PSMM — Predictive Source Mapping Module
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: PGA™ — Prediction Governance Architecture
Parent Standard:
Prediction Basis Standard (PBAS)
Operational Layer: Prediction Basis Governance Layer
Category:
Governance & Enforcement
Subcategory:
Prediction Governance
Type: Parent Standard Module
Governed Space:
Predictive Source Mapping
Version: 1.0
Status: Canonical · Open Module
Effective Date: 7 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
Predictive Source Mapping
Canonical Definition
Predictive Source Mapping Module (PSMM) defines the governance conditions under which identified Prediction Inputs are associated with their material sources, origins, intermediaries, dependencies, informational relationships, and relevant proximity to the Operational Reality from which the predicted outcome may emerge.
PSMM establishes the governed source structure beneath Prediction Basis.
It enables PGA™ to distinguish:
multiple inputs
from:
multiple independent sources
and:
predictive capability
from:
informational advantage or proximity to the reality being predicted.
PSMM does not determine whether a source is evidentially valid, whether its information is predictively relevant, whether the Prediction Basis is sufficient, or whether source proximity constitutes misconduct.
Its function is to make the source relationships behind the prediction identifiable.
Operational Role
PSMM serves as the second internal governance space of the Prediction Basis Standard (PBAS) .
It receives:
Identified Prediction Inputs
from:
PIIM — Prediction Input Identification Module
and establishes the source relationships underlying those inputs.
The module asks:
Where did the identified Prediction Inputs actually come from, how are their sources related, and how close are those sources to the information, actors, decisions, processes, or conditions from which the predicted outcome may emerge?
The governed progression is:
Identified Prediction Inputs → Source Identification → Source-Origin Mapping → Dependency Mapping → Predictor–Reality Proximity Mapping → Governed Predictive Source Map
The resulting source map becomes an input to:
SRVM — Signal Relevance Module.
Internal Module Flow
Prediction Input Identification → → Predictive Source Mapping → Signal Relevance → Input Sufficiency → Prediction Basis Traceability
Current Position: Predictive Source Mapping
Module Operational Space
PSMM governs:
predictive source identity; source origin; immediate source; originating source; intermediary source; direct source relationships; indirect source relationships;
derived-source relationships; source chains; source lineage; shared-source relationships; source dependency; source independence claims; source concentration; informational concentration; source duplication; source transformation relationships; human sources; organizational sources; machine sources; AI sources; model sources; simulation sources; market sources; operational sources; public-information sources; non-public-information sources; information-access relationships; information-timing relationships; information-exclusivity relationships; organizational proximity; operational proximity; decision proximity; authority proximity; outcome knowledge; outcome influence; outcome control; conflicts of interest where informationally material; Predictor–Reality Proximity; source-map change; and source-map traceability.
Module Function
PSMM prevents organizations from assuming that the number of visible Prediction Inputs represents the number or diversity of underlying information sources.
A predictive system may receive:
100 inputs
that ultimately originate from:
one source.
Five forecasting systems may appear independent while all relying on the same upstream dataset.
Ten analysts may appear to provide independent judgments while all relying upon the same private briefing.
A prediction market may appear to represent distributed forecasting while a small number of highly informed or capital- concentrated participants materially determine the signal.
PSMM makes these relationships visible before PGA™ evaluates relevance, sufficiency, collective prediction legitimacy, or predictive performance.
Multiple Inputs ≠ Independent Sources
Predictive Source
A Predictive Source is an identifiable person, system, organization, mechanism, information origin, or other source from which a material Prediction Input originates or through which it materially reaches the predictive process.
Sources may include:
individuals; expert groups; organizations; databases; sensors; operational systems; AI systems; analytical models; simulation systems; prediction markets; public records; private information systems; research institutions; external services; market infrastructures; other identifiable information origins.
Immediate Source
The Immediate Source is the source from which the predictive system directly receives the Prediction Input.
For example:
Original Dataset → Research Firm → Analyst Report → Prediction System
The analyst report may be the Immediate Source.
It is not necessarily the originating source.
Immediate Source ≠ Original Source
Originating Source
The Originating Source represents the materially relevant origin from which the information emerged.
Identifying originating sources helps prevent transformed, republished, summarized, or redistributed information from appearing independent merely because it entered the prediction through different channels.
Intermediary Source
Information may pass through one or more intermediaries before becoming Prediction Input.
An intermediary may:
transmit; summarize; transform; classify; aggregate; interpret; filter; enrich; redistribute
information.
PSMM preserves material intermediary relationships where they affect understanding of source structure.
Source Chain
Where information passes through multiple sources, PSMM may represent the relationship as:
Originating Source → Intermediary Source → Immediate Source → Prediction Input
More complex structures may contain multiple intermediary layers.
The purpose is not unlimited provenance reconstruction.
The purpose is sufficient source mapping for Prediction Basis governance.
Source Lineage
Source Lineage preserves the material path through which information moved from origin toward Prediction Input.
It allows later governance to distinguish independent information from information that merely appears independent because it was redistributed or transformed.
Shared-Source Relationship
Two or more Prediction Inputs possess a Shared-Source Relationship where they materially derive from the same underlying informational source.
For example:
Source A → Report 1
Source A → Report 2
Source A → AI Summary
may produce three Prediction Inputs while preserving one materially shared informational origin.
Multiple Representations ≠ Multiple Independent Sources
Source Dependency
Source Dependency exists where one predictive source materially depends upon another source for the information it provides.
Dependency may be:
direct; indirect; partial; hierarchical; informational; analytical; operational.
PSMM makes dependency visible.
It does not automatically determine that dependent information lacks value.
Source Independence
Source independence must not be inferred merely because sources possess different names, organizations, interfaces, or delivery channels.
Meaningful independence requires absence of material underlying dependence relevant to the Prediction Basis.
PSMM does not certify universal independence.
It establishes the source relationships necessary for independence claims to be assessed.
Different Source Labels ≠ Independent Sources
Source Concentration
Prediction Basis may depend heavily upon a small number of underlying sources.
Source Concentration describes the degree to which informational support originates from limited source origins.
High concentration does not automatically invalidate Prediction Basis.
It must remain identifiable where material.
Source Quantity ≠ Source Diversity
Informational Concentration
Informational Concentration may exist even where numerous sources are technically present.
For example, dozens of news reports may reproduce the same original announcement.
The number of documents therefore does not necessarily represent informational diversity.
Document Count ≠ Information Diversity
Human Sources
Human sources may include:
experts; analysts; operators; decision-makers; witnesses; researchers; market participants; domain specialists.
PSMM maps their informational role without determining the correctness of their statements or allocating accountability.
Organizational Sources
Organizations may function as predictive sources through:
reports; internal records; forecasts; operational information; research; disclosures; private communications.
Where multiple outputs originate from one organization, organizational relationships may be relevant to source dependency.
Machine and AI Sources
Machine systems may provide Prediction Inputs directly or through other systems.
PSMM maps relationships such as:
Underlying Information → AI System → AI Output → Prediction Input
or:
Model A → Model B → Prediction System
AI-generated outputs must not automatically be treated as independent sources merely because they are generated by different interfaces.
Different AI Systems ≠ Independent Information Sources by Default
Simulation Sources
Simulation systems may produce Prediction Inputs.
PSMM preserves the simulation source relationship.
It does not govern the simulation itself.
Simulation Source ≠ Operational Reality Source
SIMULOS® retains governance of simulation.
Market Sources
Markets may produce:
prices; implied probabilities; volume; liquidity information; order-book states; sentiment indicators; prediction-market outputs.
PSMM maps the market as a source and, where material, the informational structure underlying the market signal.
A market signal may represent many participants while remaining highly concentrated in actual influence.
Market Participation ≠ Information Independence
Public-Information Sources
Public information may form part of Prediction Basis.
PSMM identifies its source relationships where material.
Multiple predictors using the same public information may produce different predictions without possessing independent informational foundations.
Non-Public-Information Sources
Non-public information may also form Prediction Basis.
PSMM preserves its source relationship where material.
It does not determine whether access or use was lawful.
Non-Public Information ≠ Misconduct by Default
Legal and compliance determinations remain outside PGA™.
Information Access Relationship
PSMM may identify whether a predictor or predictive source possesses:
ordinary access; privileged access; earlier access; exclusive access; operational access; decision-proximate access
to information materially related to the predicted outcome.
Access characteristics can materially affect interpretation of predictive capability.
Information Timing
Two predictors may receive the same information at different times.
Earlier access may create predictive advantage.
PSMM therefore preserves information-timing relationships where material.
Earlier Information Access ≠ Superior Prediction Methodology
Information Exclusivity
A source may possess information unavailable to other predictors.
Information exclusivity may materially affect predictive performance.
PSMM identifies the condition without determining whether the exclusivity is appropriate.
Exclusive Information ≠ Predictive Skill
Predictor–Reality Proximity
Predictor–Reality Proximity (PRP) represents the governed relationship between a predictor or predictive source and the information, actors, processes, authorities, systems, decisions, or operational conditions from which the predicted outcome may emerge or by which that outcome may be materially determined or influenced.
PRP provides PGA™ with a structured method for distinguishing ordinary forecasting from prediction occurring close to the mechanisms that generate the outcome.
Predictor–Reality Proximity Dimensions
Where relevant, PRP may consider:
information access; information timing; information exclusivity; organizational proximity; operational proximity; decision proximity; authority proximity; non-public information exposure; outcome knowledge; influence capability; control capability; materially relevant conflicts.
Not every prediction requires every dimension.
The relevant dimensions depend upon the predicted outcome and predictive context.
Predictor–Reality Proximity Continuum
Where useful, PSMM may classify predictive relationships along the following governance continuum:
Public-Information Predictor
↓
Information-Advantaged Predictor
↓
Decision-Proximate Predictor
↓
Outcome-Informed Predictor
↓
Outcome-Influencing Predictor
↓
Outcome-Controlling Predictor
The continuum describes relationships.
It does not determine wrongdoing.
High Predictor–Reality Proximity ≠ Misconduct
Public-Information Predictor
A Public-Information Predictor operates primarily from information broadly available to other participants.
This does not imply low predictive capability.
It describes the informational relationship to Operational Reality.
Information-Advantaged Predictor
An Information-Advantaged Predictor possesses materially superior, earlier, richer, or more exclusive information than ordinary predictors.
Such advantage may improve observed predictive performance independently of methodological capability.
Information Advantage ≠ Predictive Capability
Decision-Proximate Predictor
A Decision-Proximate Predictor possesses material proximity to people, systems, or processes capable of influencing or determining the predicted outcome.
Decision proximity does not establish authority.
Decision Proximity ≠ Decision Authority
Outcome-Informed Predictor
An Outcome-Informed Predictor possesses information that materially indicates or establishes the likely or actual outcome beyond what ordinary prediction would reveal.
This creates a fundamentally different informational condition from ordinary forecasting.
Outcome Knowledge ≠ Predictive Capability
Outcome-Influencing Predictor
An Outcome-Influencing Predictor possesses some ability to materially affect the conditions from which the predicted outcome emerges.
Influence may be direct or indirect.
Prediction Influence ≠ Outcome Control
PSMM maps the relationship.
PCRS and PMRS govern later implications for communication, reliance, and changing predictive conditions.
Outcome-Controlling Predictor
An Outcome-Controlling Predictor possesses sufficient authority or operational capability to materially determine the outcome within the relevant context.
A prediction produced under such conditions must not automatically be interpreted as demonstrating ordinary forecasting capability.
Outcome Control ≠ Predictive Skill
Outcome Knowledge
Where a source already possesses material knowledge of the outcome, that condition must remain distinguishable from prediction.
Knowing an outcome before others is not the same as predicting it better than others.
PSMM records the informational relationship.
It does not determine legal or ethical consequences.
Outcome Influence and Control
Influence and control must remain separate governance states.
A source may influence an outcome without controlling it.
Another may possess authority without exercising it.
Another may participate operationally while lacking decision authority.
Therefore:
Outcome Influence ≠ Outcome Control
Decision Proximity ≠ Decision Authority
Authority ≠ Exercised Control
Conflict Relationships
A source may possess interests connected to the predicted outcome.
Where materially relevant to understanding the source relationship, PSMM may preserve such conflict context.
It does not determine misconduct or allocate responsibility.
Potential Conflict ≠ Established Misconduct
Source Map Change
Source relationships may change over time.
Examples include:
new source introduction; source removal; dependency change; ownership change; changed information access; changed operational role; changed decision proximity; new influence capability.
A historical source map must not automatically be replaced by the current source map.
Current Source Relationship ≠ Historical Source Relationship
Source Mapping Sufficiency
Predictive Source Mapping is sufficiently governed when:
material sources are identifiable; immediate and originating sources are distinguishable where necessary; material intermediary relationships are visible; shared-source relationships can be identified; material source dependencies remain visible;
source concentration can be recognized; information-access relationships are identifiable where material; Predictor–Reality Proximity can be represented where relevant; outcome knowledge, influence, and control remain distinguishable; historical source relationships remain reconstructable.
PSMM does not determine signal relevance or Prediction Basis sufficiency.
Governance Requirements
PSMM requires that:
1. material Prediction Inputs be associated with identifiable sources where possible; 2. immediate source not automatically be treated as originating source; 3. material intermediary relationships remain visible; 4. shared-source relationships remain identifiable; 5. multiple inputs not automatically be treated as independent sources; 6. source dependency remain governable; 7. source quantity not be mistaken for source diversity; 8. AI, model, simulation, market, human, organizational, and operational sources remain distinguishable where material; 9. public and non-public informational relationships remain identifiable where relevant; 10. information timing and exclusivity remain governable where material; 11. Predictor–Reality Proximity be assessed where materially relevant; 12. information advantage remain distinguishable from predictive capability; 13. decision proximity remain distinguishable from decision authority; 14. outcome knowledge remain distinguishable from predictive capability; 15. outcome influence remain distinguishable from outcome control; 16. high Predictor–Reality Proximity not automatically establish misconduct; 17. material conflicts remain identifiable without premature determination; 18. source-map changes remain historically traceable.
Governance Logic
PSMM applies the following governance logic:
Identified Prediction Inputs
↓
Immediate Sources Identified
↓
Originating Sources Identified where material
↓
Intermediary Relationships Mapped
↓
Source Lineage Established
↓
Shared-Source Relationships Identified
↓
Source Dependencies Assessed
↓
Source Concentration Identified
↓
Information Access / Timing / Exclusivity Mapped where material
↓
Predictor–Reality Proximity Assessed where applicable
↓
Outcome Knowledge / Influence / Control Relationships Distinguished
↓
Governed Predictive Source Map
↓
SRVM — Signal Relevance Module
Where material source relationships cannot be established:
Predictive Source Mapping Gap → Clarification / Reconstruction / Source Disclosure Requirement
Minimum Implementation Framework
A minimum implementation of PSMM must establish:
A. Prediction Input Reference
The identified Prediction Input governed through PIIM.
B. Immediate Source
The source directly supplying the input.
C. Originating Source
The material informational origin where identifiable and relevant.
D. Intermediary Sources
Material intermediary sources through which information passed.
E. Source Type
Where applicable:
Human / Organization / Operational System / Sensor / Database / AI / Model / Simulation / Market / Public Source / Private Source / Other
F. Source Relationship
At minimum:
Direct / Indirect / Derived / Shared / Dependent / Unknown
G. Dependency State
At minimum:
Independent Relationship Not Materially Contradicted / Partially Dependent / Materially Dependent / Shared Origin / Unable to Determine
This state does not constitute universal certification of independence.
H. Information Access State
Where material:
Public / Restricted / Privileged / Exclusive / Unknown
I. Information Timing State
Where material:
Ordinary / Earlier Access / Outcome-Proximate / Post-Outcome / Unknown
J. Predictor–Reality Proximity
Where applicable:
Public-Information → Information-Advantaged → Decision-Proximate → Outcome-Informed → Outcome- Influencing → Outcome-Controlling
K. Influence and Control State
Where applicable:
No Material Influence Identified / Potential Influence / Material Influence / Material Control / Unable to Determine
L. Source Mapping Gap
Any material source relationship that cannot be sufficiently established.
M. Historical Preservation
The source relationships associated with the prediction must remain reconstructable.
Governance Outputs
PSMM may produce:
Governed Predictive Source Map; Source Register; Source Lineage Map; Immediate Source Record; Originating Source Record; Intermediary Source Record; Shared-Source Map; Source Dependency Profile; Source Concentration Profile; Information Access Profile; Information Timing Profile; Information Exclusivity Record; Predictor–Reality Proximity Profile; outcome-knowledge relationship; outcome-influence relationship; outcome-control relationship; material conflict context; historical source-state record; unresolved source-mapping requirements.
The primary governed output is:
Governed Predictive Source Map
This becomes a principal input into:
SRVM — Signal Relevance Module.
Critical Distinctions
Immediate Source ≠ Original Source
Multiple Inputs ≠ Independent Sources
Multiple Representations ≠ Multiple Independent Sources
Different Source Labels ≠ Independent Sources
Source Quantity ≠ Source Diversity
Document Count ≠ Information Diversity
Different AI Systems ≠ Independent Information Sources by Default
Simulation Source ≠ Operational Reality Source
Market Participation ≠ Information Independence
Earlier Information Access ≠ Superior Prediction Methodology
Exclusive Information ≠ Predictive Skill
High Predictor–Reality Proximity ≠ Misconduct
Information Advantage ≠ Predictive Capability
Decision Proximity ≠ Decision Authority
Outcome Knowledge ≠ Predictive Capability
Outcome Knowledge ≠ Outcome Control
Prediction Influence ≠ Outcome Control
Outcome Control ≠ Predictive Skill
Authority ≠ Exercised Control
Potential Conflict ≠ Established Misconduct
Current Source Relationship ≠ Historical Source Relationship
Enterprise Application
PSMM enables organizations to understand the real informational structure beneath predictive systems.
This becomes especially important where predictions are produced from large networks of:
data providers + AI systems + external models + research services + experts + markets + operational systems + public information.
Without source mapping, organizations may mistake information duplication for confirmation, dependency for diversity, privileged access for predictive skill, or outcome proximity for forecasting capability.
PSMM therefore creates a governance layer between:
“How many inputs or predictors do we have?”
and:
“How many materially distinct informational sources actually support the prediction?”
Use Case 1 — Twenty Independent Forecasts That All Depend on the Same Source
An organization aggregates twenty forecasts from different analysts, AI systems, research providers, and external forecasting services.
The apparent diversity creates strong confidence because many different predictors reach similar conclusions.
Source mapping later reveals that:
twelve use the same commercial dataset; four rely upon reports derived from that dataset; two AI systems retrieve summaries of those reports; several analysts reference one another's forecasts.
The twenty predictions therefore contain substantially less informational independence than their visible number suggests.
The critical governance question is:
Can agreement among many predictive sources legitimately be interpreted as independent confirmation when those sources materially depend upon the same underlying information?
PSMM maps the shared origins and dependencies before later PGA™ governance determines relevance, collective prediction legitimacy, or reliance.
Predictor Count ≠ Independent Information Count
Use Case 2 — A Predictor Appears Exceptionally Accurate Because It Is Close to the Outcome
A prediction market repeatedly shows one participant making unusually accurate predictions about whether major corporate transactions will close.
The participant appears to possess exceptional predictive skill.
Further source mapping shows that the participant maintains operational relationships with individuals close to the relevant transactions and frequently receives information earlier than the wider market.
In some cases, the participant may also interact with people capable of influencing the final outcome.
The critical governance question is:
Does the participant's observed accuracy demonstrate superior forecasting capability, or does it materially reflect informational and operational proximity to the reality being predicted?
PSMM does not assume misconduct.
It establishes the relevant Predictor–Reality Proximity and preserves the distinction between:
Prediction
Information Advantage
Outcome Knowledge
Outcome Influence
and:
Outcome Control
This allows later PGA™ performance governance to interpret predictive success within its actual informational context.
Knowing more about the emerging outcome is not automatically the same as predicting it better.
Canonical Boundary
PSMM governs the identification and mapping of predictive sources, source origins, intermediaries, source lineage, shared-source relationships, source dependency, source concentration, information-access relationships, information timing, information exclusivity, Predictor–Reality Proximity, and materially relevant outcome-knowledge, influence, and control relationships. It does not govern Prediction Input identification, signal relevance, Prediction Basis sufficiency, evidence validity, legal information access, misconduct determination, accountability allocation, collective prediction legitimacy, Operational Reality, or predictive performance.
PSMM establishes:
where the information behind the prediction came from and how its sources relate to the reality being predicted.
It does not determine:
whether those sources make the prediction correct.
Canonical Closing Statement
The number of inputs behind a prediction says little about informational diversity until their underlying sources and dependencies are known.
PSMM makes the source structure visible so that information advantage, source dependence, and proximity to reality are not mistaken for predictive capability.