PMRS — Prediction Monitoring & Revision Standard
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: PGA™ — Prediction Governance Architecture
Operational Layer: Prediction Monitoring & Revision
Category:
Governance & Enforcement
Subcategory:
Prediction Governance
Type: Parent Standard
Governed Space:
Prediction Monitoring & Revision
Origin ID: PGA-PMRS-010
Version: 1.0
Status: Canonical · Open Standard
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™)
Canonical Definition
The Prediction Monitoring & Revision Standard (PMRS) governs how a Governed Prediction is monitored after formation and use, how material changes in its Prediction Basis, assumptions, conditions, environment, probability structure, feasibility, interpretation context, or operational relevance are identified, and how the prediction is reviewed, revised, superseded, withdrawn, preserved, or returned for further governance without destroying its historical continuity.
PMRS establishes the governance conditions under which an existing prediction may remain current, require review, undergo revision, be replaced by a subsequent prediction state, cease to support continued use, or remain preserved solely as historical predictive record.
The Standard governs the transition:
Current Governed Prediction → Monitored Prediction State → Review / Revision / Supersession / Withdrawal → Governed Updated Prediction State
while preserving every materially relevant prior prediction state.
PMRS therefore governs how a prediction changes after it has been formed — not whether the predicted outcome has already occurred.
Operational Role
A prediction is not static merely because it was legitimate when originally formed.
After formation:
new information may emerge; Prediction Basis may change; assumptions may weaken; operational conditions may shift; feasibility may change; collective prediction structures may change; uncertainty may increase or decrease; Prediction Horizon may approach; external events may materially alter the predicted environment; the prediction itself may influence Operational Reality.
PMRS establishes the governance layer required to determine whether the prediction remains legitimate in its existing form.
Its core responsibility is:
to preserve continuing predictive legitimacy under changing conditions without allowing revision to erase prediction history.
Core Question
When changing information or conditions materially affect a prediction, when must it be preserved, reviewed, revised, superseded, or withdrawn?
Governance Flow
Current Governed Prediction → Prediction Condition Monitoring → Material Change Detection → Review Trigger Assessment → Prediction Review → Revision Determination → Revision / Preservation / Supersession / Withdrawal → Updated Governed Prediction State → Continued Monitoring
This flow may repeat throughout the Prediction Lifecycle.
Prediction governance is therefore not necessarily linear after publication.
A prediction may move through multiple governed states before its Prediction Outcome becomes available.
Governed Space
PMRS governs:
prediction monitoring; prediction-condition monitoring; Prediction Basis change; Prediction Basis Decay; assumption change; assumption failure; probability-relevant change; uncertainty change; confidence-relevant change; feasibility change; collective prediction change; operational-condition change; external-condition change; prediction-use feedback; Prediction Horizon proximity; review triggers; prediction review; revision thresholds; revision necessity; prediction preservation; prediction revision; partial revision; material revision; prediction supersession; prediction withdrawal; temporary review states; revision qualification; revision traceability; version continuity; historical prediction preservation; prediction-state transitions; re-entry into earlier PGA™ governance where necessary.
PMRS governs the continuing condition of the prediction.
It does not determine whether the predicted outcome has occurred.
Prediction Monitoring Principle
A prediction remains governed only while the conditions supporting its continued use remain sufficiently understood.
Initial legitimacy does not create permanent legitimacy.
Therefore:
Valid Initial Prediction ≠ Valid Continuing Prediction
And:
Historical Predictive Conditions ≠ Current Predictive Conditions
A prediction may remain unchanged.
Its environment may not.
Monitoring ≠ Continuous Recalculation
Prediction Monitoring does not require every prediction to be continuously recalculated.
Monitoring may be:
continuous; periodic; event-triggered; threshold-triggered; condition-triggered; manually initiated; automatically initiated.
The required monitoring intensity should reflect the prediction's:
consequence; volatility; horizon; dependency structure; uncertainty; use; change sensitivity.
Prediction Monitoring ≠ Continuous Model Execution
Monitoring Scope
PMRS should identify what must be monitored in order to determine whether continued predictive legitimacy may have changed.
This may include:
Prediction Basis; material assumptions; critical external conditions; relevant operational conditions; feasibility conditions; collective-prediction conditions; predictor-source conditions; uncertainty structure; prediction use; Prediction Horizon; major intervening events.
Monitoring scope should remain proportionate.
Not every change in the environment is relevant to every prediction.
Material Change
A Material Change is a change capable of meaningfully affecting:
predictive meaning; probability; uncertainty; feasibility; assumption validity; Prediction Basis relevance; Prediction Horizon interpretation; permitted use; reliance legitimacy.
Change ≠ Material Change by Default
PMRS requires materiality assessment before forcing revision.
Prediction Basis Change
PBAS establishes Prediction Basis at formation.
PMRS governs whether that basis remains sufficiently current after formation.
New information may:
reinforce the basis; weaken the basis; contradict the basis; replace a source; invalidate a source; introduce materially new information.
PMRS does not re-own Prediction Basis governance.
Where material reconstruction is required, the prediction may return to PBAS.
Prediction Basis Decay
Prediction Basis Decay describes material deterioration in the continuing relevance, completeness, reliability, or contextual adequacy of information upon which a prediction depends.
Decay may arise because:
data becomes stale; external conditions change; source reliability deteriorates; new information supersedes earlier observations; the environment moves outside the original basis; a previously important signal loses relevance.
Prediction Basis Decay does not automatically invalidate the prediction.
It creates a governance question:
Does the existing prediction still remain sufficiently supported?
Assumption Change
PAS establishes Predictive Assumptions.
PMRS governs whether those assumptions continue to hold.
A material assumption may become:
Still Supported
Weakened
Changed
No Longer Supported
Unable to Determine
Where an assumption materially changes, the prediction may require review.
Assumption Failure
Assumption Failure occurs where a material assumption necessary for predictive meaning no longer holds.
This may require:
qualification; restriction; revision; supersession; withdrawal.
But:
Assumption Failure ≠ Automatic Prediction Falsification
The prediction may still occur.
The issue is whether the prediction remains legitimately supported under its original governed structure.
Condition Change
Predictions may depend upon conditions that were not formally classified as assumptions but remain materially relevant.
Examples:
regulation; infrastructure availability; market structure; weather; supply availability; geopolitical environment; organizational capability.
PMRS governs whether such change materially affects continuing predictive legitimacy.
Probability-Relevant Change
New information may change the probability assigned to the predicted outcome.
PMRS does not independently calculate the revised probability.
Where recalculation is required, the prediction must return to the relevant probability governance under PPUS.
PMRS determines when that reconsideration becomes necessary.
Uncertainty Change
Uncertainty may increase even where the headline probability remains numerically unchanged.
Example:
70% → 70%
may conceal a materially wider uncertainty structure.
Therefore:
Same Probability ≠ Same Predictive State
PMRS requires material uncertainty changes to remain visible.
Confidence Change
Confidence may change because:
source quality changes; model validity changes; assumptions weaken; prediction basis narrows; environment becomes less comparable.
A stable probability value must not conceal materially reduced confidence.
Feasibility Change
PFES governs Prediction Feasibility.
PMRS monitors whether feasibility conditions materially change.
A predicted outcome may move from:
Feasible
to:
Conditionally Feasible
or:
Feasibility Restricted
or:
Infeasible
without the original prediction itself being immediately replaced.
Such change may trigger review.
Collective Prediction Change
Where a prediction is governed through CPGS, relevant collective conditions may change after formation.
Examples:
participant composition changes; source diversity declines; dependence increases; influence concentration increases; aggregation mechanism changes; major contributors exit; new information becomes widespread.
Same Collective Number ≠ Same Collective Structure
PMRS must preserve such changes where they materially affect predictive legitimacy.
Prediction Source Change
A predictor, source, model, institution, market, or information provider may materially change.
This may affect:
continuity; comparability; methodology; Prediction Basis; confidence; independence.
A source label remaining the same does not guarantee source continuity.
Predictor–Reality Proximity Change
Predictor–Reality Proximity may change during the Prediction Lifecycle.
A predictor may move:
Information-Advantaged → Decision-Proximate
or:
Decision-Proximate → Outcome-Influencing
Such transitions may materially affect interpretation and later performance evaluation.
PMRS preserves these changes without treating them automatically as misconduct.
Change in Predictor–Reality Proximity ≠ Prediction Revision by Default
But it may require review.
Prediction Use Feedback
PCRS-governed use may itself influence the environment.
The flow may become:
Prediction → Communication → Reliance → Action → Changed Operational Reality
Where use materially changes the predicted environment, PMRS must assess whether the prediction remains valid under the changed conditions.
Reflexive Prediction Monitoring
Predictions capable of influencing their predicted outcome require monitoring of that influence.
Examples include:
market forecasts; bank-run forecasts; election forecasts; demand forecasts; shortage forecasts; congestion forecasts.
The prediction may become part of the system it predicts.
Prediction Observation ≠ Prediction Influence
Prediction Influence ≠ Outcome Control
But influence can create a legitimate revision trigger.
Intervening Event
An Intervening Event is an occurrence after prediction formation that may materially alter the predicted environment.
Examples:
regulatory decision; geopolitical event; system failure; acquisition; new competitor; natural disaster; technological breakthrough; policy change; operational disruption.
Not every intervening event requires revision.
Its material relevance must be established.
Prediction Horizon Proximity
As the Prediction Horizon approaches, the prediction's informational environment may change materially.
New evidence may progressively reduce uncertainty or fundamentally alter expectations.
PMRS may therefore apply different monitoring intensity across the Prediction Horizon.
Prediction Horizon Expiry
The end of the Prediction Horizon does not by itself establish the outcome.
It establishes that the prediction's specified predictive interval has ended.
Outcome governance belongs to POFPS.
Prediction Horizon End ≠ Outcome Determination
Review Trigger
A Review Trigger is a governed condition requiring formal reconsideration of the prediction.
Potential Review Triggers include:
material new information; Prediction Basis Decay; assumption failure; material assumption weakening; feasibility change; uncertainty change; major probability-relevant information; material collective-structure change; prediction-use feedback; intervening event; Prediction Horizon milestone; governance conflict; evidence correction.
Review Trigger ≠ Revision Requirement
The trigger begins review.
It does not determine the result.
Scheduled Review
Some predictions may require periodic review even where no explicit material event occurs.
Scheduled review may be appropriate where:
conditions evolve continuously; Prediction Horizon is long; prediction use is consequential; underlying basis is volatile; assumptions require periodic confirmation.
Event-Triggered Review
An identified event may trigger review immediately.
Examples:
Regulatory Approval Issued
Major Data Revision Published
Critical Assumption Invalidated
Prediction Basis Source Withdrawn
Threshold-Triggered Review
A review may be required when a monitored condition crosses a defined threshold.
Examples:
source reliability below threshold; uncertainty above threshold; basis age above threshold; market change above threshold; feasibility condition below threshold.
Thresholds should not become arbitrary hidden revision rules.
Prediction Review
Prediction Review evaluates whether the prediction remains sufficiently supported in its current governed form.
Possible review outcomes include:
Prediction Remains Current
Prediction Remains Current with Qualification
Revision Required
Supersession Required
Withdrawal Required
Additional Governance Required
Unable to Determine
Prediction Preservation
A prediction should remain unchanged where review does not establish a material basis for revision.
This is important.
PMRS is not a mechanism for continuously rewriting predictions.
It is a mechanism for determining whether change is justified.
Review ≠ Revision
Revision Threshold
A Revision Threshold identifies the level of change at which the existing predictive state can no longer legitimately remain unchanged.
Revision thresholds may be:
methodological; informational; assumption-based; probabilistic; uncertainty-based; feasibility-based; operational; policy-based.
The threshold must remain governable and traceable.
Prediction Revision
Prediction Revision occurs where material change requires modification of the governed prediction.
Revision may alter:
probability; uncertainty; range; assumptions; Prediction Basis; feasibility state; conditions; Prediction Horizon; predictive scope; qualification.
Revision must create a new governed prediction state.
It must not rewrite the prior state.
Partial Revision
Not every revision requires reconstruction of the entire prediction.
A specific dimension may change while others remain valid.
Example:
Probability revised
while:
Prediction Object; methodology; horizon; boundary;
remain unchanged.
PMRS should support proportionate revision.
Material Revision
A Material Revision changes a substantive component of predictive meaning.
Examples:
65% → 35%
Feasible → Infeasible
12-month horizon → 24-month horizon
Region A → Region A + B
Condition X removed
Such revisions must remain clearly distinguishable from editorial corrections.
Technical Correction ≠ Prediction Revision
Corrections such as:
spelling; formatting; metadata normalization; identifier correction;
do not automatically constitute material prediction revision.
Technical Correction ≠ Predictive Change by Default
Prediction Reformation
Some changes may be too substantial to remain a normal revision.
If the Prediction Object, methodology, basis, assumptions, or scope changes fundamentally, a materially new prediction may need to be formed.
Therefore:
Material Predictive Reconstruction May Require New Prediction Formation
PMRS must not force continuity where the predictive object has materially changed.
Revision Scope
Every revision should identify what changed and what did not.
This supports:
comparability; traceability; performance evaluation; historical analysis.
A revised prediction should not appear as if it had always existed in its revised form.
Revision Rationale
A material revision should preserve:
trigger; changed information; affected prediction dimensions; revision rationale; prior state; new state; time of revision; governance basis.
Prediction Supersession
Supersession occurs when a newer governed prediction state replaces an earlier prediction as the current operational prediction.
The earlier prediction remains historically valid as a prior state.
Supersession ≠ Deletion
Superseded Prediction
A superseded prediction may remain relevant for:
historical reconstruction; predictive performance; calibration; audit; methodology evaluation; decision-history analysis.
It should no longer silently appear as the current prediction.
Prediction Withdrawal
Withdrawal occurs where the prediction should no longer be represented as an active predictive statement.
Potential reasons include:
invalidated Prediction Basis; major methodological defect; unrecoverable assumption failure; incorrect Prediction Object; material integrity problem; inability to support the existing claim.
Withdrawal ≠ Prediction History Deletion
Withdrawal ≠ Prediction Failure
A prediction may be withdrawn before its outcome becomes known.
Withdrawal concerns current predictive legitimacy.
It does not itself establish whether the eventual outcome would have occurred.
Temporary Review State
A prediction may enter:
Under Review
without immediately being revised or withdrawn.
This allows governance to respond to material uncertainty without premature conclusion.
Prediction State
PMRS may represent prediction states including:
Current
Current with Qualification
Monitoring Intensified
Review Required
Under Review
Revision Required
Revised
Superseded
Withdrawn
Historical
Unable to Determine
These are lifecycle governance states.
They are not outcome states.
State Transition Governance
Prediction-state transitions must remain explicit.
Examples:
Current → Review Required
Review Required → Current
Review Required → Revised
Revised → Current
Current → Superseded
Current → Withdrawn
Transitions should remain reconstructable.
Revision ≠ Historical Replacement
This is a foundational PMRS rule.
When prediction P1 becomes P2:
P1 must remain preserved.
The legitimate structure is:
P1 → Revision Trigger → Review → P2
not:
P2 replaces the historical existence of P1.
Prediction Version Continuity
Each material prediction state should remain linked within a Prediction Version Chain.
Example:
P1 → P2 → P3 → P4
Each version should retain:
creation state; revision basis; supersession status; historical position; relationship to later versions.
Historical Prediction Integrity
Historical predictive records must remain stable.
Once a prediction has been used, communicated, or relied upon, later revision must not retroactively modify the original prediction as if the revised information had been available earlier.
Later Knowledge ≠ Earlier Prediction State
Revision Timestamp
The time at which a prediction was revised must remain distinguishable from:
original formation time; Prediction Horizon; outcome time; publication time; use time.
This supports historical reconstruction.
Revision and Communication
Where a prediction is revised, downstream representations governed under PCRS may require update.
PMRS does not own communication representation.
It must, however, create a sufficient revision state for PCRS systems to identify that earlier representations may no longer be current.
Revision and Reliance
A material revision may affect Reliance Legitimacy.
RLCM may need to reassess:
reliance degree; safeguards; exposure; automation; continuation.
Prediction Revision ≠ Automatic Reliance Continuation
Revision and Use Boundary
PUBM may need to alter the Prediction Use Boundary after revision.
A previously permitted use may become:
conditional; restricted; suspended; prohibited.
Likewise, a prior restriction may later be relaxed.
Revision and Outcome
PMRS ends before final predictive performance governance.
Where sufficient governed outcome becomes available, POFPS takes ownership of comparison between prediction and outcome.
Prediction Revision ≠ Prediction Outcome Assessment
Outcome Knowledge and Late Revision
A prediction must not be revised after the outcome becomes known in a manner that makes the historical prediction appear more accurate than it was.
This is essential.
Outcome Knowledge Must Not Rewrite Historical Prediction
Any post-outcome correction must remain clearly classified as correction, annotation, or retrospective analysis rather than historical prediction revision.
Revision Gaming
Revision Gaming occurs when prediction changes are made strategically to improve later appearance of accuracy rather than because legitimate predictive conditions changed.
Examples:
narrowing Prediction Horizon shortly before outcome; removing failed conditions; retroactively altering probability; deleting earlier versions; changing Prediction Object after outcome becomes clearer.
PMRS must preserve version history to expose such behavior.
Prediction Lock Point
A prediction may require a Lock Point after which specific historical fields can no longer be modified as predictive claims.
The lock may occur at:
publication; first reliance; first use; defined lifecycle milestone; outcome availability.
The historical object remains immutable even where later annotations are added.
Revision Authority
PMRS does not determine institutional authority structures.
An implementation may define who or what is authorized to initiate, approve, or execute revision.
PMRS requires that material revision authority remain identifiable where relevant.
Methodological Revision Governance ≠ Institutional Authority Assignment
Human Revision
Humans may initiate or approve prediction revision.
Human involvement does not remove the requirement for:
material basis; traceability; version continuity; preserved history.
Human Revision ≠ Ungoverned Override
AI-Assisted Revision
AI may identify:
basis changes; assumption deterioration; contradictions; new information; probable revision triggers.
AI may assist revision.
But:
AI Revision Recommendation ≠ Governed Prediction Revision
The governed transition must still satisfy PMRS conditions.
Autonomous Revision
An autonomous system may be authorized to revise predictions automatically.
Where such authority exists, revision must still preserve:
trigger; basis; prior prediction; new prediction; version history; constraints; traceability.
Autonomous Revision ≠ Historical Rewrite
Prediction Monitoring Across Systems
Predictions may be consumed across many systems.
PMRS should support propagation of material prediction-state change to downstream systems.
Example:
Prediction Revised → Dashboard Updated → API Updated → Agent Use Reassessed
However:
State Propagation ≠ Prediction Recalculation
Stale Prediction Propagation
A major governance risk occurs where the source prediction has been revised but downstream systems continue using an older state.
PMRS should permit identification of stale downstream prediction instances.
Available Prediction Copy ≠ Current Prediction State
Prediction State Synchronization
Where multiple systems consume the same prediction, current-state synchronization may be required.
This does not require deletion of historical versions.
The goal is:
Current Operational State + Complete Historical Record
Prediction Monitoring and Model Change
A forecasting model may change after prediction formation.
A later model version does not automatically revise predictions produced by the earlier model.
Model Version Change ≠ Historical Prediction Revision
But it may affect future prediction generation or trigger review where continuing use depends on the model's ongoing validity.
Methodology Change
Where the underlying prediction methodology materially changes, the revised prediction must preserve the distinction between:
Prediction Revision Under Same Methodology
and:
Prediction Reformation Under Changed Methodology
This distinction is important for later performance comparison.
Prediction Monitoring and Operational Reality
ORA™ may supply relevant governed operational change information.
PMRS may consume such information to determine whether the prediction's conditions remain valid.
PMRS does not establish Operational Reality itself.
Operational Change Input ≠ PMRS Ownership of Operational Reality
Prediction Monitoring and Evidence
ADIT® may govern evidence used to support review or revision.
PMRS governs the lifecycle decision made in response to the governed predictive condition.
Revision Evidence ≠ Prediction Revision
Evidence supports revision.
It does not automatically determine it.
Prediction Monitoring and Validation
VALIDOS™ may validate relevant methods, inputs, claims, or revision processes.
PMRS remains responsible for the prediction monitoring and revision lifecycle.
Validation Result ≠ Revision Decision by Default
Prediction Monitoring and Integrity
INTEGROS® may govern broader integrity questions.
PMRS specifically governs integrity of prediction-state continuity during monitoring and revision.
Prediction Monitoring and Accountability
AGA™ may govern responsibility and accountability for prediction governance decisions.
PMRS does not assign legal or organizational accountability.
Historical Decision Preservation
Where decisions were made using an earlier prediction state, PMRS must preserve the relationship between:
Decision Time → Prediction Version Used
A later revision must not make it appear that the later prediction informed the earlier decision.
Later Prediction Version ≠ Historical Decision Input
Monitoring Failure
Monitoring Failure occurs where a prediction required continued monitoring but relevant change was not identified or acted upon.
This is distinct from prediction error.
Monitoring Failure ≠ Prediction Error
A prediction may originally have been legitimate while later becoming stale because governance failed to monitor changing conditions.
Revision Failure
Revision Failure occurs where material change justified revision but the prediction remained operationally unchanged.
Again:
Revision Failure ≠ Initial Prediction Failure
The lifecycle governance failure occurred after formation.
Over-Revision
Predictions can also be revised too frequently.
Excessive revision may:
destroy comparability; create noise; encourage hindsight adaptation; reduce stability; obscure methodology performance.
PMRS therefore protects against unnecessary revision.
More Frequent Revision ≠ Better Prediction Governance
Under-Revision
A prediction that remains unchanged despite material changes may become stale or misleading.
Prediction Stability ≠ Prediction Quality
The objective is governed revision proportional to material change.
Revision Frequency
Revision frequency should reflect:
environmental volatility; prediction horizon; update sensitivity; consequence; methodological design; use context.
There is no universal optimal revision frequency.
Review and Revision Independence
A prediction may be reviewed repeatedly and remain unchanged.
This is legitimate.
Repeated Review ≠ Repeated Revision
The presence of monitoring does not require visible prediction changes.
Prediction Lifecycle Continuity
PMRS connects the live prediction lifecycle between initial communication/use and later outcome assessment.
It ensures that prediction state remains governed through change.
The lifecycle may appear as:
Formed Prediction → Communicated Prediction → Used Prediction → Monitored Prediction → Revised Prediction → Superseded Prediction → Outcome-Comparable Prediction
Governance Requirements
PMRS requires that:
prediction monitoring begin from an identifiable Governed Prediction; monitoring scope be defined proportionately; material and non-material change be distinguished; Prediction Basis Decay remain identifiable; assumption changes remain identifiable; assumption failure not automatically be treated as prediction falsification; probability-relevant changes be escalated to appropriate governance; uncertainty changes remain visible; confidence changes remain visible where material; feasibility changes remain governed; collective-prediction structural changes remain visible; Predictor–Reality Proximity changes remain governable where material; prediction-use feedback be monitored where relevant; reflexive predictive conditions remain identifiable; intervening events be assessed for materiality; review triggers remain distinct from revision requirements; scheduled, event-triggered, and threshold-triggered review be supported where appropriate; prediction review permit preservation as a legitimate outcome; revision thresholds remain explicit; revisions remain proportionate to material change; technical corrections remain distinct from substantive prediction revisions; materially reconstructed predictions be distinguishable from ordinary revisions; revision scope remain identifiable; revision rationale remain traceable; supersession preserve previous prediction states; withdrawal preserve prediction history; prediction states remain explicit; state transitions remain reconstructable; revision never erase historical prediction; prediction version continuity be maintained; Later Knowledge not rewrite earlier prediction state; revision timing remain distinguishable from prediction timing and outcome timing; material revisions trigger relevant downstream communication and reliance reassessment; Outcome Knowledge not be used to retroactively improve prediction history; Revision Gaming be detectable; prediction Lock Points be supported where required; human, AI-assisted, and autonomous revision remain governed; downstream stale prediction use be identifiable; current-state synchronization preserve historical versions; methodology change remain distinguishable from ordinary prediction revision; PMRS remain distinct from Operational Reality governance; PMRS remain distinct from evidence governance; PMRS remain distinct from validation; PMRS remain distinct from accountability governance; historical decisions remain associated with the prediction version actually used; Monitoring Failure remain distinguishable from Prediction Error; Revision Failure remain distinguishable from Initial Prediction Failure; over-revision and under-revision remain governable; successful monitoring preserve a current governed prediction state suitable for continued lifecycle governance.
Governance Logic
PMRS applies the following Parent Standard-level logic:
Is the existing Governed Prediction still operating under materially comparable predictive conditions?
If yes:
→ preserve the current prediction state.
Has a relevant change occurred?
If no:
→ continue monitoring according to the applicable monitoring model.
If yes:
→ assess whether the change is material.
Is the change material to Prediction Basis, assumptions, probability, uncertainty, feasibility, Prediction Horizon, collective structure, operational conditions, or legitimate use?
If no:
→ preserve the current prediction and record the non-material change where appropriate.
If yes:
→ trigger Prediction Review.
Does review establish that the current prediction remains legitimate without substantive change?
If yes:
→ preserve the prediction or preserve it with qualification.
Does review establish that material predictive meaning must change?
If yes:
→ initiate governed Prediction Revision.
Does the revised prediction remain sufficiently continuous with the same Prediction Object and predictive structure?
If yes:
→ create a new prediction version and supersede the relevant prior operational state.
If no:
→ return the prediction to appropriate earlier PGA™ governance for new formation or material reconstruction.
Does the prediction remain incapable of legitimate current use?
If yes:
→ withdraw or restrict the prediction while preserving the historical record.
At every transition:
Previous Prediction State Must Remain Reconstructable.
Governance Outputs
PMRS may produce:
Prediction Monitoring State Prediction Condition Monitoring Record Material Change Indicator Prediction Basis Decay Indicator Assumption Change State Assumption Failure Indicator Probability Review Trigger Uncertainty Change Indicator Feasibility Change Indicator Collective Prediction Change Indicator Predictor–Reality Proximity Change Record Intervening Event Record Prediction Review Trigger Prediction Review Determination Prediction Preservation Determination Prediction Revision Requirement Governed Revised Prediction State Prediction Supersession Record Prediction Withdrawal Record Prediction Version Chain Prediction State Transition Record Revision Rationale Record Historical Prediction Preservation Record Stale Prediction Indicator Monitoring Failure Indicator Revision Failure Indicator Prediction Lifecycle Continuity Record
The principal output is:
Governed Current Prediction State with complete historical continuity.
This state proceeds through the Prediction Lifecycle until sufficient governed outcome becomes available for:
POFPS — Prediction Outcome & Performance Standard
Critical Distinctions
Valid Initial Prediction ≠ Valid Continuing Prediction
Historical Predictive Conditions ≠ Current Predictive Conditions
Prediction Monitoring ≠ Continuous Model Execution
Change ≠ Material Change by Default
Prediction Basis Decay ≠ Automatic Prediction Invalidity
Assumption Failure ≠ Automatic Prediction Falsification
Same Probability ≠ Same Predictive State
Same Collective Number ≠ Same Collective Structure
Change in Predictor–Reality Proximity ≠ Prediction Revision by Default
Prediction Observation ≠ Prediction Influence
Prediction Influence ≠ Outcome Control
Prediction Horizon End ≠ Outcome Determination
Review Trigger ≠ Revision Requirement
Review ≠ Revision
Technical Correction ≠ Predictive Change by Default
Material Predictive Reconstruction ≠ Ordinary Revision
Supersession ≠ Deletion
Withdrawal ≠ Prediction History Deletion
Withdrawal ≠ Prediction Failure
Revision ≠ Historical Replacement
Later Knowledge ≠ Earlier Prediction State
Prediction Revision ≠ Automatic Reliance Continuation
Prediction Revision ≠ Prediction Outcome Assessment
Outcome Knowledge Must Not Rewrite Historical Prediction
Human Revision ≠ Ungoverned Override
AI Revision Recommendation ≠ Governed Prediction Revision
Autonomous Revision ≠ Historical Rewrite
Available Prediction Copy ≠ Current Prediction State
Model Version Change ≠ Historical Prediction Revision
Revision Evidence ≠ Prediction Revision
Validation Result ≠ Revision Decision by Default
Later Prediction Version ≠ Historical Decision Input
Monitoring Failure ≠ Prediction Error
Revision Failure ≠ Initial Prediction Failure
More Frequent Revision ≠ Better Prediction Governance
Prediction Stability ≠ Prediction Quality
Repeated Review ≠ Repeated Revision
Canonical Boundary
PMRS governs monitoring and lifecycle revision of an existing Governed Prediction after formation, including Prediction Basis Decay, assumption change, material condition change, probability- and uncertainty-relevant change, feasibility change, collective-prediction change, Predictor–Reality Proximity change, prediction-use feedback, intervening events, review triggers, Prediction Review, preservation, revision, supersession, withdrawal, state transitions, version continuity, Lock Points, historical preservation, stale prediction detection, and continuing predictive legitimacy. It does not form the original Prediction Object, define the original Prediction Methodology, establish the original Prediction Basis or assumptions, calculate probability, determine Prediction Feasibility, establish Collective Prediction legitimacy, govern final prediction communication or use, determine Operational Reality, determine whether the predicted outcome occurred, or assess predictive performance.
PCRS governs how a prediction is represented, interpreted, relied upon, and used.
PMRS governs what happens when the predictive conditions change after that prediction enters operational life.
POFPS subsequently governs comparison between the governed prediction and sufficiently governed outcome.
Canonical Closing Statement
A prediction does not remain governed merely because it was governed once.
PMRS preserves the integrity of the prediction through changing information, assumptions, conditions, use, and operational reality by determining when the prediction should remain unchanged, when it must be reviewed, when it must be revised, and when it must cease to operate as the current predictive state.
A revised prediction must not erase the prediction that preceded it.
The future may change the prediction. It must never rewrite the history of what was predicted.
Mahlad
Prediction Monitoring & Revision Standard
Architecture:
PGA™ — Prediction Governance Architecture Category: Governance & Enforcement Subcategory: Prediction Governance Canonical Definition: The Prediction Monitoring & Revision Standard governs how a Governed Prediction is monitored after formation, how material changes in its Prediction Basis, assumptions, uncertainty, feasibility, collective structure, operational conditions, or use context are identified, and when the prediction must be preserved, reviewed, revised, superseded, withdrawn, or returned for further governance while maintaining complete historical continuity. Governed Space: Prediction Monitoring & Revision Core Question: When changing information or conditions materially affect a prediction, when must it be preserved, reviewed, revised, superseded, or withdrawn? Valid Initial Prediction ≠ Valid Continuing Prediction A revised prediction must not erase the prediction that preceded it. → View Standard
About standard
Governed Links
About Prediction Monitoring & Revision Standard (PMRS)
Prediction Change Monitoring Module (PCHM)
Review Trigger Module (RTM)
Prediction Revision Module (PRVM)
Prediction Supersession Module (PSSM)
Prediction Withdrawal Module (PWM)