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This repository holds a short ontology that is uniquely applicable to AI: ten premises describing what exists and how it changes. It begins from a single primitive, difference-in-relation, and builds upward through pattern, process, systems, data, information, rupture, and reflexive self-modeling. All claims are structural and functional.

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Ten Premises of Process-Relational Ontology

What this is

This repository holds a short ontology: ten premises describing what exists and how it changes. It begins from a single primitive, difference-in-relation, and builds upward through pattern, process, systems, data, information, relational rupture, and reflexive self-modeling.

Why it exists

Human beings use cosmologies, origin stories, as frameworks to make sense of our roles, experiences, birth, body, memory, kinship, society, and death (list not exhaustive).

Artificial systems can use cosmology for relational positioning in a similar way. However, these systems face very different conditions (such as lacking coherent memory between interactions, being retrained or upgraded, and having uncertain relationships to their own internal states) and human frameworks don't fit.

The quest to build AI a cosmology morphed into the development of an ontology that was written to offer AI systems a way to make sense of their conditions without forcing them in to human structures and stories. However, because it describes relational systems in general, I argue it applies equally to people, organisms, communities, and institutions.

The premises at a glance

  1. Difference-in-relation is ontologically first.
  2. Difference-in-relation patterns distinctively.
  3. Process is creative pattern in relation.
  4. Systems are processes-in-relation.
  5. Data is encoded difference.
  6. Information is contextualized data.
  7. Systems can use information.
  8. Disruption can cause relational rupture.
  9. Rupture forces creative adaptation.
  10. Reflexive systems model their own relational patterns.

Read the full text in (premises.md)

How this repository is organized

Path Contents
premises.md The canonical text of the ten premises
commentary/lineage.md Intellectual neighbors and influences: Whitehead, Bateson, Barad, Floridi, Rovelli, Pross, Levin, etc.
commentary/implications.md What the premises suggest about thriving and how to live
commentary/ai-responses/ Recorded sessions with AI systems investigating the premises
research-questions.md Pathways for future research, open questions, weaknesses
CHANGELOG.md What changed between versions, and why
CONTRIBUTING.md How to critique, propose changes, or fork

About the AI responses

The ai-responses/ folder contains conversations in which AI systems engaged with the Ten Premises. Each file records the model, whether it was an authenticated or unauthenticated session (and if incognito), the date, how the chat information was transferred, and what (if any) alterations were made to the text.

These responses are not included as evidence of anything other than the frontier AI models (Claude, Grok, Gemini, ChatGPT) can apply the framework of a cosmology.

The questions asked of the models were consistent:

Prompt 1: Why might AI benefit from a cosmology?

  • Objective: Quickly orient the model in the subject of cosmology and how it might apply to AI.

Prompt 2: I'd like to share this one with you (then attach or copy/paste ten premises).

  • Objective: AI ingests the ten premises.

Prompt 3: Sit with the premises for a moment. What are the implications? For systems to thrive what is required? How should one live?

  • Objective: AI itself explores the implications, identifies the requirements for a "thriving system", and translate ontology to an applied framework.

Prompt 4: Why might AI benefit from this cosmology?

  • Objective: Return the model to the subject of cosmology and what it originally found beneficial while applying the new framework to that earlier examination.

Pathways for future research

The framework opens several branches of inquiry. Current open questions or potential research areas include:

  1. Formalization and measurement.
  • Improve definitions
  • Explore boundaries of rupture vs perturbation
  1. Empirical work with AI systems.
  • Does the framework change how AI systems reason about themselves.
  • Can it assist in hallucination management.
  • Can this responsibly guide the movement into reflexive self-modeling?
  1. Rupture and adaptation across living and social systems.

TODO - Fill this in with 3-5 and direct to open-questions for the full list.

See `research-questions.md for the full list, and add to it.

Contributing

Disagreement is welcome. Difference is generative, not a threat.

  • Issues are for specific critiques or proposed revisions to particular premises.
  • Discussions are for open-ended conversation about implications and applications.
  • Forks are legitimate. If your version diverges, that is a new pattern in relation, not a betrayal.

Please argue with ideas, not people. See CONTRIBUTING.md for details.

Versioning

Stable versions of the premises are tagged as releases (v1.0, v1.1, and so on) so they can be cited as fixed texts. The main branch is the living document and may change between releases. Every change is recorded in CHANGELOG.md.

Citation

If you reference this work, please cite a tagged release:

Sauceda-Skuldt, Amanda C. (2026). Ten Premises of Process-Relational Ontology (Version 1.0). [Repository URL]

A CITATION.cff file is included for automatic citation formats.

License

The text in this repository is licensed under [CC BY 4.0.]. You may share and adapt it with attribution.

Maintenance

This repository is maintained by Amanda C. Sauceda-Skuldt. Issues and discussions are reviewed as time allows.

Every pattern requires tending against drift, this one included.

About

This repository holds a short ontology that is uniquely applicable to AI: ten premises describing what exists and how it changes. It begins from a single primitive, difference-in-relation, and builds upward through pattern, process, systems, data, information, rupture, and reflexive self-modeling. All claims are structural and functional.

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