PLDIM — Prediction-Led Decision Interpretation Module
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: PGA™ — Prediction Governance Architecture
Parent Standard:
PCRS — Prediction Communication & Reliance Standard
Operational Layer: Prediction Communication & Reliance
Category:
Governance & Enforcement
Subcategory:
Prediction Governance
Type: Parent Standard Module
Governed Space:
Prediction-Led Decision Interpretation
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
PLDIM governs the interpretation of a Governed Predictive Representation when its predictive meaning is translated into decision-relevant meaning.
It governs the transition from:
What does the prediction establish?
to:
What does the prediction mean for a contemplated decision?
PLDIM ensures that a prediction is not silently transformed into a recommendation, instruction, requirement, prohibition, authorization, or decision merely because it is decision-relevant.
Canonical Definition
The Prediction-Led Decision Interpretation Module (PLDIM) defines the operational governance requirements by which a Governed Predictive Representation is interpreted in relation to a contemplated decision without allowing the prediction to acquire decision authority, normative force, operational meaning, or prescriptive significance beyond what the underlying predictive structure legitimately supports.
PLDIM governs the relationship between:
Governed Predictive Representation → Decision Context → Prediction-Led Decision Interpretation
It establishes how predictive information may inform a decision while preserving the distinction between what is predicted and what should be done.
A prediction may inform a decision. It does not become the decision merely because it influences one.
Operational Role
PLDIM is the second operational module within PCRS.
PRSM governs whether the prediction has been faithfully represented.
PLDIM governs what that representation is interpreted to mean for a decision.
Its focused operational question is:
What decision-relevant meaning may legitimately be derived from a Governed Predictive Representation without converting predictive information into unsupported decision authority?
PLDIM does not determine whether reliance is ultimately legitimate.
It governs the interpretation from which downstream reliance may later be assessed.
Internal Module Flow
Prediction Representation & Signaling → Prediction-Led Decision Interpretation → Prediction Interpretation Boundary → Reliance Legitimacy & Context → Prediction Use Boundary
Current Position:
Prediction-Led Decision Interpretation
Module Operational Space
PLDIM applies whenever a prediction becomes relevant to a contemplated human, organizational, algorithmic, AI- assisted, autonomous, financial, operational, strategic, or public decision.
Examples include:
investment decisions; capital allocation; hiring and workforce planning; insurance decisions; infrastructure planning; supply-chain decisions;
maintenance decisions; medical resource planning; emergency preparation; policy planning; procurement; market positioning; autonomous-agent decisions; AI-assisted decisions; operational scheduling; risk mitigation; strategic planning.
The presence of a prediction does not itself establish what decision should follow.
1. Prediction-to-Decision Transition
A prediction describes a possible future condition or outcome.
A decision selects, authorizes, rejects, delays, modifies, or prioritizes an action.
These are different governed objects.
PLDIM governs the transition:
Predictive Meaning → Decision-Relevant Meaning
It does not permit the uncontrolled transition:
Predictive Meaning → Required Action
Prediction ≠ Decision
2. Decision Context
A prediction acquires decision relevance only within a Decision Context.
Decision Context may include:
decision objective; available alternatives; timing; resource constraints; legal constraints; organizational authority; operational conditions; risk tolerance; reversibility; consequence severity; competing objectives; other available information.
The same prediction may legitimately produce different decision interpretations in different contexts.
Same Prediction ≠ Same Decision Meaning
3. Decision Object
The Decision Object identifies what decision is actually under consideration.
Examples:
whether to invest; whether to evacuate; whether to delay deployment; whether to increase inventory; whether to inspect equipment; whether to enter a market; whether to allocate additional resources.
Without an identifiable Decision Object, predictive information may become attached to vague or uncontrolled decision implications.
4. Prediction Relevance
PLDIM determines whether the prediction is materially relevant to the contemplated Decision Object.
A prediction may be accurate and methodologically governed while still being irrelevant to a particular decision.
Predictive Quality ≠ Decision Relevance
5. Decision-Relevant Prediction Scope
Only the portion of the prediction relevant to the contemplated decision should inform its interpretation.
A broad forecast may contain multiple predictive elements.
Not every element necessarily applies to every decision.
PLDIM establishes the relationship between:
Prediction Scope ↔ Decision Scope
6. Prediction Purpose Alignment
POPS establishes the predictive purpose.
PLDIM evaluates whether the contemplated decision interpretation remains compatible with that purpose.
A prediction created for:
strategic scenario planning
must not automatically be interpreted as:
real-time operational instruction.
Predictive Purpose ≠ Universal Decision Purpose
7. Prediction Horizon Alignment
Decision timing must be interpreted relative to Prediction Horizon.
A five-year forecast may be highly relevant to strategic infrastructure planning while being inappropriate as the sole basis for an immediate operational intervention.
Likewise, a seven-day prediction may have little legitimate meaning for a ten-year capital decision.
Prediction Horizon ≠ Decision Horizon
Alignment must be established rather than assumed.
8. Predictive Boundary Alignment
PLDIM evaluates whether the decision falls within the Predictive Boundary.
A prediction limited to:
one jurisdiction; one facility; one customer segment; one technology; one operating environment;
must not automatically govern interpretation outside that boundary.
Decision Within Related Domain ≠ Decision Within Predictive Boundary
9. Methodological Relevance
A prediction methodology may be appropriate for one predictive purpose but insufficient for another decision context.
PLDIM does not validate the methodology itself.
PMES owns Prediction Methodology.
PLDIM determines whether the methodological meaning available from the prediction supports the contemplated decision interpretation.
10. Prediction Basis Relevance
A prediction may be formed from information that is appropriate for prediction formation but incomplete for a particular decision.
PLDIM therefore distinguishes:
Sufficient Prediction Basis ≠ Sufficient Decision Basis
A prediction can be legitimately formed without containing every fact necessary for a downstream decision.
11. Assumption Relevance
Material assumptions may affect decision interpretation.
If a prediction assumes:
stable regulation
but the contemplated decision concerns an environment in which regulatory change is imminent, the assumption becomes materially relevant to decision interpretation.
PLDIM preserves applicable assumption conditions without taking ownership of PAS governance.
12. Probability Interpretation
Probability indicates predictive likelihood under governed predictive conditions.
It does not directly determine what action should be taken.
For example:
70% probability
does not independently establish:
Approve
Reject
Invest
Evacuate
Deploy
Do Not Deploy
Decision meaning depends on consequences and context.
Probability ≠ Decision Threshold
13. Probability Threshold Interpretation
Organizations may define decision thresholds.
For example:
If probability exceeds 80%, trigger additional inspection.
The threshold is a decision rule.
It is not part of the probability itself.
PLDIM requires this distinction to remain visible.
Prediction Probability ≠ Decision Rule
Decision Threshold ≠ Predictive Truth Boundary
14. Uncertainty Interpretation
A prediction with significant uncertainty may still be decision-relevant.
Likewise, a prediction with relatively low uncertainty may still be insufficient for a high-consequence decision.
PLDIM governs how uncertainty affects decision interpretation without automatically converting uncertainty into prohibition.
High Uncertainty ≠ No Decision Relevance
Low Uncertainty ≠ Automatic Decision Authority
15. Confidence Interpretation
Confidence may affect how strongly a prediction is considered.
It does not independently determine whether action is justified.
High Confidence ≠ Mandatory Action
Low Confidence ≠ Mandatory Inaction
16. Feasibility Interpretation
PFES determines Prediction Feasibility.
PLDIM governs what that feasibility state means within a Decision Context.
A predicted outcome may be:
Feasible but improbable
or:
Probable but subject to material feasibility restriction.
Neither condition independently determines the appropriate decision.
Feasible ≠ Advisable
Infeasible ≠ Irrelevant to Planning
17. Collective Prediction Interpretation
Where CPGS governs a collective prediction, PLDIM preserves material collective qualifications when deriving decision meaning.
A market, crowd, ensemble, or multi-agent consensus may inform a decision.
It does not automatically become superior decision evidence because it is collective.
Collective Prediction ≠ Collective Decision Authority
18. Prediction Strength
Prediction Strength represents how strongly the governed prediction supports the specific predictive proposition it makes.
Decision strength is different.
A strong prediction concerning a minor outcome may justify less action than a weaker prediction concerning a catastrophic outcome.
Prediction Strength ≠ Required Decision Strength
19. Consequence Context
Decision interpretation may depend materially on the consequences of:
acting; not acting; acting too early; acting too late; choosing the wrong alternative.
PLDIM allows predictive meaning to be interpreted within consequence context without converting the prediction into a risk-governance system.
20. Asymmetric Consequences
Decision consequences may be asymmetric.
A 10% probability of a catastrophic event may justify substantial preparation.
A 90% probability of a trivial event may justify almost none.
Therefore:
Higher Probability ≠ Greater Decision Importance by Default
21. Reversibility
Decision reversibility may affect legitimate prediction-led interpretation.
A low-cost reversible decision may tolerate greater predictive uncertainty than an irreversible high-consequence decision.
Same Prediction + Different Decision Reversibility ≠ Same Decision Interpretation
22. Decision Materiality
Decision Materiality describes the significance of the contemplated decision.
Materiality may depend on:
financial exposure; human impact; safety implications; legal consequences; operational disruption; strategic significance; reversibility; duration.
PLDIM uses materiality to determine how much predictive interpretation governance is required.
23. Prediction as One Decision Input
A prediction may be only one input among many.
Other inputs may include:
current operational facts;
legal requirements; contractual obligations; budgets; ethical constraints; safety controls; human judgment; organizational policy; alternative scenarios.
Prediction Input ≠ Complete Decision Basis
24. Multi-Prediction Decision Context
A decision may involve several predictions.
Examples:
demand forecast; cost forecast; regulatory forecast; supply forecast; technology forecast.
PLDIM prevents one predictive signal from silently becoming the complete predictive basis where several materially relevant predictions exist.
25. Conflicting Predictions
Different predictions may support different decision interpretations.
PLDIM requires conflict to remain visible where material.
Conflict must not be silently resolved by selecting whichever prediction supports the preferred decision.
Prediction Conflict ≠ Permission for Selective Interpretation
26. Decision Confirmation Bias
Decision-makers may already prefer an outcome before consulting predictive information.
PLDIM identifies the risk that a prediction is interpreted primarily to validate a pre-existing decision.
Prediction Supporting Preferred Action ≠ Independent Decision Justification
27. Prediction Cherry-Picking
Where multiple relevant predictions exist, selecting only favorable predictions may materially distort decision interpretation.
PLDIM requires material predictive plurality to remain identifiable where applicable.
Available Supporting Prediction ≠ Complete Predictive Picture
28. Decision Framing
The same prediction may be framed differently.
For example:
30% probability of failure
versus:
70% probability of success
may describe complementary aspects of the same predictive state while producing different psychological interpretations.
PLDIM governs whether framing materially changes decision meaning.
Equivalent Probability Expression ≠ Equivalent Decision Perception
29. Language Strength
Predictive language can become prescriptive through wording.
Examples:
Prediction:
Failure is likely.
Interpretation:
We should stop the project.
These statements are not equivalent.
PLDIM prevents linguistic escalation from silently converting descriptive predictive meaning into prescriptive decision meaning.
Likely ≠ Should
Unlikely ≠ Ignore
30. Recommendation Conversion
A prediction may be used by another methodology or governance process to generate a recommendation.
PLDIM requires the transition to remain explicit:
Prediction → Interpretation → Recommendation
not:
Prediction = Recommendation
The recommendation requires its own legitimate basis.
31. Decision Authority
The existence of predictive information does not establish who has authority to make the decision.
Decision authority may belong to:
an individual; an organization; a regulator; an automated control system; an authorized AI agent; another governance structure.
Predictive Authority ≠ Decision Authority
32. AI Decision Interpretation
AI systems may interpret predictions as decision inputs.
PLDIM requires the AI system to preserve:
prediction scope; probability meaning; uncertainty; conditions; feasibility; purpose; applicable boundaries; decision context.
An AI system must not convert:
0.82 probability
directly into:
execute action
unless a separately governed decision rule legitimately establishes that transition.
Prediction Score ≠ Execution Command
33. Autonomous-Agent Interpretation
Autonomous agents may consume predictions dynamically.
PLDIM governs the predictive interpretation stage before action authority is considered.
The agent may determine:
This prediction is materially relevant to the contemplated action.
That does not itself mean:
The action is authorized.
Interpretation Eligibility ≠ Execution Eligibility
34. Decision Rule Interaction
A governed Decision Rule may define how predictions affect decisions.
Example:
If flood probability exceeds 60% and water level exceeds threshold X, initiate preparation protocol Y.
PLDIM governs the prediction's role within the rule.
It does not establish the legitimacy of unrelated operational controls.
35. Automated Thresholds
Automated systems frequently use thresholds.
Threshold governance should preserve:
threshold source; threshold purpose; applicable prediction type; decision context; consequence context; revision history.
A threshold should not become an unexplained bridge between prediction and action.
36. Historical Performance Interpretation
Past predictive performance may affect how a prediction is considered.
However:
Past Accuracy ≠ Current Decision Authority
POFPS governs Prediction Outcome & Performance.
PLDIM may use governed performance information as contextual input without treating it as permanent authority.
37. Predictor Identity Interpretation
The identity, reputation, institutional status, or authority of a predictor may influence how recipients interpret a prediction.
PLDIM distinguishes source reputation from predictive meaning.
Authority of Source ≠ Certainty of Prediction
38. Predictor–Reality Proximity
A predictor may have unusually close access to the actors, information, systems, or decisions from which the predicted outcome may emerge.
High Predictor–Reality Proximity may materially affect interpretation.
But:
High Predictor–Reality Proximity ≠ Misconduct
Outcome Knowledge ≠ Predictive Capability
Decision Proximity ≠ Decision Authority
PLDIM preserves these distinctions when prediction-led decisions are considered.
39. Prediction Influence on Decision
A prediction may influence a decision.
PLDIM makes that influence identifiable.
Potential influence states may include:
Informational; Material; Significant; Dominant; Determinative.
A dominant predictive influence does not automatically mean the decision was improperly governed.
It means the relationship should be visible.
40. Prediction Influence on Reality
A prediction-led decision may alter the reality being predicted.
The chain may become:
Prediction → Decision Interpretation → Decision → Operational Action → Changed Operational Reality
Where this occurs, the predictive environment may change.
PMRS may subsequently govern whether the prediction requires review or revision.
Prediction Influence ≠ Predictive Accuracy
41. Decision Interpretation State
PLDIM may classify a Prediction-Led Decision Interpretation as:
Decision-Interpretation Ready
Conditionally Decision-Interpretation Ready
Additional Decision Context Required
Interpretation Restricted
Prediction-Decision Misaligned
Insufficiently Supported
Unable to Determine
The state concerns the legitimacy of prediction-led interpretation.
It does not determine the final decision.
42. Interpretation Qualification
Where prediction-led interpretation remains legitimate only under specific conditions, PLDIM may attach an Interpretation Qualification.
Examples:
applicable only to strategic planning; insufficient for autonomous execution; additional operational evidence required; dependent on specified assumptions; limited to stated jurisdiction; requires additional prediction comparison; high-consequence reliance requires escalation.
43. Interpretation Traceability
A governed interpretation should remain traceable through:
Governed Predictive Representation → Decision Context → Prediction-Led Decision Interpretation
Where material, the record should identify:
prediction reference; prediction version; representation reference; Decision Object; decision context; relevant prediction elements; applicable qualifications; interpretation state.
44. Interpretation Versioning
Decision context can change even when the prediction remains unchanged.
Therefore, interpretation may require revision without prediction revision.
Prediction Unchanged ≠ Interpretation Permanently Unchanged
Interpretation Revision ≠ Prediction Revision
45. Governance Requirements
A governed PLDIM implementation should be able to:
1. identify the Governed Predictive Representation; 2. identify the underlying prediction; 3. identify the contemplated Decision Object; 4. identify Decision Context; 5. determine Prediction Relevance; 6. determine decision-relevant prediction scope; 7. evaluate predictive-purpose alignment; 8. evaluate Prediction Horizon alignment; 9. evaluate Predictive Boundary alignment; 10. preserve methodology context where relevant;
11. preserve Prediction Basis qualifications where relevant; 12. preserve material assumptions; 13. interpret probability without converting it into decision authority; 14. distinguish probability from decision thresholds; 15. preserve uncertainty; 16. preserve confidence distinctions; 17. preserve feasibility state; 18. preserve collective-prediction qualifications; 19. determine decision materiality; 20. identify consequence context; 21. identify asymmetric consequences; 22. identify decision reversibility; 23. distinguish prediction strength from required decision strength; 24. determine whether prediction is one input or the dominant input; 25. identify other materially relevant decision inputs; 26. identify multiple relevant predictions; 27. identify conflicting predictions; 28. detect material prediction cherry-picking; 29. identify decision-confirmation risk; 30. identify material framing effects; 31. prevent descriptive predictive language from becoming unsupported prescriptive language; 32. distinguish prediction from recommendation; 33. distinguish prediction from Decision Rule; 34. distinguish predictive authority from Decision Authority; 35. govern AI interpretation of predictive information; 36. govern autonomous-agent interpretation of predictive information; 37. identify automated prediction thresholds; 38. preserve threshold context; 39. identify relevant historical performance information; 40. prevent past accuracy from becoming automatic current decision authority; 41. identify predictor identity where materially relevant; 42. identify relevant Predictor–Reality Proximity; 43. distinguish outcome proximity from predictive skill; 44. identify Prediction Influence on Decision; 45. determine influence materiality; 46. identify prediction-to-reality feedback where relevant; 47. determine Decision Interpretation State; 48. determine required Interpretation Qualification; 49. preserve interpretation traceability; 50. preserve interpretation versioning; 51. distinguish interpretation revision from prediction revision; 52. identify Prediction-Decision Misalignment; 53. identify insufficient Decision Context; 54. identify unsupported predictive extrapolation; 55. identify decisions outside Predictive Boundary; 56. identify decisions outside Prediction Horizon relevance; 57. identify inappropriate conversion of probability into action; 58. identify inappropriate conversion of feasibility into advisability; 59. produce a Governed Prediction-Led Decision Interpretation; 60. preserve the separation between predictive information and decision authority.
46. Governance Logic
PLDIM applies the following operational logic:
If the Governed Predictive Representation is materially relevant and appropriately aligned with the Decision Context → Decision-Interpretation Ready.
If interpretation is legitimate only under explicit conditions or qualifications → Conditionally Decision- Interpretation Ready.
If material Decision Context is missing → Additional Decision Context Required.
If the contemplated interpretation exceeds predictive purpose, horizon, boundary, uncertainty, feasibility, or other governed conditions → Interpretation Restricted.
If the prediction and contemplated decision are materially incompatible → Prediction-Decision Misaligned.
If the available predictive structure cannot support the proposed decision meaning → Insufficiently Supported.
If sufficient information is unavailable to determine interpretation legitimacy → Unable to Determine.
47. Minimum Implementation Framework
A minimum PLDIM implementation should contain:
A. Prediction Reference
Reference to the underlying Governed Prediction.
B. Predictive Representation Reference
Reference to the PRSM-governed representation being interpreted.
C. Decision Object
Identification of the contemplated decision.
D. Decision Context
Relevant environment, objective, constraints, alternatives, and conditions.
E. Prediction Relevance
Relationship between the prediction and Decision Object.
F. Purpose Alignment
Compatibility between predictive purpose and contemplated decision use.
G. Horizon Alignment
Relationship between Prediction Horizon and Decision Horizon.
H. Boundary Alignment
Relationship between Predictive Boundary and Decision Scope.
I. Probability & Uncertainty Interpretation
Decision-relevant interpretation preserving PPUS distinctions.
J. Feasibility Interpretation
Applicable PFES state and its decision relevance.
K. Consequence & Materiality Context
Relevant consequence, materiality, and reversibility conditions.
L. Decision Input Position
Role of the prediction among other relevant decision inputs.
M. Prediction Influence State
Degree of influence the prediction is expected to have on the contemplated decision.
N. Decision Interpretation State
Current PLDIM governance state.
O. Interpretation Qualification & History
Applicable qualification, version, and historical interpretation reference.
48. Governance Outputs
PLDIM may produce:
Governed Prediction-Led Decision Interpretation Prediction Relevance Determination Purpose Alignment Determination Horizon Alignment Determination Boundary Alignment Determination Decision Context Profile Prediction Influence State Prediction-Decision Misalignment Record Interpretation Qualification Decision Interpretation State Interpretation History Reference
The primary output is:
Governed Prediction-Led Decision Interpretation
This becomes an input for subsequent PCRS interpretation-boundary and reliance governance.
49. Critical Distinctions
Prediction ≠ Decision
Prediction ≠ Recommendation
Prediction ≠ Decision Rule
Prediction ≠ Execution Authority
Predictive Authority ≠ Decision Authority
Predictive Quality ≠ Decision Relevance
Same Prediction ≠ Same Decision Meaning
Predictive Purpose ≠ Universal Decision Purpose
Prediction Horizon ≠ Decision Horizon
Prediction Basis ≠ Complete Decision Basis
Probability ≠ Decision Threshold
Prediction Probability ≠ Decision Rule
Decision Threshold ≠ Predictive Truth Boundary
High Probability ≠ Mandatory Action
Low Probability ≠ Mandatory Inaction
High Uncertainty ≠ No Decision Relevance
Low Uncertainty ≠ Automatic Decision Authority
High Confidence ≠ Mandatory Action
Feasible ≠ Advisable
Collective Prediction ≠ Collective Decision Authority
Prediction Strength ≠ Required Decision Strength
Higher Probability ≠ Greater Decision Importance by Default
Prediction Input ≠ Complete Decision Basis
Prediction Conflict ≠ Permission for Selective Interpretation
Likely ≠ Should
Unlikely ≠ Ignore
Authority of Source ≠ Certainty of Prediction
Outcome Knowledge ≠ Predictive Capability
Decision Proximity ≠ Decision Authority
Prediction Influence ≠ Predictive Accuracy
Interpretation Eligibility ≠ Execution Eligibility
Prediction Unchanged ≠ Interpretation Permanently Unchanged
Interpretation Revision ≠ Prediction Revision
50. Enterprise Application
PLDIM can be applied across:
executive decision-support systems; enterprise forecasting; investment decisions; financial planning; insurance; infrastructure planning; supply-chain management; predictive maintenance; emergency management; public-sector planning; healthcare resource planning; AI decision-support systems; autonomous systems; AI agents; multi-agent systems;
prediction markets; strategic planning; operational planning; procurement; resource allocation.
Its function remains constant:
preserve the difference between what the prediction says and what the decision-maker decides that information means for action.
51. Use Case 1 — 15% Flood Probability and a High-Consequence Decision
A regional infrastructure authority receives a governed prediction:
15% probability that a major flood threshold will be exceeded within the next 72 hours.
The probability is relatively low.
However, failure to prepare could expose:
tens of thousands of residents; critical electricity infrastructure; transport networks; hospitals; emergency services.
A simplistic decision system applies:
Probability below 50% → No action.
The critical governance question is:
Does a prediction below 50% legitimately mean that preparation is unnecessary?
PLDIM determines that probability alone cannot establish the decision interpretation.
The Decision Context contains severe asymmetric consequences.
A 15% probability may therefore remain materially decision-relevant even though the predicted event is not the most probable outcome.
The prediction does not command evacuation.
It may legitimately support preparation, escalation, monitoring, or contingency activation when combined with appropriate decision governance.
Low Probability ≠ Low Decision Importance
Probability ≠ Decision Threshold
Prediction ≠ Decision
52. Use Case 2 — AI Turns an 82% Prediction into an Automatic Investment Decision
An enterprise AI system receives a Governed Predictive Representation:
82% probability that demand for a new industrial technology will exceed a defined market threshold within three years.
The prediction was created for:
strategic market planning.
Its assumptions include:
continued regulatory support; specified manufacturing-cost reductions; infrastructure availability.
An autonomous investment agent receives the prediction and interprets:
82% probability → Investment approved.
The critical governance question is:
Does a strong strategic prediction legitimately authorize an autonomous capital-allocation decision?
PLDIM identifies several governance gaps.
The prediction does not itself establish:
expected investment return; acceptable acquisition price; capital availability; portfolio exposure; regulatory permission; organizational investment authority; downside consequences; alternative investments.
The correct transition is:
Governed Prediction → Decision-Relevant Interpretation → Separate Decision Governance
not:
Governed Prediction → Automatic Investment Authorization
The prediction may be highly relevant.
It still does not possess investment authority.
High Probability ≠ Mandatory Action
Prediction Input ≠ Complete Decision Basis
Predictive Signal ≠ Execution Permission
53. Canonical Boundary
PLDIM governs how a Governed Predictive Representation is interpreted in relation to a contemplated decision, including Prediction Relevance, predictive-purpose alignment, horizon alignment, boundary alignment, probability and uncertainty interpretation, feasibility interpretation, consequence context, materiality, reversibility, Prediction Influence, Decision Context, interpretation qualification, and interpretation traceability. It does not establish the underlying prediction, determine Prediction Methodology, govern Prediction Basis or assumptions, establish probability, determine Prediction Feasibility, establish Collective Prediction legitimacy, make the decision, create Decision Authority, authorize execution, determine final Reliance Legitimacy, establish Operational Reality, or determine predictive performance.
PRSM governs Prediction Representation & Signaling.
PLDIM governs the transition from represented predictive meaning to decision-relevant meaning.
Subsequent PCRS modules govern Prediction Interpretation Boundary, Reliance Legitimacy & Context, and Prediction Use Boundary.
54. Canonical Closing Statement
The moment a prediction enters a decision process, its probability can easily be mistaken for permission, its confidence for authority, and its relevance for instruction.
PLDIM preserves the boundary between predictive knowledge and decision meaning by governing what a prediction legitimately contributes to a decision without allowing it to silently become the decision itself.
A prediction may change what a decision-maker knows. It does not, by itself, decide what the decision-maker must do.