Canonical vs. Derived Outputs Doctrine

2.1.1.

A deterministic, inspectable, context-independent numeric representation of the quantifiable, non-price components associated with a single option, expressed in standardized monetary units, computed under a declared ruleset version.

2.2.1. Determinism

Identical inputs under the same declared ruleset version always produce identical outputs.

2.2.2. Context Independence

Canonical AVOL outputs are invariant to undeclared user identity, preferences, intent, inferred attributes, probability of realization, or situational factors. Any variation in canonical output must arise solely from an explicitly declared evaluation context or ruleset, not from implicit personalization or adaptive inference.

2.2.3. Structural Neutrality

Outputs encode no implicit or undisclosed incentives, commissions, affiliations, ranking intent, or outcome optimization.

2.2.4. Inspectability

Outputs are decomposable into explicit arithmetic transformations using declared conversion constants and aggregation logic.

2.2.5. Scalar Finality

The output is a single numeric value, complete and self-contained, requiring no further interpretation to exist.

2.2.6. Cross-Option Comparability (Passive)

Outputs are numerically comparable across options without AVOL performing any comparison, ordering, or selection. Canonical AVOL outputs are comparable only when generated under the same declared evaluation context and ruleset version.

Rulesets that include base price produce a semantically distinct canonical output and SHALL NOT be compared to outputs produced under rulesets that exclude base price.

2.2.7. Numeric Magnitude

Numeric magnitude within a canonical AVOL output conveys no directive, preferential, or ordering semantics and does not imply suitability, recommendation, or selection

2.3.1.

The normalized numeric value itself

2.3.2.

The declared ruleset or version identifier

2.3.3.

The explicit unit of expression (standardized monetary equivalent)

2.3.4.

No additional metadata is canonical.

2.4.1.

Best value

2.4.2.

Cheapest option

2.4.3.

Recommended option

2.4.4.

Optimal choice

2.4.5.

Likely preference

2.4.6.

Expected outcome

2.4.7.

Predicted behavior

3.1.1.

Consumes a canonical AVOL output, and

3.1.2.

Applies intent, preference, objectives, or contextual logic beyond representation.

3.2.1. Comparative Derivation

• Ranking options
• Sorting by AVOL output
• Selecting a “best” or “top” option
• Computing deltas or relative advantage

3.2.2. Interpretive Derivation

• Labeling outputs (e.g., “best value,” “savings”)
• Translating outputs into recommendations or advice
• Framing outputs as outcomes or benefits

3.2.3. Objective-Based Derivation

• Optimization against a goal (cost minimization, reward maximization)
• Weighting outputs by preferences or constraints
• Combining AVOL outputs with probabilities or forecasts

3.2.4. Contextual Derivation

• Personalization
• User-specific adjustment
• Situational filtering or tailoring

3.2.5. Predictive or Behavioral Derivation

• Estimating likelihood of realization
• Inferring user intent or satisfaction
• Projecting future outcomes

4.1.1.

The canonical boundary terminates at the moment AVOL emits its numeric output. No operation beyond that emission – whether computational, visual, semantic, or behavioral – falls within AVOL.

4.2.1.

Generating or emitting any AVOL-attributable output that encodes comparison, ordering, selection, or relative advantage across options.

4.2.2.

Combining AVOL outputs with user data

4.2.3.

Using AVOL outputs as inputs to optimization logic

4.2.4.

Embedding AVOL outputs into recommendation systems in a manner that attributes the resulting recommendations to AVOL.

4.3.1.

Downstream systems may freely derive, interpret, or act upon AVOL outputs, but those actions:

• Are not AVOL behavior
• Are not AVOL responsibility
• Do not alter AVOL semantics

5.1.1.

Correct, deterministic computation of canonical outputs

5.1.2.

Adherence to declared rulesets and constraints

5.1.3.

How outputs are interpreted

5.1.4.

What decisions are made

5.1.5.

What outcomes occur

5.1.6.

What recommendations are issued

5.2.1.

Redefine the meaning of a canonical AVOL output

5.2.2.

Imply intent retroactively

5.2.3.

Attribute decisioning behavior to AVOL

5.3.1.

Canonical meaning is fixed at output emission and cannot be expanded by downstream behavior.

6.2.1.

Ranks options using AVOL outputs

6.2.2.

Recommends an option

6.2.3.

Optimizes for a goal

6.2.4.

Incorporates preferences or probabilities

6.3.1.

AVOL does not recommend.

6.3.2.

AVOL cannot recommend.

6.3.3.

AVOL does not implicitly recommend by enabling recommendation.