The Universal & Atomic Elements of Organization
A falsifiable scientific theory proposing that the same irreducible structures organize both thought and reality — from the quantum to the social scale.
O-Theory proposes that four irreducible structures — Distinctions, Systems, Relationships and Perspectives (DSRP) — form the universal grammar of organization. Rather than being merely useful ways of thinking, these structures are hypothesized to be the atomic elements from which every organized phenomenon emerges, whether in cognition, biology, physics, mathematics, society, or the cosmos.
| D | S | R | P |
| Distinctions | Systems | Relationships | Perspectives |
| Identity ↔ Other | Part ↔ Whole | Action ↔ Reaction | Point ↔ View |
| D := (i ↔ o) | S := (p ↔ w) | R := (a ↔ r) | P := (ṗ ↔ v) |
This is a living scientific evidence compendium: an open, continually evolving collection of independent empirical research, formal theory, mathematical proofs, cross-disciplinary analyses, applications, critiques, and proposed falsifications. Every entry is included because it supports, refines, challenges, or attempts to falsify the theory.
Scientific theories are strengthened not only by evidence that confirms their predictions, but also by surviving attempts to falsify them. This compendium brings both together: independent evidence from researchers who were not testing DSRP and proposed counterexamples evaluated against the formal theory.
One counterexample is enough to falsify O-Theory. Until then, the question remains: do the same four structures organize everything from quantum systems to human thought?
Independent convergence is one of the strongest forms of scientific evidence because researchers arrive at the same conclusion while investigating different questions for different reasons.
The number is not the point. Researchers in different fields, studying different questions with different methods, repeatedly arrived at the same structural predictions—almost always without testing DSRP or using its language.
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This compendium began as the peer-reviewed literature review “A Literature Review of the Universal and Atomic Elements of Complex Cognition,” published in the Journal of Systems Thinking with 109 studies. That paper is the peer-reviewed foundation. What you see here is its living, continuously updated version . New studies are checked before they are added, and the collection now holds and keeps growing. Open any card to see what the researchers found, why it bears on DSRP, and where the original review discusses it, the fuller account.
The collection is updated continuously, so the citation carries the date you consulted it rather than a study count — the count changes weekly, and putting it in the reference would make the same collection look like a different work to everyone who cites it. To cite a single claim or study, use its own address: every one has a permanent link.
This is the adversarial half of the compendium. Where the evidence track asks what converges on DSRP, this one asks what would end it: a single organized phenomenon whose structure needs a fifth pattern, a ninth element, or a fifth structural dynamic. It holds written up from candidates across territories, and resolutions — the general answers those cases settle against. Every case is published whether it held or failed, including the ones still open.
Cite this rather than the evidence collection when the point is what survived attack. The two are separate works with separate addresses: one asks what converges on the theory, the other asks what would end it, and a reference to the first does not support a claim about the second. Case and resolution numbers change when the record is revised, so cite a case by its own permanent link rather than by number.
This collection is meant to keep growing. Send us a study, paper, book or critique that bears on DSRP, whether it supports the theory or cuts against it, and we will read it and decide whether it belongs.
Authors Prill, Patrick
Year 2026
Publisher Test Pappy
Kind of work press
Discipline Computer Science & AI
Secondary disciplines Systems Science
Kind of engagement public
Applied false
To argue that what makes an AI prompt useful is the same thing that makes an engineering decision sound — a description of the surrounding system — and to use the Cabreras' VMCL model to say what shared context human colleagues hold that an AI does not.
A blog post built on a worked contrast. Prill opens from an Elisabeth Hendrickson remark on context as the cumulative effect of past decisions that cannot now be changed, inverts it to ask what context is needed for good future decisions, runs a 'Context Scan,' and demonstrates with a pair of prompts: a vague one asking how to structure an authentication module, and one describing platform, team and constraints.
Prill concludes that missing context makes AI output generic and locally optimised, that front-loading a description of the system saves iteration time, that experienced engineers already gather context before deciding so AI use should follow the same practice, and that agents should be set up to request more context before starting. He maps repeated unhelpful correction cycles with an AI onto the systems-thinking 'fixes that fail' pattern. The support is reasoning plus one illustrative example, not data.
A software tester applies VMCL to the problem of AI prompting, using it to characterise the shared organizational context a human colleague holds and a model does not, and builds his prompt checklist from boundaries, elements, relations and perspectives.
Prill uses 'the Vision, Mission, Capacity, Learning model from Drs. Cabrera' to name what a colleague inside an organization already knows and an AI does not: the vision it is working toward, the mission it repeats, the capacity it has, and what it has learned. That asymmetry is his explanation for why a prompt that would be enough for a teammate is not enough for a model. He then gives the dimensions of context worth supplying in a technical prompt as boundaries, elements, relations and perspectives — the four patterns in substance, though he does not name DSRP in this post. The demonstration is a before-and-after pair: 'How should I structure my Auth module?' against a prompt that states the platform, the team and the constraints, with the second yielding a far more relevant answer. He argues that a component cannot be understood apart from the system around it, and reads the loop of repeated AI corrections as the 'fixes that fail' archetype, where each local repair leaves the underlying absence of context in place.
The use of VMCL is accurate and does real work here: the model is applied as an account of what an organization holds in common, which is what it is for, rather than as a planning template. Prill's prompt checklist is the four patterns arriving under other names — boundaries for distinctions, elements for part-whole, relations for relationships, and perspectives as given — which is a reasonable independent route to the same four and worth noting as such. Because he does not name DSRP in this post, the connection between his checklist and the framework he names in his other writing is left for the reader to make.
Patterns it shows D, S, R, P
Added 2026-10-19
How to cite this Prill, Patrick (2026). Context is the System – What AI Prompting and Systems Thinking Have in Common. Test Pappy.