverage and calibration; trigger precision, recall, latency, and lead gained; rehearsal time versus modeled execution; preserved-capacity health; gate override and condition closure; missed-warning duration; group-specific residual harm; false rollback claims retired; and adaptation outcomes after crossing.

prose_parameter_dimensions:
### Return-state tolerance and governing dimension

Define which loss closes the option: cost, time, completion probability, rights, safety, ecological recovery, strategic exposure, or burden. A tolerable financial return cannot override a fatal safety boundary.

### Forecast range and scenario spread

Short ranges improve evidence but can hide slow lock-in; long ranges widen uncertainty. Maintain early, expected, and late scenarios and identify the assumptions controlling spread.

### Observation latency and data quality

Tune monitoring to the speed of degradation. Account for reporting delay, censoring, sparse data, manipulated indicators, and sensors that fail near threshold. Missing data may require amber or red state.

### Decision and execution lead time

Use measured distributions where possible. Include consultation, authorization, procurement, mobilization, coordination, stabilization, and verification, not only the reversal command.

### Confidence and model plurality

Confidence should reflect evidence, structural agreement, sensitivity, and forecast performance. Preserve multiple credible models when no justified collapse exists; gate against the earliest severe scenario or explain why not.

### Safety margin

Larger margins reduce missed crossings but increase premature action. Increase margin with severity, irreversibility, observation lag, model error, execution variability, weak controllability, or unequal harm.

### Trigger sensitivity, persistence, and combination

Single-point thresholds react quickly but may be noisy. Trend, persistence, multi-signal, and rate-of-change rules reduce false alarms but add delay. Predefine overrides and evidence rather than improvising after a signal fires.

### Monitoring and review cadence

Cadence should accelerate as margin shrinks and after any structural commitment. Event-driven review is essential for new contracts, decommissioning, exposure, habitat change, rehearsal failure, or external response.

### Preserved capacity and option cost

Backups, staff, interoperability, reserves, contracts, and slack maintain maneuver but cost current efficiency. Set the minimum readiness level required for the horizon claim and retire the claim when capacity falls below it.

### Gate independence and escalation authority

Consequential horizons require challengers outside delivery incentives, affected-party access, and power to pause. Tune escalation tiers to severity and time remaining; slow hierarchy can itself consume the margin.

### Post-crossing declaration threshold

Declare crossing when credible scenarios no longer meet the return contract within available capacity and time. Avoid both premature fatalism and denial. State uncertainty and remaining containment or adaptation options.

prose_variants_and_near_names:
**Economic and Contractual Reversibility Horizon** foregrounds accumulated investment, cancellation terms, liabilities, market response, switching cost, stranded assets, and rights. A financial crossover alone is insufficient when legal or social return closes earlier. Common signals include termination dates, irreversible purchase orders, legacy capacity, and counterparty reliance.

**Ecological and Physical Point of No Return** foregrounds nonlinear damage, thresholds, hysteresis, regeneration rates, monitoring lag, restoration uncertainty, and long-lived residue. Margins are typically wider because observation and recovery are slow and controlled experiments limited.

**Strategic and Operational Commitment Horizon** foregrounds mobilization, logistics, opponent or market response, information exposure, coordination, de-escalation, and emergency authority. The act that closes return may be disclosure or response rather than deployment itself.

“Point of no return,” “reversal-cost horizon,” and “commitment horizon” route to the parent. “Latest safe decision time” names an output of lead-time calculation. A deadline, budget threshold, kill switch, dashboard, or rollback rehearsal is a mechanism. Promote a variant only when distinct horizon mechanisms, indicators, governance, failure and recovery patterns recur across domains.

name: Economic and Contractual Reversibility Horizon

short_definition: Detects when accumulated financial legal market and switching commitments make reversal costlier than continuation.

distinctive_feature: Liabilities rights cancellation terms and market response dominate.

distinct_from_parent: Financial and contractual signals are primary but cannot override an earlier rights or safety horizon.

still_same_parent_because: Uses cost-feasibility curves lead time margin gates and fallback.

use_when: Market reliance or liabilities accumulate

example_domains: investment procurement infrastructure migration

domain: procurement

example: Noncancelable orders and supplier reliance trigger review before termination rights expire.

failure_modes:
Cancellation cost omitted
Market response missed
Stranded asset transfer
False rollback

name: Ecological and Physical Point of No Return

short_definition: Detects nonlinear physical or ecological closure where restoration becomes infeasible slow or severely incomplete.

distinctive_feature: Regeneration rates monitoring lag residual damage and hysteresis dominate.

distinct_from_parent: Long observation and recovery require wider margins and stronger precaution.

still_same_parent_because: Uses scenario horizons early indicators margins and adaptation.

use_when:
Thresholds or hysteresis matter
Monitoring lags damage
Regeneration is slow
Controlled reversal tests are limited

example_domains: ecosystems climate materials infrastructure

domain: habitat

example: Indicator and mobilization lag place the gate well before expected fragmentation threshold.

failure_modes:
Threshold precision illusion
Slow recovery ignored
Monitoring lag
Residual harm omitted

name: Strategic and Operational Commitment Horizon

short_definition: Detects when mobilization response disclosure logistics and coordination close de-escalation or rollback options.

distinctive_feature: Opponent reaction information exposure and execution time dominate.

distinct_from_parent: Interactive response can move the horizon as a consequence of the actor's own signal.

still_same_parent_because: Uses staged commitments executable return paths and pre-horizon authority gates.

source_primes: reversibility_horizon decision time

use_when:
Opponent or market response matters
Disclosure changes options
Mobilization takes time
Emergency authority may lag

domain: negotiation

example: Public commitment triggers counterparty response before material implementation.

failure_modes:
Response omitted
Mobilization delay
De-escalation fiction
Authority lag

near_names:
Point of No Return
Economic Reversibility Boundary
Latest Safe Decision Time


Reversible Operation Structure Design

type: archetype

source: generated

batch: Batch 01

canonical_family: Algebraic & Set-Theoretic Structure

aliases:
Group Structure Design
Algebraic Group Modeling
Reversible Composition System
Transformation Group Design
Invertible Operation Architecture

source_primes: group set_and_membership associativity

related_primes: closure commutativity invariance isomorphism symmetry relation composition category span basis linear_independence vector_space equivalence_relation bijectivity constraint order abstraction

slug: group

slug: associativity

slug: closure

slug: commutativity

slug: invariance

slug: isomorphism

slug: symmetry

slug: relation

slug: composition

slug: category

slug: span

slug: basis

slug: bijectivity

slug: constraint

slug: order

slug: abstraction

components:
Carrier Set Scope
Closed Binary Operation
Associativity Constraint
Identity Element Specification
Inverse Mapping Rule
Composition Trace
Invariant or Symmetry Target
Substructure Boundary
Homomorphism Translation Rule
Axiom Test Suite

mechanisms:
Operation Table or Cayley Table
Axiom Checklist for Group Structure
Inverse Operation Registry
Symmetry Transformation Catalog
Permutation Group Model
Group Action Model
Property-Based Algebraic Test
Rewrite and Cancellation Trace
Homomorphism Check

essence_one_line: Design the admissible operations of a system as a closed, associative, identity-bearing, invertible structure so composition and reversal stay reliable.

compression_statement: Reversible Operation Structure Design is the intervention pattern of declaring a carrier set, an operation for composing elements, a closure boundary, an associativity guarantee, an identity element, and inverse rules so that transformations can be chained, simplified, undone, compared, and translated without leaving the valid structure.

core_intuition: A group is useful because it turns many possible moves into a governed algebra of movement. If every allowed move stays inside the system, composes predictably, has a do-nothing baseline, and can be undone, then long chains of action can be inspected instead of treated as opaque history.

canonical_formula: G = <S, ∘, e, inv>; for a,b,c ∈ S: a∘b ∈ S, (a∘b)∘c = a∘(b∘c), e∘a = a∘e = a, and a∘a⁻¹ = a⁻¹∘a = e.

structural_problem: A system contains transformations, moves, permissions, state changes, or symbolic operations that are composed in practice, but the valid carrier set, composition rule, no-op state, inverse behavior, and invariant-preserving structure are not explicit. As a result, operation sequences are hard to simplify, undo, compare, translate, or audit.

trigger_conditions: Actors need to compose operations repeatedly and trust that the result remains valid.

trigger_conditions: Undo, cancellation, rollback, or reversibility is a load-bearing property rather than an afterthought.

trigger_conditions: Different operation sequences may be equivalent, but no formal structure exists for proving equivalence.

trigger_conditions: A domain relies on symmetry, transformation, permutation, or invariant reasoning.

trigger_conditions: A small set of primitive operations is expected to generate a larger reachable space.

trigger_conditions: An implementation claims to support reversible actions but lacks explicit inverse and identity semantics.

trigger_conditions: A mathematical or computational representation needs to transfer across domains without losing composition behavior.

symptoms: Some operation chains unexpectedly leave the intended state space.

symptoms: Rollback actions leave residues because they are compensations rather than true inverses.

symptoms: No one can identify the do-nothing operation or baseline transformation.

symptoms: Equivalent sequences are duplicated, overcounted, or treated inconsistently.

symptoms: Optimization depends on regrouping operations, but regrouping has not been proven safe.

symptoms: Symmetry claims are made informally without a catalog of transformations and preserved invariants.

symptoms: Translation between representations changes composition behavior in subtle ways.

root_tension: Reversible compositional structure creates powerful simplification and control, but it only works when the domain is tightly specified. Many real systems have irreversible side effects, partial operations, hidden state, or context-dependent rules that make group-like reasoning unsafe unless the boundary is explicit.

anti_signatures: Operations only need to stay inside a domain; identity and inverses are not required.

anti_signatures: The task is to compensate for damage after irreversible actions rather than design true reversibility.

anti_signatures: Composition is directional, partial, or typed in a way better modeled by category-like arrows rather than a single group operation.

anti_signatures: The relevant structure is a partition, complement, intersection, or union of sets rather than reversible operations on a carrier.

anti_signatures: The system intentionally accumulates history, path dependence, or irreversible commitment as the point of the design.

structural_intervention: Define the carrier set, operation, closure boundary, associativity guarantee, identity element, inverse rule, composition trace, invariant target, and translation checks that make operation sequences behave as a governed reversible structure.

action_logic: State the domain being modeled and decide what counts as an admissible element or transformation.

action_logic: Declare the carrier set and exclude elements that lack the required operation semantics.

action_logic: Define the binary operation for composing two admissible elements.

action_logic: Test closure: every composition of admissible elements must remain admissible.

action_logic: Test associativity so multi-step chains can be regrouped without changing the result.

action_logic: Identify and document the identity element that changes nothing under composition.

action_logic: For each admissible element, define and test a left and right inverse that returns to the identity.

action_logic: Record composition traces so cancellations, simplifications, generated substructures, and equivalent sequences are visible.

action_logic: Declare the invariants or symmetries preserved by the allowed operations.

action_logic: If the model is translated to another representation, check that the translation preserves composition structure.

decision_rules: If an action has no inverse, do not place it inside the group; model it as external, partial, irreversible, or compensating.

decision_rules: If a composition leaves the carrier set, either narrow the operation, expand the carrier, or reject the group model.

decision_rules: If associativity fails, use a non-associative algebra or explicit evaluation-order discipline instead.

decision_rules: If only some elements close under the operation, mark a subgroup or generated substructure rather than overclaiming full coverage.

decision_rules: If two representations are linked, accept the translation only when it preserves composition and identity behavior.

decision_rules: If the system has side effects, distinguish true inverse restoration from practical remediation or compensation.

target_invariants: The carrier set is explicit and reviewable.

target_invariants: The operation is defined for the intended admissible elements.

target_invariants: Closure is enforced relative to the carrier set.

target_invariants: Associativity holds for valid compositions.

target_invariants: A unique do-nothing identity behavior is identified for the structure.

target_invariants: Every included element has a valid inverse under the operation.

target_invariants: Operation traces preserve enough detail to prove equivalence, cancellation, and simplification.

target_invariants: Invariant and symmetry claims are tied to explicit operations, not metaphor.

target_invariants: Translations preserve operation, identity, and inverse behavior or are labeled lossy.

expected_outcomes: Composed operations become easier to simplify, audit, and compare.

expected_outcomes: True reversible operations are separated from merely compensating or remedial actions.

expected_outcomes: Symmetries, invariants, and equivalent configurations become explicit.

expected_outcomes: Reachable states under generated operations can be analyzed systematically.

expected_outcomes: Implementations become safer because closure, identity, inverse, and associativity tests catch invalid operations.

expected_outcomes: Representations can be transferred across domains with clearer preservation obligations.

prose_essence:
Reversible Operation Structure Design turns a domain of possible moves into a governed reversible operation structure. The key move is to stop treating each action as an isolated step and instead ask whether actions compose inside a declared carrier set, whether long chains can be regrouped safely, whether there is a do-nothing identity, and whether each included operation has a true inverse.

The archetype is useful whenever reversible composition, symmetry, cancellation, invariant preservation, or generated reachability is doing practical work. It is not enough that a system has operations. The operations must satisfy the full group-like package: closure, associativity, identity, and inverses.

Symmetry Group Modeling

variant_type: subtype

short_definition: Models allowed transformations as a group in order to identify what remains invariant.

distinctive_feature: The invariant target is the central reason for constructing the group structure.

distinct_from_parent: Narrower than the parent because it emphasizes symmetry and invariance rather than general reversible composition.

still_same_parent_because: It still depends on a carrier set, closed operation, associativity, identity, and inverses.

source_primes: group symmetry invariance

use_when: The practical problem is to reason about symmetry, invariance, or equivalent transformed configurations.

use_when: The transformations can be composed and reversed.

domain: geometry

example: Rotations of a shape are cataloged to determine which appearances are equivalent.

failure_modes: Symmetry is asserted visually but the allowed transformations do not actually close under composition.

Rollback-Inverse Operation Design

short_definition: Designs operational actions so each admissible action has an explicitly modeled inverse or cancellation counterpart.

distinctive_feature: Inverse availability is central to operational safety and auditability.

distinct_from_parent: Narrower because it applies group-like reversibility to operational rollback and state management.

still_same_parent_because: It remains a group-structure variant only when closure, identity, associativity, and inverse semantics are preserved.

source_primes: group closure composition

use_when: Actions are expected to be reversible rather than merely compensated afterward.

use_when: The system must compose many operations without losing an auditable undo path.

example: A command framework treats each state transition as paired with an inverse command and records composite undo traces.

failure_modes: A compensating action is mistaken for a true inverse even though it leaves residual state changes.

Generated Subgroup Scoping

short_definition: Uses selected generators to define the reachable subgroup rather than assuming the whole carrier is reachable.

distinctive_feature: The generated subset and its closure boundary are the practical focus.

distinct_from_parent: Narrower because it emphasizes generated reachability and subgroup boundaries.

still_same_parent_because: It still relies on group closure, identity, inverses, and associative composition inside the generated scope.

source_primes: group span basis

use_when: Only certain primitive operations are available.

use_when: Reachability by composition matters more than the entire abstract group.

example: A small set of reversible moves is tested to determine which configurations are reachable.

failure_modes: The reachable subset is assumed to be the full group without proof.


Revision-Readiness Precommitment

batch: Second Iteration/Batch 07

aliases:
Update Condition Precommitment
If-Then Belief Revision Commitment
Evidence-Triggered Revision Commitment
Precommitted Belief Update Trigger
Revision Trigger Precommitment

related_primes: uncertainty metacognition feedback threshold accountability bounded_rationality

slug: uncertainty

slug: metacognition

slug: feedback

slug: threshold

slug: accountability

components:
Revisable Claim Statement
Current Confidence Baseline
Evidence Update Condition
Observation Window
Revision Threshold
Update Action Mapping
Accountability Trace
Independent Revision Witness
Alternative Hypothesis Set
Dissonance Buffer

mechanisms:
If-Then Revision Contract
Preregistration Refutation Table
Forecast Update Trigger Log
Strategic Revisit Gate
Belief Update Review Template
Red-Team Update Condition Session

essence_one_line: Specify in advance what evidence would change a belief, forecast, diagnosis, or strategy so that later revision is easier, more accountable, and less vulnerable to motivated reinterpretation.

compression_statement: Revision-readiness precommitment converts a vague willingness to be corrected into an explicit if-evidence-then-update structure: a current claim, confidence state, observation window, revision threshold, and update action are all stated before the contested evidence arrives.

core_intuition: People often say they will change their minds, but after new evidence appears they can reinterpret it, move the goalposts, or protect identity and prior commitments. A precommitment makes the update conditions visible while the person or institution is still comparatively cool, reducing the room for post hoc rationalization.

canonical_formula: claim + current confidence + update condition + observation window + revision action -> accountable evidence-sensitive revision

structural_problem: A belief, forecast, diagnosis, or strategy may need revision, but the decision-maker has not defined what evidence would be sufficient to trigger revision before incentives and emotions attach to the existing view.

trigger_conditions: A claim or plan is important enough that failure to revise would be costly.

trigger_conditions: The relevant evidence will arrive after the current commitment is made.

trigger_conditions: The decision-maker is exposed to confirmation bias, sunk-cost pressure, identity threat, status incentives, or institutional inertia.

trigger_conditions: The current belief is plausible but uncertain, and future observations can discriminate among alternatives.

trigger_conditions: A later review would otherwise be vulnerable to goalpost shifting or selective interpretation.

symptoms: Participants insist they are open-minded but cannot name what would change their view.

symptoms: Evidence reviews repeatedly end with reinterpretations that preserve the prior conclusion.

symptoms: Confidence remains stable despite repeated prediction errors or failed assumptions.

symptoms: Debates become arguments over standards only after inconvenient evidence appears.

symptoms: Strategic plans continue without clear thresholds for reconsideration.

root_tension: Epistemic humility requires openness to revision, but human and institutional cognition often protects existing commitments unless the revision standard is fixed before the challenge arrives.

anti_signatures: The question is already settled by a hard rule, direct measurement, or externally binding standard.

anti_signatures: The future evidence cannot be specified even approximately without fabricating false precision.

anti_signatures: The main problem is lack of evidence collection rather than unwillingness to revise.

anti_signatures: The decision must be made immediately and cannot support a prospective observation window.

anti_signatures: The precommitment would be used to freeze a bad threshold and block legitimate contextual judgment.

structural_intervention: Create an explicit revision contract before new evidence arrives: define the current claim, the alternative possibilities, the observations that would warrant change, the evidential threshold, the time or sampling window, and the action that follows if the condition is met.

action_logic: State the current belief, forecast, diagnosis, or strategic assumption in a form that can be challenged by future observation.

action_logic: Record the current confidence level or commitment strength so later movement is visible rather than hidden.

action_logic: Identify the specific kinds of evidence that would count for, against, or around the claim.

action_logic: Define revision thresholds before the contested evidence appears, including what would weaken, reverse, or retire the claim.

action_logic: Link each threshold to an update action, such as reducing confidence, reopening a decision, changing a forecast, commissioning a review, or replacing a plan.

action_logic: Preserve an accountability trace so later reviewers can distinguish genuine revision from goalpost shifting.

decision_rules: Use this archetype when the cost of motivated non-revision is greater than the cost of specifying update conditions.

decision_rules: Prefer qualitative thresholds when numerical thresholds would create false precision.

decision_rules: Require a named observation window when evidence timing matters.

decision_rules: Separate evidence that changes confidence from evidence that directly changes action.

decision_rules: Escalate to external review when the same actor who benefits from the existing belief controls the revision judgment.

target_invariants: The update conditions must be defined before the evidence to be evaluated is known.

target_invariants: The conditions must be observable enough that later parties can judge whether they were met.

target_invariants: The revision action must be meaningful, not merely symbolic.

target_invariants: The commitment must preserve legitimate learning rather than forcing mechanical updating from noisy evidence.

target_invariants: The record must make later goalpost movement visible.

expected_outcomes: Better calibration between confidence and evidential warrant.

expected_outcomes: Reduced motivated reasoning and post hoc reinterpretation.

expected_outcomes: Faster recognition of wrong assumptions or stale strategies.

expected_outcomes: More credible claims of openness to evidence.

expected_outcomes: Improved institutional learning from forecasts, experiments, and reviews.

Forecast Revision Trigger Precommitment

short_definition: A forecasting variant that defines event, time, and evidence triggers that will change a forecast probability.

distinctive_feature: It pairs prospective evidence with explicit probability movement.

distinct_from_parent: The parent pattern is domain-general; this variant is probability-forecast specific.

still_same_parent_because: The core intervention remains precommitted evidence-triggered revision.

use_when: A forecast will be updated over time as signals arrive.

use_when: The forecaster may be tempted to preserve a public prediction after contrary signals appear.

example_domains:
election forecasting
market forecasting
risk estimation

domain: forecasting

example: A forecaster states in advance which polling or event evidence would move a probability estimate.

failure_modes: Overfitting update triggers to a noisy signal

failure_modes: Moving the probability too mechanically without considering evidence quality

Research Refutation Precommitment

short_definition: A research variant that states what results would support, weaken, or refute a hypothesis before analysis begins.

distinctive_feature: It uses study-design artifacts such as preregistration tables or analysis plans.

distinct_from_parent: It is narrower and tied to empirical research workflows.

still_same_parent_because: The logic remains precommitted conditions for belief revision.

use_when: Hypotheses, exclusion rules, or analytical comparisons could be changed after seeing data.

use_when: The evidential claim needs credibility against p-hacking, hindsight interpretation, or selective reporting.

example_domains:
clinical trials
social science research
product experiments

domain: research

example: An analysis plan states which results would disconfirm the primary interpretation before data inspection.

failure_modes: Preregistered standards are too vague to constrain interpretation

failure_modes: The team treats preregistration as a shield against legitimate exploratory learning

name: Strategic Revisit Threshold Precommitment

short_definition: A governance variant that defines performance or context thresholds that will reopen a strategy or plan.

distinctive_feature: It links evidence thresholds to governance action rather than only confidence revision.

distinct_from_parent: The parent can revise beliefs without institutional action; this variant uses decision gates and authority structures.

still_same_parent_because: The intervention is still pre-evidence commitment to revision conditions.

source_primes: epistemic_humility threshold feedback

use_when: A plan should not be abandoned too quickly but also should not persist despite clear contrary evidence.

use_when: Stakeholders need a credible agreement about when strategy review will occur.

example_domains:
business strategy
public policy implementation
program evaluation

domain: strategy

example: A board agrees that two quarters below a retention threshold triggers strategy review.

failure_modes: The revisit gate occurs but lacks authority to change the plan