PRVM — Prediction Revision 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 Revision
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
PRVM governs the controlled revision of an existing Governed Prediction when formal review establishes that its current predictive state can no longer remain unchanged without materially misrepresenting the prediction's present support, conditions, probability, uncertainty, feasibility, scope, horizon, or qualification.
It governs the transition:
Governed Prediction Review Determination + Existing Prediction State + Revision Basis → Governed Revised Prediction State
PRVM determines what may legitimately change, what must remain preserved, how the revised state relates to the prior state, and when proposed change exceeds ordinary revision and requires re-entry into earlier PGA™ governance.
PRVM does not erase, overwrite, or retrospectively replace the prediction that existed before revision.
Canonical Definition
The Prediction Revision Module (PRVM) defines the operational governance requirements by which an existing Governed Prediction may be substantively modified after formal review establishes a legitimate Revision Basis.
PRVM governs revision scope, revision proportionality, revision basis, affected predictive dimensions, preserved dimensions, probability and uncertainty changes, assumption changes, Prediction Basis changes, feasibility changes, horizon changes, boundary changes, qualification changes, revision timing, version creation, revision rationale, historical continuity, and downstream revision signaling.
Its foundational rule is:
A revised prediction is a new governed prediction state — not a rewritten historical prediction.
The legitimate structure is:
P1 → Revision Basis → Governed Revision → P2
not:
P1 silently becomes P2.
Operational Role
PRVM is the third operational module within PMRS.
Its focused operational question is:
When formal review establishes that the current prediction can no longer legitimately remain unchanged, what may be revised, how far may the revision extend, and how must the new predictive state remain connected to the prediction that preceded it?
PRVM receives a governed review determination arising from PMRS Review Trigger governance.
It may produce:
preserved prediction; qualified prediction; partially revised prediction; materially revised prediction; reformation requirement; new governed prediction version.
PRVM does not determine final supersession or withdrawal.
Those lifecycle transitions belong respectively to PSSM and PWM.
Internal Module Flow
Prediction Change Monitoring → Review Trigger → Prediction Revision → Prediction Supersession → Prediction Withdrawal
Current Position:
Prediction Revision
1. Revision Basis
No substantive prediction revision should occur without an identifiable Revision Basis.
A Revision Basis may include:
material new information; Prediction Basis deterioration; assumption change; assumption failure; probability-relevant change; uncertainty change; feasibility change; structural environmental change; collective prediction change; operational-condition change; corrected evidence; methodological correction; formal review finding.
Desire to Change Prediction ≠ Revision Basis
2. Revision Eligibility
A prediction becomes Revision-Eligible where formal review establishes that maintaining its current state would no longer adequately represent its governed predictive condition.
Revision eligibility does not require that every part of the prediction change.
Revision Eligibility ≠ Full Prediction Reconstruction
3. Prediction Preservation Outcome
Formal review may establish that no substantive revision is required.
The legitimate result is then:
Prediction Preserved
or:
Prediction Preserved with Qualification
PRVM must support non-revision as a legitimate outcome.
Review ≠ Mandatory Revision
4. Revision Necessity
Revision is necessary where material predictive meaning can no longer be preserved through qualification alone.
The question is not:
Can the prediction be changed?
The question is:
Must the predictive state change to remain methodologically legitimate?
5. Revision Proportionality
Revision should be proportionate to the material change that justified it.
A local change should not automatically rewrite the entire prediction.
Limited Material Change → Limited Revision where sufficient
Foundational Material Change → Broader Governance Reassessment
6. Revision Scope
Revision Scope identifies which predictive dimensions require change.
Possible scopes include:
Qualification-Only Revision
Component Revision
Multi-Component Revision
Material Prediction Revision
Prediction Reformation Required
7. Qualification-Only Revision
Some changes may be addressed by adding or modifying a qualification without changing the core prediction.
Example:
70% probability
remains 70%, but:
subject to materially increased uncertainty
must now accompany the prediction.
This remains a governed predictive change and should be versioned where material.
8. Component Revision
A Component Revision changes one primary dimension while preserving the remainder.
Examples:
probability; uncertainty; range; feasibility qualification; assumption state; Prediction Horizon; Predictive Boundary.
9. Multi-Component Revision
Multiple predictive dimensions may change together.
Example:
Probability + uncertainty + assumption qualification
may require simultaneous revision.
The relationship among those changes should remain traceable.
10. Material Prediction Revision
A Material Prediction Revision changes substantive predictive meaning.
Examples:
75% → 42%
Feasible → Infeasible
12 months → 24 months
Region A → Region A only under Condition X
Such revisions require explicit historical preservation.
11. Prediction Reformation
A proposed change may exceed legitimate revision.
Prediction Reformation may be required where there is fundamental change to:
Prediction Object; Predictive Purpose; methodology; fundamental Prediction Basis; core assumption structure; Predictive Boundary; outcome definition.
Revision Must Not Be Used to Hide a New Prediction
12. Same Prediction Test
PRVM should determine whether the revised object remains sufficiently continuous with the prior prediction to remain within the same prediction lineage.
Relevant questions include:
Is the Prediction Object still materially the same? Is the predicted outcome still materially the same? Is the Predictive Purpose still compatible? Is the methodology sufficiently continuous? Is the Prediction Horizon still meaningfully comparable? Is the Predictive Boundary still recognizably continuous?
If not:
New Prediction Formation May Be Required
13. Prediction Object Change
A material change in what is being predicted may exceed revision.
Example:
Will Company X receive regulatory approval?
cannot silently become:
Will Company X successfully commercialize Product Y?
These are different Prediction Objects.
Related Prediction Object ≠ Same Prediction Object
14. Predictive Purpose Change
A prediction originally created for one purpose may later be proposed for a fundamentally different purpose.
Purpose expansion belongs partly to PCRS/PUBM.
Where the predictive purpose itself is materially reconstructed, earlier PGA governance may need to be revisited.
15. Methodology Change
A material methodology change may create a new predictive state whose comparability with the prior prediction is limited.
PRVM should distinguish:
Revision Under Same Methodology
from:
Revision Under Modified Methodology
and:
Prediction Reformation Under New Methodology
PMES retains ownership of Prediction Methodology.
16. Prediction Basis Revision
New information may require the Prediction Basis supporting the revised prediction to change.
PBAS owns Prediction Basis governance.
PRVM records that the revised prediction depends upon a changed basis and preserves the relationship between:
Prior Prediction Basis → Revision Basis → Updated Prediction Basis
17. Prediction Basis Addition
New information may be added to the basis without invalidating earlier information.
The revision should identify:
added information; relevance; timing; resulting predictive effect.
18. Prediction Basis Removal
A source or information element may need to be removed because it becomes:
invalid; irrelevant; duplicated; corrupted; superseded.
Removal should remain historically visible.
Removed Basis Element ≠ Basis Element That Never Existed
19. Prediction Basis Correction
Where a material basis element was incorrect, correction should preserve:
original basis state; identified error; correction time; corrected state; resulting prediction effect.
This prevents retrospective reconstruction.
20. Assumption Revision
A material Predictive Assumption may be revised where current conditions no longer support the earlier assumption.
PAS governs the assumption.
PRVM governs the resulting prediction-state transition.
21. Assumption Removal
A prior assumption may become unnecessary.
Its removal should remain historically visible where it materially affected earlier predictive meaning.
Removed Assumption ≠ Assumption That Never Existed
22. New Assumption
Revision may introduce a new material assumption.
The revised prediction should not conceal that its predictive support now depends upon a condition absent from the prior version.
23. Assumption Failure and Revision
Assumption Failure may require:
probability revision; feasibility revision; qualification; scope reduction; withdrawal.
It does not automatically determine which response is appropriate.
24. Probability Revision
A probability may be revised where PPUS-governed reconsideration supports a new representation.
Example:
P1: 68%
→ material new information →
P2: 44%
PRVM governs the transition and historical relationship.
It does not independently calculate the 44%.
25. Probability Increase
An increased probability must remain attributable to a legitimate Revision Basis.
Higher Probability ≠ Improved Prediction by Default
The prediction has changed.
Its eventual quality remains unknown.
26. Probability Decrease
A lower probability is not necessarily evidence that the earlier prediction was defective.
New conditions may legitimately justify the decrease.
Probability Revision ≠ Admission of Earlier Prediction Error
27. Probability Stability with Other Revision
A prediction may retain the same probability while materially changing elsewhere.
Example:
70% → 70%
while uncertainty widens or assumptions change.
Therefore:
Same Probability ≠ No Prediction Revision
28. Uncertainty Revision
A revised prediction should preserve material changes in uncertainty.
Uncertainty may:
increase; decrease; change structure; become asymmetric; become less determinable.
Uncertainty revision must not be hidden behind a stable point estimate.
29. Confidence Revision
Confidence-related change should remain distinct from probability change.
Confidence Revision ≠ Probability Revision
Both may occur independently.
30. Range Revision
Where prediction ranges change, PRVM should preserve:
prior range; revised range; rationale; time; conditions.
Wider Range ≠ Weaker Prediction by Definition
Interpretation depends upon context.
31. Conditionality Revision
A revised prediction may introduce, remove, or alter conditions.
Example:
70% probability
becomes:
70% probability provided regulatory approval occurs before Date X.
That is a substantive predictive revision even if the numerical probability remains unchanged.
32. Feasibility Revision
A change in PFES-governed feasibility may require revision of the prediction's qualifications or scope.
Possible transitions include:
Feasible → Conditionally Feasible
Conditionally Feasible → Infeasible
Infeasible → Feasible
PRVM preserves the resulting prediction-state change.
33. Prediction Horizon Revision
A Prediction Horizon may require revision where new conditions materially alter expected timing.
Example:
Expected within 12 months → Expected within 24 months
This is substantive.
The original horizon must remain preserved.
Horizon Revision ≠ Original Horizon Never Existed
34. Predictive Boundary Revision
The Predictive Boundary may narrow or expand.
Examples:
Europe → Germany
Product Family → Product X
All Customers → Enterprise Customers
Boundary change must remain explicit.
35. Boundary Narrowing
Boundary narrowing may preserve legitimacy where the original scope becomes too broad.
It should not be represented as though the narrower boundary had always applied.
36. Boundary Expansion
Boundary expansion may exceed ordinary revision because it introduces predictive claims not previously governed.
Boundary Expansion ≠ Automatic Revision Eligibility
Earlier PGA governance may need to be repeated.
37. Collective Prediction Revision
A collective prediction may require revision because:
participants change; independence changes; aggregation changes; concentration changes; source diversity changes.
CPGS governs the collective predictive structure.
PRVM governs the resulting lifecycle revision state.
38. Collective Probability Revision
A market or collective probability may change naturally over time.
Not every market movement constitutes formal prediction revision.
PRVM applies where the governed Collective Prediction itself is being revised as a lifecycle object.
Market Movement ≠ Governed Prediction Revision by Default
39. Predictor–Reality Proximity Revision Context
A change in Predictor–Reality Proximity may affect the qualifications surrounding a revised prediction.
It does not itself establish the revised predictive result.
Changed Information Position ≠ Automatic Predictive Revision
40. Operational Reality Change
A change in Operational Reality may justify revision.
ORA™ may establish the relevant operational state.
PRVM consumes that governed condition.
It does not own Operational Reality.
41. Intervening Event Revision
An Intervening Event may justify revision where formal review establishes material predictive effect.
The revision should preserve:
event; timing; predictive dimensions affected; resulting changes.
42. Reflexive Revision
Where prediction use materially changes Operational Reality, the prediction may require revision.
Example:
Failure Prediction → Preventive Intervention → Changed Failure Conditions
The revision must not rewrite the fact that the earlier prediction preceded the intervention.
43. Revision Timing
Revision timing must remain explicit.
Relevant times may include:
original prediction formation; Change Signal occurrence; Change Signal detection; Review Trigger; review completion; revision decision; revised prediction publication.
These times must not be collapsed.
44. Effective Revision Time
The Effective Revision Time identifies when the revised prediction becomes the current governed predictive state, subject to supersession governance.
It may differ from the time the change originally occurred.
Change Time ≠ Revision Effective Time
45. Revision Latency
Revision Latency is the interval between material change recognition and effective revision.
Excessive latency may allow stale predictions to remain active.
Very low latency may also create premature revision.
Fast Revision ≠ Good Revision by Default
46. Revision Rationale
Every material revision should have an explicit rationale.
The rationale should answer:
what changed; why it matters; what was reviewed; what is being revised; what remains unchanged; why the revised state is legitimate.
47. Revision Evidence Reference
Where evidence supports revision, relevant evidence should remain referenceable.
ADIT® owns evidence governance.
PRVM preserves the relationship:
Governed Evidence Reference → Revision Basis
Evidence ≠ Revision Decision
48. Revision Validation
Where relevant, revision may require validation.
VALIDOS™ may govern applicable validation processes.
Validated Input ≠ Automatically Valid Revision
Revision legitimacy depends on the complete governed transition.
49. Revision Authority
The actor or system initiating, approving, or executing material revision should remain identifiable where relevant.
Possible actors include:
human analyst; forecasting team; institutional authority; AI system; autonomous agent.
PRVM does not create organizational authority.
50. Revision Initiator
The Revision Initiator identifies who or what initiated the revision process.
Initiation is distinct from approval or execution.
51. Revision Approval
Where an implementation requires approval, the approval state should remain explicit.
Revision Initiation ≠ Revision Approval
52. Human Revision
Human revision remains subject to:
Revision Basis; proportionality; traceability; historical preservation; version continuity.
Human Judgment ≠ Permission to Rewrite History
53. AI-Assisted Revision
AI may:
identify revision candidates; summarize new information; compare prediction versions; propose probability changes; identify assumption deterioration.
AI assistance does not remove governance requirements.
AI-Generated Revision Proposal ≠ Governed Revised Prediction
54. Autonomous Revision
An autonomous system may revise predictions where properly authorized.
It must still preserve:
trigger; Revision Basis; prior state; revised state; rationale; timestamp; version relationship.
Autonomous Revision ≠ Autonomous Historical Editing
55. Revision State
PRVM may classify revision as:
No Revision Required
Qualification Revision Required
Component Revision Required
Multi-Component Revision Required
Material Revision Required
Prediction Reformation Required
Revision Unable to Determine
These are revision governance states.
56. Revised Prediction State
The revised prediction should identify:
revised predictive claim; revised probability where applicable; revised uncertainty; revised assumptions; revised basis references; revised feasibility; revised horizon; revised boundary; qualifications; revision relationship.
Only dimensions that actually changed should be represented as changed.
57. Preserved Dimensions
PRVM should explicitly identify material dimensions that remain unchanged.
This prevents ambiguity about whether revision affected the entire prediction.
Example:
Changed: Probability, uncertainty.
Preserved: Prediction Object, methodology, horizon, boundary.
58. Revision Delta
The Revision Delta represents the difference between prior and revised predictive states.
Canonical relationship:
Prediction State P1 + Governed Revision Delta → Prediction State P2
The Revision Delta should remain reconstructable.
59. Revision Version
A material revision creates a distinct prediction version.
Example:
PGA-PRED-00421-v1
→
PGA-PRED-00421-v2
The exact implementation may differ.
The methodological requirement is stable version distinction.
60. Version Lineage
Each revised state should remain linked to its predecessor.
Example:
P1 → P2 → P3
The lineage should not become:
P3 only
61. Historical Prediction Preservation
The prior prediction must remain available as historical state.
It should preserve what was legitimately known and predicted at that time.
Current Prediction State ≠ Complete Prediction History
62. Immutable Historical Fields
Implementations may define fields that become historically immutable after a Lock Point.
Examples may include:
original probability; original horizon; original prediction text; original timestamp; original Prediction Object.
Later corrections should be added as new governed records.
63. Prediction Lock Point
A Lock Point may occur at:
publication; communication; first reliance; first operational use; predefined lifecycle milestone.
After the Lock Point:
Revision Creates New State
rather than:
Revision Overwrites Old State
64. Outcome Knowledge Boundary
Once outcome information becomes available, historical predictive claims require special protection.
The prediction must not be retrospectively changed to match known reality.
Outcome Knowledge ≠ Revision Basis for Historical Prediction
65. Post-Outcome Correction
A genuine technical error discovered after outcome may be corrected through annotation or correction record.
It must not be represented as though the corrected state was the prediction originally made.
Correction After Outcome ≠ Historical Prediction Revision
66. Revision Gaming
PRVM must prevent revision processes from being used to artificially improve predictive appearance.
Potential Revision Gaming includes:
late probability adjustment; horizon manipulation; outcome-definition change; boundary narrowing after adverse information; deleting failed assumptions; silent version replacement.
67. Hindsight Revision
Hindsight Revision occurs where later knowledge is incorporated into the historical prediction as if it had existed earlier.
Later Knowledge ≠ Earlier Predictive Knowledge
Hindsight Revision is incompatible with governed prediction history.
68. Revision Frequency
Predictions may be revised:
rarely; periodically; frequently; dynamically.
Frequency should reflect legitimate predictive need.
More Revision ≠ Better Prediction Governance
69. Over-Revision
Over-Revision may occur where predictions are repeatedly changed in response to low-materiality noise.
It may:
destroy stability; obscure predictive skill; reduce comparability; encourage hindsight adaptation.
70. Under-Revision
Under-Revision occurs where materially changed conditions do not produce necessary predictive revision.
It may create stale prediction persistence.
Prediction Stability ≠ Continuing Prediction Legitimacy
71. Revision Stability
A revised prediction should remain stable until new material conditions justify another review.
Revision is not an invitation to continuous uncontrolled adjustment.
72. Revision Propagation
Once a revised state becomes current, downstream systems may need to receive the revision.
Potential destinations include:
dashboards; APIs; AI agents; planning systems; operational systems; human decision-makers.
PCRS governs downstream representation, reliance, and use.
PRVM provides the governed revision state.
73. Stale Version Detection
Where downstream systems continue using an earlier prediction after revision, the stale version should remain detectable.
Available Historical Version ≠ Current Prediction Version
74. Revision Notification
A material revision may generate a notification or machine-readable state change.
Notification should identify:
prediction; prior version; new version; revision time; material changes; applicable qualification.
Notification itself does not govern downstream reliance.
75. Revision and Reliance Reassessment
Material revision may require RLCM reassessment.
Prediction Revision ≠ Automatic Reliance Continuation
A new probability, uncertainty state, horizon, or feasibility condition may change legitimate reliance.
76. Revision and Use Reassessment
PUBM may need to reconsider permitted use.
A prediction previously permitted for autonomous use may become restricted after material revision.
Likewise, a restriction may later become unnecessary.
77. Revision and Performance Assessment
POFPS should later assess predictions using the prediction states that actually existed at relevant times.
A later revised version must not replace an earlier version in historical performance assessment.
78. Revision and Decision History
Historical decisions should remain associated with the prediction version actually available at Decision Time.
Later Revised Prediction ≠ Earlier Decision Input
79. Revision Traceability
A material revision should permit reconstruction of:
Prior Prediction → Review Determination → Revision Basis → Revision Scope → Revision Delta → Revised Prediction → Version Relationship
This is the minimum conceptual chain required for governed revision.
80. Revision History
Revision History should preserve:
all material versions; revision timestamps; Revision Bases; Revision Deltas; preserved dimensions; revised dimensions; revision initiators; applicable approval; supersession relationship.
81. Governance Requirements
A governed PRVM implementation should be able to:
1. identify the prediction under revision; 2. identify the current prediction version; 3. consume the applicable Review Determination; 4. identify Revision Basis; 5. establish Revision Eligibility; 6. support Prediction Preservation as a legitimate outcome; 7. determine Revision Necessity; 8. preserve Revision Proportionality; 9. determine Revision Scope; 10. support Qualification-Only Revision; 11. support Component Revision; 12. support Multi-Component Revision; 13. support Material Prediction Revision; 14. identify when Prediction Reformation is required; 15. apply a Same Prediction Test; 16. identify material Prediction Object change; 17. identify Predictive Purpose change; 18. distinguish methodology continuity from methodology replacement; 19. identify Prediction Basis revision; 20. preserve Prediction Basis additions; 21. preserve Prediction Basis removals; 22. preserve Prediction Basis corrections; 23. identify assumption revisions; 24. preserve assumption removals; 25. preserve new material assumptions; 26. govern Assumption Failure effects; 27. govern probability revision; 28. preserve Probability Revision Basis; 29. distinguish probability increase from predictive improvement; 30. distinguish probability decrease from prior predictive error;
31. recognize material revision despite stable probability; 32. govern uncertainty revision; 33. govern confidence revision; 34. govern range revision; 35. govern conditionality revision; 36. govern feasibility-related revision; 37. govern Prediction Horizon revision; 38. govern Predictive Boundary revision; 39. govern Boundary Narrowing; 40. identify when Boundary Expansion requires earlier-stage governance; 41. govern collective prediction revision state; 42. distinguish market movement from formal prediction revision; 43. preserve Predictor–Reality Proximity revision context; 44. consume relevant Operational Reality change; 45. preserve Intervening Event Revision Basis; 46. support Reflexive Revision; 47. preserve Revision Timing; 48. establish Effective Revision Time; 49. identify Revision Latency; 50. preserve Revision Rationale; 51. preserve Revision Evidence References; 52. support revision validation where required; 53. identify Revision Authority where relevant; 54. identify Revision Initiator; 55. identify Revision Approval where required; 56. govern human revision; 57. govern AI-assisted revision; 58. govern autonomous revision; 59. determine Revision State; 60. produce Revised Prediction State; 61. identify Preserved Dimensions; 62. identify Revision Delta; 63. create distinct Revision Version; 64. preserve Version Lineage; 65. preserve Historical Prediction State; 66. support immutable historical fields; 67. support Prediction Lock Points; 68. enforce Outcome Knowledge Boundary; 69. distinguish post-outcome correction from historical revision; 70. prevent Revision Gaming; 71. prevent Hindsight Revision; 72. govern Revision Frequency; 73. identify Over-Revision; 74. identify Under-Revision; 75. preserve Revision Stability; 76. support Revision Propagation; 77. support Stale Version Detection; 78. support Revision Notification; 79. trigger downstream reliance reassessment where appropriate; 80. trigger downstream use reassessment where appropriate; 81. preserve prediction versions for later performance assessment; 82. preserve prediction versions used in historical decisions; 83. preserve Revision Traceability; 84. preserve Revision History; 85. prevent revision from erasing prior prediction state; 86. prevent revision from silently creating a materially different Prediction Object; 87. distinguish Revision from Supersession; 88. distinguish Revision from Withdrawal; 89. produce a Governed Revised Prediction State.
82. Governance Logic
PRVM applies the following operational logic:
Has formal review established that the current prediction may remain unchanged?
If yes:
→ No Revision Required / Prediction Preserved
If the prediction remains legitimate but requires additional qualification:
→ Qualification Revision Required
If substantive change is necessary:
→ identify Revision Basis.
Which predictive dimensions are materially affected?
→ establish Revision Scope.
Can the required change remain within the same Prediction Object, Predictive Purpose, methodology lineage, horizon structure, and Predictive Boundary?
If yes:
→ proceed with governed revision.
If no:
→ classify Prediction Reformation Required and return to the appropriate earlier PGA™ governance stage.
Is one predictive dimension affected?
→ Component Revision.
Are multiple dimensions affected?
→ Multi-Component Revision.
Does the change materially alter predictive meaning?
→ Material Prediction Revision.
For every substantive revision:
→ preserve prior state;
→ identify Revision Delta;
→ create new prediction version;
→ establish Effective Revision Time;
→ preserve Version Lineage;
→ produce Revised Prediction State.
At no point may the revised state overwrite the historical prediction.
P1 → Revision → P2
never:
P1 = retrospectively replaced by P2
83. Minimum Implementation Framework
A. Prediction Reference
Identifier of the prediction undergoing revision.
B. Prior Prediction Version
The governed prediction state existing before revision.
C. Review Determination Reference
The formal review state supporting revision.
D. Revision Basis
Material reason supporting predictive change.
E. Revision Scope
Qualification, component, multi-component, material, or reformation.
F. Revised Dimensions
Predictive elements materially changed.
G. Preserved Dimensions
Predictive elements remaining unchanged.
H. Prior Predictive State
Relevant state before revision.
I. Revised Predictive State
New governed predictive state.
J. Revision Delta
Structured difference between prior and revised states.
K. Revision Rationale
Reason the revision is methodologically justified.
L. Effective Revision Time
Time at which the revised state becomes effective.
M. Revision State
Current PRVM classification.
N. Version Relationship
Relationship between prior and revised prediction versions.
O. Revision History
Historical record preserving the complete revision lineage.
84. Governance Outputs
PRVM may produce:
Governed Revised Prediction State Prediction Preservation Determination Prediction Preservation with Qualification Revision Eligibility Determination Revision Basis Record Revision Scope Determination Qualification Revision Component Revision Multi-Component Revision Material Prediction Revision Prediction Reformation Requirement Probability Revision Record Uncertainty Revision Record Assumption Revision Record Prediction Basis Revision Reference Feasibility Revision Record Prediction Horizon Revision Record Predictive Boundary Revision Record Revision Delta Revision Rationale Record Effective Revision Time Prediction Version Version Lineage Historical Prediction Preservation Record Stale Version Indicator Downstream Reassessment Signal Revision History
The primary output is:
Governed Revised Prediction State
Where the revised state replaces the earlier state as the current operational prediction, the lifecycle proceeds to:
PSSM — Prediction Supersession Module
85. Critical Distinctions
Desire to Change Prediction ≠ Revision Basis
Revision Eligibility ≠ Full Prediction Reconstruction
Review ≠ Mandatory Revision
Qualification Revision ≠ Full Prediction Revision
Revision Must Not Be Used to Hide a New Prediction
Related Prediction Object ≠ Same Prediction Object
Removed Basis Element ≠ Basis Element That Never Existed
Removed Assumption ≠ Assumption That Never Existed
Probability Revision ≠ Admission of Earlier Prediction Error
Higher Probability ≠ Improved Prediction by Default
Same Probability ≠ No Prediction Revision
Confidence Revision ≠ Probability Revision
Horizon Revision ≠ Original Horizon Never Existed
Boundary Expansion ≠ Automatic Revision Eligibility
Market Movement ≠ Governed Prediction Revision by Default
Changed Information Position ≠ Automatic Predictive Revision
Change Time ≠ Revision Effective Time
Fast Revision ≠ Good Revision by Default
Evidence ≠ Revision Decision
Validated Input ≠ Automatically Valid Revision
Revision Initiation ≠ Revision Approval
Human Judgment ≠ Permission to Rewrite History
AI-Generated Revision Proposal ≠ Governed Revised Prediction
Autonomous Revision ≠ Autonomous Historical Editing
Current Prediction State ≠ Complete Prediction History
Outcome Knowledge ≠ Revision Basis for Historical Prediction
Correction After Outcome ≠ Historical Prediction Revision
Later Knowledge ≠ Earlier Predictive Knowledge
More Revision ≠ Better Prediction Governance
Prediction Stability ≠ Continuing Prediction Legitimacy
Available Historical Version ≠ Current Prediction Version
Prediction Revision ≠ Automatic Reliance Continuation
Later Revised Prediction ≠ Earlier Decision Input
Revision ≠ Supersession
Revision ≠ Withdrawal
86. Enterprise Application
PRVM can govern prediction revision across:
enterprise forecasting; strategic planning; financial forecasting; investment analysis; insurance; manufacturing; supply chains; predictive maintenance; energy; infrastructure;
logistics; public-sector forecasting; emergency planning; AI forecasting systems; autonomous agents; model ensembles; prediction markets; Human-AI forecasting; multi-agent prediction systems.
Its function remains constant:
allow predictions to change when the world legitimately requires them to change without allowing that change to rewrite what was previously predicted.
87. Use Case 1 — Regulatory Approval Prediction Falls from 74% to 38%
A Governed Prediction states:
P1 — 74% probability that a new industrial technology will receive regulatory approval within twelve months.
The prediction is based partly upon:
successful preliminary review; expected completion of safety testing; stable regulatory requirements.
Four months later:
a new safety requirement is introduced; additional testing becomes mandatory; approval timing becomes materially less certain.
PCHM detects the changes.
RTM establishes a formal Review Trigger.
Review determines that the existing 74% probability and twelve-month timing can no longer remain unchanged.
PPUS-governed reconsideration establishes:
38% probability within the original twelve-month horizon
with materially increased uncertainty.
PRVM creates:
P1 — 74%
→ Regulatory Change
→ Formal Review
→ P2 — 38%
The original prediction remains preserved.
PRVM records:
Revised Dimensions: Probability · Uncertainty · Regulatory Assumption
Preserved Dimensions: Prediction Object · Predictive Purpose · Core Methodology · Original Horizon
The critical governance distinction is:
P2 does not replace the historical existence of P1.
Both remain part of the Prediction Version Chain.
88. Use Case 2 — A “Revision” Attempts to Change What Was Actually Predicted
A technology forecasting organization publishes:
P1 — 65% probability that fully autonomous delivery vehicles will achieve commercial deployment in Country A by 2028.
Two years later, deployment remains significantly delayed.
The organization proposes revising the prediction to:
P2 — 65% probability that semi-autonomous delivery systems will achieve limited pilot deployment somewhere in Europe by 2030.
The numerical probability remains 65%.
But the proposed revision changes:
fully autonomous → semi-autonomous ; commercial deployment → limited pilot deployment ; Country A → Europe ; 2028 → 2030 .
The critical governance question is:
Is this still a revision of the same prediction?
PRVM determines that the proposed change materially alters:
Prediction Object; outcome definition; geographic boundary; deployment threshold; Prediction Horizon.
The correct state is therefore:
Prediction Reformation Required
The organization cannot preserve the appearance of predictive continuity by calling the new statement a revision.
Revision Must Not Be Used to Hide a New Prediction
Related Prediction Object ≠ Same Prediction Object
P1 remains historically preserved.
A new prediction must enter the appropriate earlier PGA™ governance stages.
89. Canonical Boundary
PRVM governs substantive revision of an existing Governed Prediction after formal review establishes a legitimate Revision Basis. It governs Revision Eligibility, Prediction Preservation, Revision Necessity, proportionality, scope, qualification changes, probability and uncertainty revision, assumption revision, Prediction Basis revision relationships, feasibility-related revision, Prediction Horizon and Predictive Boundary revision, collective prediction revision state, Operational Reality-related revision, Revision Timing, Revision Delta, Revision Rationale, Effective Revision Time, human, AI-assisted and autonomous revision, Prediction Lock Points, version creation, Version Lineage, historical prediction preservation, stale-version identification, and downstream reassessment signaling. It does not create the original prediction, independently recalculate probability, establish Operational Reality, determine final Prediction Supersession, determine Prediction Withdrawal, determine the eventual outcome, 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
90. Canonical Closing Statement
Prediction revision is legitimate only when new conditions justify changing what can responsibly be predicted now — never when later knowledge creates an opportunity to improve what appears to have been predicted before.
PRVM allows predictive knowledge to evolve while preserving the exact historical states from which that evolution occurred.
The prediction may change. The record of what was predicted must not.