Document Type: Framework
Status: Active
Version: v1.0
Authority: MWMS HeadOffice
Parent: Experimentation Brain Canon
Slug: experimentation-brain-experimentation-operating-system-framework
Purpose
Defines the complete system structure governing how experimentation operates across MWMS environments.
Ensures experimentation functions as an organisational capability producing structured learning, reliable decision support, and continuous performance improvement.
Prevents experimentation from degrading into isolated testing activity disconnected from strategic objectives.
Scope
Applies to all MWMS Brains conducting structured validation activity including:
- Ads Brain
- Lifecycle Brain
- Affiliate Brain
- Ecommerce Brain
- Research Brain
- Strategy Brain
- AIBS Brain
Applies across:
- acquisition experimentation
- conversion experimentation
- retention experimentation
- messaging experimentation
- offer experimentation
- operational experimentation
- automation experimentation
Core Principle
Experimentation is a continuous intelligence generation system designed to improve decision quality under uncertainty.
The system must produce:
- reliable signals
- structured learning
- directional clarity
- measurable performance change
Operating Model Structure
Layer 1 — Problem Assessment and Strategic Integration
Identifies performance constraints aligned with strategic objectives.
Ensures experimentation effort targets meaningful system improvement opportunities.
Layer 2 — Solution Design and Planning
Structures hypotheses into testable models.
Defines:
- intervention logic
- measurable outputs
- success criteria
- feasibility constraints
Layer 3 — Test Execution
Ensures experiments are implemented with sufficient:
- signal clarity
- measurement integrity
- methodological discipline
Layer 4 — Decision Logic
Determines how results influence:
- system change
- iteration direction
- resource allocation
- priority adjustments
Layer 5 — Learning Capture and Distribution
Ensures knowledge generated from experiments becomes reusable organisational intelligence.
Supports:
- cross-Brain learning
- repeated pattern detection
- capability development
Inputs
Strategic priorities
performance constraints
signal anomalies
research insights
operational friction indicators
customer behaviour signals
conversion inefficiencies
cost inefficiencies
Outputs
validated learning
decision support signals
performance improvement direction
test outcome classifications
knowledge assets
optimisation pathways
Interfaces
Strategy Brain
Research Brain
Lifecycle Brain
Ads Brain
Affiliate Brain
AIBS Brain
HeadOffice Brain
Decision Logic
Decision pathways supported by the operating system include:
implementation
iteration
expansion
reversal
reframing
additional research requirement
Signal Requirements
Experiments must generate signals that meet minimum thresholds for:
clarity
interpretability
reliability
directional value
Signals must reduce uncertainty regarding system behaviour.
Failure Modes
Testing without problem clarity
testing without measurable outputs
testing without decision pathways
learning not captured
learning not distributed
false signal interpretation
overfitting to isolated conditions
Governance Notes
HeadOffice retains authority over experimentation capability standards.
Experimentation Brain responsible for:
process discipline
signal quality
knowledge capture structure
Canon Relationships
Experimentation Brain Canon
Research Brain Canon
Strategy Brain Architecture
Signal Surface Map
Change Log
v1.0 initial canonical structure defined