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MICE-EX

MICE — Execution Model

1. Purpose

The Micro Engines (MICE) Execution Model defines how micro engines work together to transform raw data into trusted, explainable, and auditable ESG intelligence within ZAYAZ.

This chapter serves as the conceptual entry point to the Computation Hub:

• It explains how engine categories compose
• It clarifies why categories exist
• It establishes execution order, boundaries, and provenance rules

Detailed documentation of individual micro engines follows in subsequent chapters.

2. Micro Engines vs Engine Categories

ZAYAZ distinguishes between:

• Micro Engine Instances (MEIDs)
  Concrete executable components implementing specific logic.

• Engine Categories (Types)
  Semantic classifications that describe what kind of operation a micro engine performs.

Examples:

Engine Category	Meaning
CALC	Primary computation
VALI	Validation
TRFM	Transformation
AGGR	Aggregation
SEM	Estimation
META	Metadata enrichment

A single category (e.g. CALC) may contain many micro engines, each registered with a unique MEID.

This distinction enables:

• consistent provenance
• USO semantic classification
• audit-grade explainability

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3. Canonical Engine Categories

The following engine categories form the MICE taxonomy.

3.1. Core Computation Spine

These categories define the mandatory backbone of execution:

Code	Category	Purpose
CALC	Calculation Engines	Compute primary values from raw inputs
VALI	Validation Engines	Enforce schema and constraint correctness
TRFM	Transformation Engines	Convert units, formats, and structures
AGGR	Aggregation Engines	Consolidate computed outputs
SEM	Stochastic Extrapolation Module	Estimate missing values

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3.2. Semantic & Analytical Engines

These categories enrich meaning and enable analysis:

Code	Category	Purpose
META	Metadata Enrichment Engines	Attach taxonomy, compliance, and semantic metadata
NORM	Normalization Engines	Normalize values for comparability
RMAP	Risk Mapping Engines	Map signals to risk frameworks

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3.3. Domain-Specific Processing Engines

These categories handle specialized data sources:

Code	Category	Purpose
EXTR	Extraction Engines	Extract structured data from unstructured sources
GEOX	Geospatial Engines	Process spatially referenced data
SENS	Sensor Logic Units	Device-aware processing of sensor/IoT data
TSER	Time-Series Engines	Source-agnostic temporal signal processing

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3.4. Execution Flow (Typical)

A typical execution pipeline may look like:

Raw Data
 ↓
EXTR / SENS
 ↓
TSER
 ↓
VALI
 ↓
TRFM
 ↓
CALC
 ↓
(optional SEM)
 ↓
AGGR
 ↓
META / NORM / RMAP
 ↓
Reporting / Disclosure / Verification

3.5. Key Principles

• Not all categories are required in every pipeline
• Execution is composable, not monolithic
• Categories may repeat (e.g. multiple AGGR stages)
• All steps are explicitly logged

4. Deterministic Control & Synchronization Engines

In addition to core computation and semantic enrichment, the MICE Execution Model includes deterministic control and coordination engines that support data readiness and trust decisions.

4.1. TSYN — Time Series Synchronization

TSYN engines provide deterministic alignment and synchronization of time-series data.

They are used to:

• align datasets with differing temporal resolutions,
• interpolate missing points using explicit, non-probabilistic rules,
• harmonize reporting calendars prior to aggregation or normalization.

TSYN engines operate after conditioning (SENS / TSER) and before aggregation (AGGR), and must not be confused with probabilistic estimation (SEM).

4.2. CFIL — Confidence Filter

CFIL engines assign confidence scores to signals and apply threshold-based filtering or flagging. They act as a trust control gate, determining whether data is admissible for downstream use.

CFIL engines:

• do not modify values,
• do not enforce policy decisions,
• make confidence explicit and auditable.

CFIL may appear at multiple points in a pipeline, particularly after:

• EXTR (extraction),
• SENS / TSER (signal conditioning),
• assistive intelligence outputs (when approved).

Both TSYN and CFIL are deterministic, replayable, and auditable, and are treated as MICE-adjacent engine categories within the Computation Hub.

5. Provenance & Audit Model

Every micro engine execution records:

• engine_type (e.g. CALC, AGGR)
• engine_instance_id (MEID)
• input and output hashes
• execution context and timestamps

This enables:

• full replay of results
• distinction between computed, estimated, and aggregated values
• verifier-grade audit trails

Engine category is a first-class provenance attribute.

6. Relationship to USO

Engine categories directly inform USO semantics.

Examples:

• Values produced by CALC are computed
• Values produced by SEM are estimated
• Values produced by AGGR are aggregated

USO identifiers reference engine categories to ensure that semantic meaning is preserved across systems, reports, and time.

7. Relationship to ZARA and Policy Engines

The execution model integrates with:

• ZARA — for explainability and orchestration
• TRPG / ZADIF — for policy-aware routing and enforcement

Because engine categories are explicit:

• ZARA can explain how a value was produced
• Policy gates can restrict or require certain engine types
• Assurance workflows can verify allowed computation paths

8. Design Rationale

The MICE Execution Model makes implicit logic explicit.

By separating:

• what kind of operation occurred (engine category)
• from how it was implemented (MEID)

ZAYAZ achieves:

• clarity
• scalability
• auditability
• long-term maintainability

Micro engines are implementation. MICE is architecture.

9. Next: Engine Deep Dives

The following chapters provide detailed specifications for each engine category, including inputs, outputs, non-responsibilities, and example micro engines (MEIDs).

Start with:

• CALC — Calculation Engines
• VALI — Validation Engines
• TRFM — Transformation Engines
• AGGR — Aggregation Engines

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Status: Stable
Owner: Computation Hub / MICE