RTM — Review Trigger Module
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: PGA™ — Prediction Governance Architecture
Parent Standard:
PMRS — Prediction Monitoring & Revision Standard
Operational Layer: Prediction Monitoring & Revision
Category:
Governance & Enforcement
Subcategory:
Prediction Governance
Type: Parent Standard Module
Governed Space:
Prediction Review Trigger Determination
Version: 1.0
Status: Canonical · Open Module
Effective Date: 10 September 2026
Compatibility: OOF® Methodology OS™ · GOA™ · ORA™ · VALIDOS™ · INTEGROS® · ADIT® · AGA™ · AIG® · ASGA™ · CLIA® · SIMULOS®
AI-Readable: Yes
Authority: OOF®
Protection:
MIP® — Methodological Intellectual Property
Canonical Language:
English (UCL™)
Governed Space
RTM governs the determination of whether an observed predictive change, monitoring condition, lifecycle event, governance conflict, or loss of support is sufficient to require formal Prediction Review.
It converts monitored change into a governed decision about review necessity.
RTM governs the transition:
Governed Prediction Change Monitoring State + Applicable Trigger Criteria + Predictive Context → Governed Prediction Review Trigger Determination
RTM does not decide whether the prediction must be revised.
It determines whether the prediction must be formally reviewed.
Canonical Definition
The Review Trigger Module (RTM) defines the operational governance requirements by which observed changes and predictive lifecycle conditions are evaluated against governed trigger criteria to determine whether an existing Governed Prediction may continue without formal review or must enter Prediction Review.
RTM determines when a change is sufficiently relevant, material, consequential, persistent, structurally significant, uncertainty-producing, support-reducing, or otherwise governance-significant to justify formal reconsideration of the prediction's current state.
It separates three distinct questions:
Has something changed?
Does that change require formal review?
Does formal review require prediction revision?
PCHM governs the first.
RTM governs the second.
PRVM governs the third.
Observed Change ≠ Review Trigger
Review Trigger ≠ Revision Requirement
Operational Role
RTM is the second operational module within PMRS.
Its focused operational question is:
Has a monitored change, lifecycle condition, or governance event become sufficiently material that the current Governed Prediction must enter formal review?
RTM consumes:
Change Escalation Candidates from PCHM; applicable trigger policies; Prediction Basis state; assumption state; uncertainty condition; feasibility condition; collective prediction condition; prediction-use context; lifecycle state; Prediction Horizon; operational consequence.
Its primary function is:
Assess Trigger Basis → Determine Trigger State → Escalate to Prediction Review where required
RTM does not itself perform the substantive review of the prediction.
Internal Module Flow
Prediction Change Monitoring → Review Trigger → Prediction Revision → Prediction Supersession → Prediction Withdrawal
Current Position:
Review Trigger
1. Review Trigger
A Review Trigger is a governed condition establishing that an existing prediction must enter formal review.
A Review Trigger may arise from:
material change; cumulative change; assumption deterioration; basis deterioration; uncertainty increase; feasibility change; external event; lifecycle milestone; governance conflict; monitoring failure; use-context change.
The trigger initiates review.
It does not predetermine the review outcome.
2. Trigger Basis
Every Review Trigger should have an identifiable Trigger Basis.
Potential Trigger Bases include:
observed material change; threshold crossing; predefined event; scheduled review date; loss of monitoring coverage; Prediction Basis deterioration; assumption failure; Prediction Horizon milestone; contradictory evidence; major prediction-use feedback; collective-structure change.
Review Without Trigger Basis ≠ Governed Review Initiation
3. Trigger Criteria
Trigger Criteria define the conditions under which formal review becomes mandatory or advisable.
Criteria may be:
qualitative; quantitative; event-based; threshold-based; time-based; state-based; composite.
Trigger Criteria should remain explicit enough to be governable.
4. Trigger Materiality
A change may be real but insufficient to justify formal review.
RTM therefore evaluates whether the change is materially relevant to:
predictive meaning; probability; uncertainty; feasibility; assumptions; Prediction Basis; collective legitimacy; continuing use; reliance; historical continuity.
Real Change ≠ Review-Worthy Change by Default
5. Materiality Context
The same change may trigger review for one prediction but not another.
Materiality depends on:
Prediction Object; purpose; horizon; sensitivity; uncertainty; consequence; reliance degree; operational context.
Same Change ≠ Same Trigger Outcome
6. Trigger Sensitivity
Trigger sensitivity defines how readily a prediction enters review when relevant change is detected.
High-consequence predictions may require lower trigger thresholds.
Exploratory predictions may tolerate more change before formal review.
7. Trigger Proportionality
The trigger framework should remain proportionate.
Too-sensitive triggers may create constant unnecessary review.
Too-insensitive triggers may permit stale predictions to remain active.
More Review Triggers ≠ Better Governance by Default
8. Scheduled Review Trigger
A prediction may require formal review at predefined intervals even when no material change is detected.
Examples:
monthly; quarterly; annual; milestone-based.
Scheduled review is especially useful where the environment changes gradually.
9. Event-Triggered Review
A specified event may require review.
Examples:
regulatory approval; regulatory rejection; major policy change; supplier failure; system failure; merger; market discontinuity; infrastructure outage.
Event Occurrence ≠ Automatic Prediction Revision
The event may trigger review without predetermining the result.
10. Threshold-Triggered Review
Formal review may begin when a monitored variable crosses a defined threshold.
Examples:
uncertainty exceeds X; basis age exceeds Y; source reliability falls below Z; feasibility indicator falls below threshold.
Thresholds should remain justified and traceable.
11. Condition-Triggered Review
Review may be required where a defined condition no longer holds.
Example:
Assumption A must remain true.
If:
Assumption A = No Longer Supported
then:
Review Trigger = Active
12. Cumulative Trigger
Multiple individually minor changes may collectively justify review.
This is important where no single event appears decisive.
Minor Change + Minor Change + Minor Change → Potentially Material Review Trigger
RTM should support cumulative trigger logic.
13. Compound Trigger
A Compound Trigger requires multiple conditions to occur together.
Example:
Basis Decay + Increased Uncertainty + High Reliance
may activate review even where none would independently be sufficient.
14. Single-Critical Trigger
Some events are sufficiently material to trigger immediate review on their own.
Examples:
discovery of materially incorrect Prediction Basis; invalidated core assumption; methodology integrity issue; major structural environmental change.
15. Prediction Basis Trigger
A Prediction Basis Trigger may arise where:
a critical source is invalidated; material new information appears; source relevance materially declines; basis becomes incomplete; contradictory information emerges.
PBAS owns the basis.
RTM determines whether the post-formation change requires formal review.
16. Prediction Basis Decay Trigger
Prediction Basis Decay may be gradual.
RTM should allow review to trigger when decay reaches a point where continued predictive support becomes uncertain.
Decay Detected ≠ Review Trigger by Default
The trigger depends on materiality.
17. Source Integrity Trigger
A source used in Prediction Basis may later be shown to contain:
major error; fabrication; methodological defect; material correction; scope mismatch.
This may create an immediate Review Trigger.
18. Assumption Trigger
Review may be triggered where a material assumption becomes:
weakened; unstable; contradicted; no longer supported.
The trigger depends on the assumption's predictive importance.
19. Critical Assumption Trigger
A critical assumption whose failure materially undermines the prediction may trigger immediate formal review.
Critical Assumption Failure ≠ Prediction Falsification
It establishes review necessity.
20. Probability-Relevant Trigger
New information may be sufficiently strong that the probability representation may no longer remain current.
RTM may issue:
Probability Review Trigger
without determining the new probability.
21. Uncertainty Trigger
Review may be required where uncertainty materially increases.
Example:
Probability remains 70%
but:
uncertainty structure becomes substantially broader
The prediction may require formal review even without numerical probability change.
22. Confidence Trigger
A material deterioration in confidence-relevant conditions may trigger review.
Stable Probability ≠ Stable Review State
23. Feasibility Trigger
A material change in feasibility conditions may trigger review.
Examples:
required infrastructure no longer available; funding removed; regulatory permission denied; technical constraint introduced.
PFES owns feasibility.
RTM governs whether the change requires prediction review.
24. Collective Prediction Trigger
Collective predictions may require review when:
participation changes materially; independence deteriorates; influence concentration increases; aggregation logic changes; large contributors dominate; common information becomes widespread.
Stable Collective Probability ≠ Stable Collective Governance State
25. Predictor–Reality Proximity Trigger
A material change in Predictor–Reality Proximity may trigger review where it affects:
informational conditions; source independence; interpretation; later performance analysis.
The transition itself does not imply misconduct.
26. External Event Trigger
An external event may trigger review where it materially alters the predictive environment.
RTM should distinguish:
Relevant External Event
from:
Material Review-Triggering Event
27. Structural Change Trigger
Structural system change may justify immediate or elevated review.
Examples:
market redesign; legal regime change; major technological transition; supply-chain restructuring.
Structural changes often affect multiple predictive assumptions simultaneously.
28. Operational Change Trigger
A material change in Operational Reality may trigger review.
ORA™ may provide the relevant governed operational state.
RTM does not establish Operational Reality itself.
29. Use-Feedback Trigger
Prediction use may change the environment sufficiently to trigger review.
Example:
Prediction → Action → Material Environmental Change
The prediction may then require renewed assessment under the changed environment.
30. Reflexivity Trigger
Where prediction dissemination materially affects the predicted system, a Review Trigger may arise.
Examples:
market movement; shortage behavior; congestion behavior; preventive maintenance.
Prediction Influence ≠ Automatic Review Trigger
The influence must be materially relevant.
31. Reliance Change Trigger
A prediction originally used informationally may later become the primary decision basis.
This may justify review because the consequence of continued predictive use has materially changed.
Reliance Change ≠ Prediction Change
But it may create a Review Trigger.
32. Use Boundary Change Trigger
A prediction may be proposed for use outside its original PUBM boundary.
This does not automatically revise the prediction.
It may require review of whether the prediction remains appropriate in the expanded context.
33. Consequence Escalation Trigger
A low-consequence prediction may become involved in a high-consequence process.
Example:
planning forecast → safety-critical automated control
The prediction may require review even where predictive content remains unchanged.
34. Automation Escalation Trigger
Movement from:
Human Decision Support
to:
Autonomous Execution
may create a formal review requirement.
Automation Increase ≠ Prediction Improvement
The changed use context may demand renewed governance.
35. Horizon Trigger
Review may occur as the Prediction Horizon approaches or where the horizon itself becomes operationally misaligned.
Examples:
milestone reached; horizon nearly expired; event timing shifts materially.
36. Horizon Expiry Trigger
Prediction Horizon expiry may trigger transition toward outcome governance.
It does not itself determine outcome.
Horizon Expiry ≠ Outcome Determination
37. Prediction Staleness Trigger
A prediction may be old enough or contextually outdated enough that its continuing use becomes questionable.
RTM should distinguish:
Chronological Age
from:
Predictive Staleness
Old Prediction ≠ Stale Prediction by Default
38. Monitoring Coverage Trigger
Insufficient monitoring coverage may itself trigger review.
If critical predictive conditions are no longer observable, continuing legitimacy may become uncertain.
Cannot Observe ≠ Can Assume Stability
39. Monitoring Failure Trigger
A significant monitoring failure may require review of the current prediction because relevant changes may have gone undetected.
The prediction itself may not have changed.
The knowledge about its current legitimacy has.
40. Data Gap Trigger
A material information gap may trigger review where continued prediction use depends upon missing information.
Missing Information ≠ Neutral Information
41. Contradictory Signal Trigger
Contradictory high-quality signals may trigger review even when no single signal is decisive.
Conflict itself may be governance-significant.
42. Model Change Trigger
A change in the model used to support continuing prediction maintenance may require review where comparability or validity is affected.
Model Change ≠ Historical Prediction Revision
43. Methodology Change Trigger
A material methodology change may create a Review Trigger and potentially require prediction reformation rather than ordinary revision.
PMES owns methodology.
RTM identifies that review is required.
44. Governance Conflict Trigger
Conflicting governance states may themselves trigger review.
Example:
Prediction Current
while:
RLCM = Reliance Not Supported
and:
PUBM = Use Suspended
Such conflict may justify formal reconsideration.
45. Validation Trigger
A new VALIDOS™ validation result may trigger review where it materially affects predictive legitimacy.
Validation Result ≠ Automatic Revision
46. Evidence Trigger
New governed evidence may trigger review where it materially affects Prediction Basis or assumptions.
ADIT® may govern evidence.
RTM determines review necessity.
47. Integrity Trigger
A material integrity issue may trigger immediate review.
Examples:
concealed assumption; undocumented basis change; version tampering; misrepresented methodology.
INTEGROS® may govern broader integrity.
RTM governs review initiation within PMRS.
48. Trigger Severity
RTM may classify Trigger Severity as:
Low
Moderate
High
Critical
Severity may affect:
review urgency; permissible continued use; escalation level; temporary restrictions.
49. Trigger Urgency
Trigger Urgency concerns how quickly formal review should begin.
Possible states:
Routine
Priority
Urgent
Immediate
Urgency is distinct from materiality.
High Urgency ≠ Automatic Revision
50. Trigger Persistence
A trigger condition may be:
Transient
Recurring
Persistent
Structural
Persistence may influence whether review is required.
51. Trigger Reversibility
Some trigger conditions may resolve rapidly.
Others may be irreversible.
Reversibility can affect review urgency and scope.
52. Trigger Confidence
RTM may record confidence in the Trigger Basis.
Possible states:
Well Supported
Supported with Qualification
Uncertain
Weakly Supported
Unable to Determine
Low confidence does not always mean no review.
In high-consequence contexts, uncertainty itself may justify review.
53. Trigger Sufficiency
Trigger Sufficiency describes whether available governance information is sufficient to determine review necessity.
Trigger Sufficiency ≠ Prediction Confidence
54. Trigger Qualification
A trigger may be qualified.
Examples:
Review Required — Probability & Uncertainty Scope
Review Required — Feasibility Scope
Review Required — Prediction Basis Scope
Review Required — Full Prediction Scope
This helps prevent unnecessary full re-review.
55. Review Scope Recommendation
RTM may indicate the minimum review scope justified by the Trigger Basis.
Possible scopes:
targeted; component-specific; multi-component; full prediction review; full lifecycle re-entry.
RTM does not perform the review itself.
56. Targeted Review
A Targeted Review addresses one specific prediction component.
Example:
Feasibility condition changed
while methodology, object, and basis remain stable.
Review Requirement ≠ Full Prediction Reconstruction by Default
57. Full Review Trigger
A Full Review Trigger may arise where multiple foundational dimensions change simultaneously.
Examples:
Prediction Basis; assumptions; methodology relevance; feasibility; horizon.
58. Lifecycle Re-Entry Trigger
A sufficiently material change may require re-entry into an earlier PGA governance stage.
Example:
Methodology no longer appropriate
may require return to PMES.
Prediction Object materially changed
may require return to POPS.
RTM may identify the need for re-entry.
59. Continued-Use Pending Review
Where review is required, RTM may also identify whether the prediction can continue to be used pending review.
Possible states:
Continued Use Permitted
Continued Use with Qualification
Continued Use Restricted
Use Suspension Recommended
Unable to Determine
PCRS/PUBM remain responsible for final use governance.
60. Trigger State
RTM may classify the current state as:
No Review Trigger
Monitoring Continuation Required
Review Recommended
Review Required
Urgent Review Required
Immediate Review Required
Trigger Condition Uncertain
Unable to Determine
These states govern review necessity.
They do not determine revision.
61. False Positive Trigger
A false positive occurs where a trigger is initiated but subsequent review finds no material predictive change.
This is not necessarily a governance failure.
A cautious trigger system may legitimately produce some false positives.
62. False Negative Trigger
A false negative occurs where material change exists but no review is initiated.
This may allow stale prediction persistence.
False negatives can be more serious in high-consequence predictive environments.
63. Trigger Calibration
Trigger systems may require calibration to balance:
missed material change; excessive review; consequence; volatility; cost; latency.
Trigger Sensitivity ≠ Trigger Quality by Default
64. Trigger Override
An implementation may allow authorized override of a trigger state.
Override should remain:
explicit; attributable; reasoned; time-bound where appropriate; historically preserved.
Override ≠ Trigger Deletion
65. Human Trigger Initiation
Humans may initiate Prediction Review even where automated trigger criteria have not fired.
Such initiation should preserve Trigger Basis.
Human Concern ≠ Automatic Prediction Revision
66. AI-Assisted Trigger Detection
AI may identify:
emerging contradictions; assumption instability; basis deterioration; environmental drift; unusual signal combinations.
AI may recommend Review Trigger activation.
AI Trigger Recommendation ≠ Governed Trigger Determination
unless the system has explicit authority and satisfies RTM.
67. Autonomous Trigger Determination
An autonomous system may be authorized to activate formal review.
Where so, the decision should preserve:
Trigger Basis; criteria; evidence references; state; timestamp; scope.
Autonomous Triggering ≠ Autonomous Revision
68. Cross-System Trigger Propagation
A Review Trigger may need to propagate to systems consuming the prediction.
Example:
Source Prediction → Review Required
should be capable of informing:
dashboards; APIs; AI agents; operational systems.
Propagation should preserve the trigger state without pretending a revised prediction already exists.
69. Trigger Timestamp
RTM should distinguish:
event time; detection time; trigger evaluation time; trigger activation time; review initiation time.
Trigger Time ≠ Event Time
70. Trigger History
All material Review Trigger states should remain historically reconstructable.
A later review outcome must not erase the fact that a trigger occurred.
71. Historical Trigger Integrity
Later knowledge should not retroactively alter what justified review at the time.
Later Knowledge ≠ Earlier Trigger State
This supports fair lifecycle evaluation.
72. Trigger Traceability
A governed Review Trigger should permit reconstruction of:
Prediction → Monitoring State → Change Signal → Trigger Basis → Trigger Criteria → Trigger Determination → Review State
This separates monitoring from review and review from revision.
73. Governance Requirements
A governed RTM implementation should be able to:
1. identify the Governed Prediction; 2. identify the current prediction version; 3. consume PCHM Change Escalation Candidates; 4. identify Trigger Basis; 5. define Trigger Criteria; 6. assess Trigger Materiality; 7. evaluate Materiality Context; 8. calibrate Trigger Sensitivity; 9. preserve Trigger Proportionality; 10. support Scheduled Review Triggers; 11. support Event-Triggered Review; 12. support Threshold-Triggered Review; 13. support Condition-Triggered Review; 14. support Cumulative Triggers; 15. support Compound Triggers; 16. identify Single-Critical Triggers; 17. identify Prediction Basis Triggers; 18. identify Prediction Basis Decay Triggers; 19. identify Source Integrity Triggers; 20. identify Assumption Triggers; 21. identify Critical Assumption Triggers; 22. identify Probability-Relevant Triggers; 23. identify Uncertainty Triggers; 24. identify Confidence Triggers; 25. identify Feasibility Triggers; 26. identify Collective Prediction Triggers; 27. identify Predictor–Reality Proximity Triggers; 28. identify External Event Triggers; 29. identify Structural Change Triggers; 30. identify Operational Change Triggers; 31. identify Use-Feedback Triggers; 32. identify Reflexivity Triggers; 33. identify Reliance Change Triggers; 34. identify Use Boundary Change Triggers; 35. identify Consequence Escalation Triggers; 36. identify Automation Escalation Triggers; 37. identify Horizon Triggers; 38. identify Horizon Expiry Triggers; 39. identify Prediction Staleness Triggers; 40. identify Monitoring Coverage Triggers; 41. identify Monitoring Failure Triggers; 42. identify Data Gap Triggers; 43. identify Contradictory Signal Triggers; 44. identify Model Change Triggers; 45. identify Methodology Change Triggers; 46. identify Governance Conflict Triggers; 47. identify Validation Triggers; 48. identify Evidence Triggers;
49. identify Integrity Triggers; 50. classify Trigger Severity; 51. classify Trigger Urgency; 52. identify Trigger Persistence; 53. identify Trigger Reversibility; 54. record Trigger Confidence; 55. determine Trigger Sufficiency; 56. issue Trigger Qualification; 57. recommend Review Scope; 58. distinguish Targeted Review from Full Review; 59. identify Lifecycle Re-Entry Triggers; 60. identify continued-use implications pending review; 61. determine Trigger State; 62. preserve distinction between Review Trigger and Revision Requirement; 63. identify false positive triggers; 64. identify false negative triggers; 65. support Trigger Calibration; 66. govern Trigger Override; 67. support Human Trigger Initiation; 68. support AI-assisted Trigger Detection; 69. support Autonomous Trigger Determination where authorized; 70. preserve distinction between autonomous triggering and autonomous revision; 71. propagate trigger states across systems where needed; 72. preserve Trigger Timestamp; 73. preserve Trigger History; 74. preserve Historical Trigger Integrity; 75. maintain Trigger Traceability; 76. prevent later review outcomes from rewriting earlier Trigger Basis; 77. prevent review necessity from being conflated with predictive invalidity; 78. produce a Governed Prediction Review Trigger Determination.
74. Governance Logic
RTM applies the following operational logic:
Has PCHM identified a Change Escalation Candidate or has another legitimate trigger source emerged?
If no:
→ preserve No Review Trigger and continue monitoring.
If yes:
→ identify Trigger Basis.
Does the Trigger Basis relate materially to the current prediction?
If no:
→ preserve monitoring without formal review.
If yes:
→ evaluate Trigger Criteria.
Does the change exceed applicable materiality, threshold, event, condition, cumulative, or lifecycle criteria?
If no:
→ classify Monitoring Continuation Required .
If uncertain:
→ classify Trigger Condition Uncertain or Review Recommended , depending on consequence and governance context.
If yes:
→ determine applicable Review Scope.
Is the trigger narrow and component-specific?
If yes:
→ issue Targeted Review Required .
Does the trigger affect multiple foundational predictive dimensions?
If yes:
→ issue Full Review Required .
Does the trigger concern a fundamental earlier-stage governance problem?
If yes:
→ identify Lifecycle Re-Entry Required .
At every stage:
Review Trigger ≠ Revision Requirement
The output proceeds into substantive Prediction Review and, where justified, PRVM.
75. Minimum Implementation Framework
A. Prediction Reference
Identifier of the Governed Prediction.
B. Prediction Version
Current prediction state under trigger evaluation.
C. Change Monitoring Reference
Relevant PCHM monitoring state or Change Escalation Candidate.
D. Trigger Basis
Observed change, event, threshold, lifecycle condition, governance conflict, or other legitimate basis.
E. Trigger Criteria
Applicable criteria for formal review initiation.
F. Materiality Assessment
Assessment of relevance and materiality to continuing predictive legitimacy.
G. Trigger Type
Scheduled, Event, Threshold, Condition, Cumulative, Compound, Critical, or other governed trigger type.
H. Trigger Severity
Low, Moderate, High, Critical.
I. Trigger Urgency
Routine, Priority, Urgent, Immediate.
J. Trigger Sufficiency
Whether available governance information is sufficient to determine review necessity.
K. Review Scope
Targeted, component-specific, multi-component, full, or lifecycle re-entry.
L. Continued-Use Pending Review State
Applicable temporary use position where relevant.
M. Review Trigger State
Current RTM determination.
N. Trigger Qualification
Conditions, caveats, or scope qualifications attached to the trigger.
O. Trigger Version & History
Historical record of trigger determination, timing, criteria, and state changes.
76. Governance Outputs
RTM may produce:
Governed Prediction Review Trigger Determination Trigger Basis Record Trigger Criteria Profile Trigger Materiality Determination Prediction Basis Review Trigger Assumption Review Trigger Probability Review Trigger Uncertainty Review Trigger Feasibility Review Trigger Collective Prediction Review Trigger Predictor–Reality Proximity Review Trigger Operational Change Review Trigger Use-Feedback Review Trigger Monitoring Failure Review Trigger Integrity Review Trigger Trigger Severity Classification Trigger Urgency Classification Trigger Sufficiency State Review Scope Determination Lifecycle Re-Entry Indicator Continued-Use Pending Review State Review Trigger State
Trigger History Record
The primary output is:
Governed Prediction Review Trigger Determination
Where formal review establishes that predictive change is required, the lifecycle proceeds to:
PRVM — Prediction Revision Module
77. Critical Distinctions
Observed Change ≠ Review Trigger
Review Trigger ≠ Revision Requirement
Review Without Trigger Basis ≠ Governed Review Initiation
Real Change ≠ Review-Worthy Change by Default
Same Change ≠ Same Trigger Outcome
More Review Triggers ≠ Better Governance by Default
Event Occurrence ≠ Automatic Prediction Revision
Decay Detected ≠ Review Trigger by Default
Critical Assumption Failure ≠ Prediction Falsification
Stable Probability ≠ Stable Review State
Stable Collective Probability ≠ Stable Collective Governance State
Relevant External Event ≠ Material Review-Triggering Event
Prediction Influence ≠ Automatic Review Trigger
Reliance Change ≠ Prediction Change
Automation Increase ≠ Prediction Improvement
Horizon Expiry ≠ Outcome Determination
Old Prediction ≠ Stale Prediction by Default
Cannot Observe ≠ Can Assume Stability
Missing Information ≠ Neutral Information
Model Change ≠ Historical Prediction Revision
Validation Result ≠ Automatic Revision
High Urgency ≠ Automatic Revision
Trigger Sufficiency ≠ Prediction Confidence
Review Requirement ≠ Full Prediction Reconstruction by Default
Override ≠ Trigger Deletion
Human Concern ≠ Automatic Prediction Revision
AI Trigger Recommendation ≠ Governed Trigger Determination
Autonomous Triggering ≠ Autonomous Revision
Trigger Time ≠ Event Time
Later Knowledge ≠ Earlier Trigger State
78. Enterprise Application
RTM can govern formal review initiation across:
enterprise forecasting; supply-chain forecasting; investment forecasting; insurance; infrastructure; manufacturing; predictive maintenance; AI forecasting; autonomous systems; public-sector planning; logistics; energy systems; prediction markets; model ensembles; multi-agent systems; strategic planning; operational forecasting.
Its function remains constant:
ensure that a prediction enters formal review when its continuing legitimacy has become sufficiently questionable — but not merely because the world produced another signal.
79. Use Case 1 — Demand Forecast Faces Multiple Small Changes
A company maintains a Governed Prediction:
68% probability that regional demand will increase by at least 15% within nine months.
PCHM detects over six weeks:
modest reduction in orders; mild increase in customer cancellations; delayed infrastructure project; slightly weaker economic indicator; one large customer postponing investment.
No individual change appears decisive.
The critical governance question is:
Can individually moderate changes collectively become sufficient to require formal Prediction Review?
RTM evaluates the changes as a Cumulative Trigger .
It identifies that together they affect:
Prediction Basis; demand assumptions;
feasibility conditions; current use of the forecast in procurement planning.
The collective change exceeds the configured review materiality threshold.
RTM determines:
Review Required — Multi-Component Scope
It does not alter the 68% probability.
Cumulative Change ≠ Automatic Prediction Revision
Review Trigger ≠ Revision Requirement
The prediction now enters substantive review.
80. Use Case 2 — AI Prediction Moves Into Autonomous Safety Control
An AI system produces a governed prediction:
84% probability of a major component failure within 48 hours.
The prediction was originally governed for:
human maintenance decision support.
No new predictive data has appeared.
No probability has changed.
The organization proposes integrating the output into a control system that would automatically stop a critical process whenever probability exceeds 80%.
PCHM identifies:
Reliance Change + Use Boundary Change + Automation Escalation
The critical governance question is:
Can a prediction remain outside formal review when the predictive content is unchanged but the consequences of relying upon it have materially increased?
RTM determines that the shift from:
Human Decision Support
to:
Autonomous High-Consequence Execution
creates an Automation Escalation Trigger and Consequence Escalation Trigger .
RTM issues:
Urgent Review Required
with scope focused on:
reliance fitness; uncertainty; threshold interpretation; prediction freshness;
automation context; safeguards.
The prediction has not yet been revised.
Reliance Change ≠ Prediction Change
Use Context Change Can Still Require Prediction Review
Review Trigger ≠ Revision Requirement
81. Canonical Boundary
RTM governs whether monitored predictive change, lifecycle conditions, external events, Prediction Basis deterioration, assumption deterioration, probability- or uncertainty-relevant change, feasibility change, collective-structure change, Predictor–Reality Proximity change, Operational Reality change, prediction-use feedback, staleness, monitoring failure, integrity conditions, governance conflict, consequence escalation, or other materially relevant conditions require formal Prediction Review. It governs Trigger Basis, Trigger Criteria, materiality, severity, urgency, sufficiency, Review Scope, lifecycle re-entry, continued-use implications pending review, and Review Trigger State. It does not monitor primary change signals, perform substantive Prediction Review, recalculate probability, revise the prediction, supersede or withdraw the prediction, establish Operational Reality, or assess predictive performance.
PCHM owns:
Prediction Change Monitoring
RTM owns:
Prediction Review Trigger Determination
PRVM owns:
Prediction Revision
PSSM owns:
Prediction Supersession
PWM owns:
Prediction Withdrawal
82. Canonical Closing Statement
A prediction should not be reopened every time the world changes, and it should not remain untouched when the changes around it become materially relevant.
RTM governs that threshold by separating ordinary change from review-worthy change, and review-worthy change from the much stronger conclusion that the prediction itself must be revised.
A Review Trigger opens the prediction to scrutiny. It does not rewrite the prediction before that scrutiny has occurred.