Model
Representation and inference component
Model has several scopes: a representation of a subject; an executable representation; or, in AI, the inference component transforming inputs into outputs. State the scope before using the term. [7] [8] Editorial interpretation
On this page
Definition comparison
General representation
Institutional; NASA modeling and simulation
NASA defines a model as a description or representation of a system, entity, phenomenon or process. It separately defines conceptual and computational models. [7]
Computational representation
Institutional; NASA terminology
A computational model is a usable implementation of a conceptual model, including mathematical, numerical, logical or qualitative representations. Executability alone does not specify what is represented. [7] Editorial interpretation
AI inference component
Institutional; AI-system context
The EU-U.S. terminology identifies a model as a core AI-system component that infers outputs from inputs. This context is narrower than every possible software function. [8] Editorial interpretation
Scope
A model need not be learned. NASA’s modeling-and-simulation sense includes conceptual representations, while the AI terminology concerns an inference component within a system. [7] [8] Editorial interpretation
Includes
- Editorial examples: a conceptual climate description and its numerical simulation fit representation-oriented usage; a trained classifier fits learned inference usage. These are distinct cases under a broad term. [7] [14] Editorial interpretation
- An LLM is one specialized learned model, rather than a synonym for all models. [8] Editorial interpretation
Excludes and counterexamples
- “Any input → output” is insufficient as a universal definition: it omits what the artifact represents and collapses ordinary utilities into model terminology. We retain it only as an internal proposal. [7] [14] Editorial interpretation
- An application containing a model, orchestration and tools is a system; the model name should identify its inference component. [8] [14] Editorial interpretation
Different usages and open boundaries
- In learned-model usage, identify the fitted parameters and inference rule; in conceptual-model usage, identify the referent and assumptions. Do not silently require training for all models. [7] [14] Editorial interpretation
- A fixed formula may be a model when it represents a stated phenomenon. Being deterministic neither establishes nor prevents that role. [7] Editorial interpretation
Operational criteria
Repository test: record the intended sense, represented subject or inference task, assumptions, inputs and outputs if executable, and whether parameters were learned. For learned models, distinguish the checkpoint from deployment and orchestration. [14] Editorial interpretation
Concept relationships
Only is-a means a strict subtype. Uses and supports/orchestrates describe composition and control.
Repository working definition
Repository · Proposed / not certified
Default to a representation or inference component with an explicit subject or task. Use “learned model” when training matters. The broader “any input-to-output mapping” guide remains a pending alternative, not our universal definition. [14]
No consensus decision recorded.
Sources and research gaps
Research notes
Scope gap: NASA’s general representation terminology applies to modeling and simulation; it does not settle every AI use. The EU-U.S. AI meaning and the internal mapping proposal are shown separately.
- Standard for Models and Simulations
NASA. Tier 1 / Institutional technical standard
NASA-STD-7009B, 5 March 2024. Section 3.2, Model on printed page 15; Computational Model and Conceptual Model on page 12. Accessed 2026-10-10.
Context: Models and simulations supporting NASA activities.
Verification: Final B PDF checked. Portal historical Change Date is inconsistent with its B document date; this record follows the PDF cover, not that legacy field.
- EU-U.S. Terminology and Taxonomy for Artificial Intelligence, Second Edition
EU-U.S. Trade and Technology Council, Working Group 1. Tier 1 / Institutional terminology document
Second edition, prepared for the sixth TTC ministerial in 2024. Printed page 12, Model and Large language model; page 8, Machine Learning. Accessed 2026-10-10.
Context: AI-system terminology for transatlantic risk-management and standards cooperation.
Verification: Original text and publication record checked 2026-10-10.
- Repository terminology guide
Repository internal proposal. Tier 4 / Repository internal proposal; not certified
Repository proposal 1.3 / not certified. Term-specific proposal sections and editorial policy. Accessed 2026-10-12.
Context: Internal proposals and editorial interpretations, not external consensus.
Verification: Versions 1.1 and 1.2 add the Schema proposal and the flow/governance experimental example while preserving prior proposal sections. All remain unratified; no adoption decision claimed. Version 1.3 changes branding and certification scope only; it does not certify any proposal.
Revision record
- 1.0 / 2026-10-10
Researched and rewritten with attributed definitions, typed relations and an unratified Repository proposal.
- 1.1 / 2026-10-12
Scope the working definition and certification labels to this repository under Open AI Glossary. Citations and proposed status remain unchanged.