Reality; It’s not just about what, but also how we think
Understanding reality: it is not what we thought
A programmatic, meta-theoretical essay introducing links to information on context-sensitive representational architecture written to secondarily motivate its empirical validation. The primary aim is not a total theory, but a structural account of when representation is complete.
With sharp titles come expectations of strong arguments. Potentially long ones. This post is a high-level overview of ongoing work under review and should not be taken as a substitute for the formal argument. This post aims to keep the necessary short version both intuitive and simple, while other places on this site offer detailed technical expressions for critique.
For foundational technical work, see the papers and study proposal referenced here:https://www.dottheory.co.uk/paper/context-sensitive-modelling-in-practiceor explore the site: www.dottheory.co.uk For those interested in these things: a theory of everything, understood as a complete and self-contained description of reality, is structurally unstable. Any such theory must either ignore the contextual conditions that determine its own validity or attempt to include them, in which case it ceases to be closed. The rational alternative pursuit is therefore not a total theory, but a representational architecture that explicitly incorporates contextual structure. That architecture is what Dot theory represents, nothing more.
The short claim
One consequence of Dot theory is the following:
Science, as it currently operates, models reality well in a practical and qualitative sense, but not fully correctly in the sense of computational structural alignment.
These are not the same category.
Science works. It predicts, explains, and builds. But success in practice does not necessarily imply that the underlying unit of representation is fully aligned with the structure of reality.
By analogy:
A map can be accurate in its coordinates and measurements, yet fail to capture how the terrain is experienced in practice. Both are correct, but they describe different levels of alignment.
This work suggests that we are in a similar situation.
The claim
This is not a claim that science fails.
It is a claim that:
science is using the right methods on what is now, due to technological progress, an increasingly mis-specified unit of representation.
A good tool, applied to a slightly mis-specified object.
That is the bold claim. It requires extraordinary evidence. Both the argument and a pathway to that evidence are laid out in the accompanying linked papers and test proposal.
What follows is a consequence of taking that claim seriously.
The shift
Modern science has been extraordinarily successful at modelling systems as if they were objects defined by state.
This success is historically recent and reflects an evolution of earlier observational traditions.
That approach works.
But it carries an assumption that is rarely examined:
that reality, for the purposes of modelling, is sufficiently captured as state.
This work suggests a refinement.
Reality is not fully an object. It is an object under context.
More precisely:
Reality is the realisation of outcomes from a space of possibilities, as constrained by contextual structures that determine admissibility.
Under this formulation:
state alone is not sufficient
context is not auxiliary
admissibility is structural
What this implies
If this is agreed as correct, then something subtle follows.
Science has been modelling reality effectively:
as if it were a well-defined object
and succeeding because:
contextual structure has been implicitly encoded within its methods
If contextual structure is made explicit, then:
the object dissolves into a structured relation
What we have been treating as a thing is more accurately:
a state realised under constraint
The ((un)comfortable) consequence
This places the framework in an unusual position.
It does not say:
science is wrong
models are invalid
It says:
models are often incomplete in a specific, structural sense
They work because they approximate:
Ψ = (ψ, μ)
while often only representing ψ explicitly. As tools improve, disciplines evolve, and data becomes richer, μ increasingly becomes visible and is but inevitable.
The test and the opportunity
This is not a purely philosophical claim and can be practically benefited from by humans, present and future.
It can be tested.
If two systems share the same state ψ, but differ in contextual structure μ, and produce different outcomes, then:
the state-only representation is incomplete
Formally:
∃ μ₁, μ₂ such thatP(O ∣ ψ, μ₁) ≠ P(O ∣ ψ, μ₂)
This is the core empirical question.
Where this leads
If these tests bear out, then the conclusion is not dramatic, but it is deep.
It is not that reality stops being computable.
It is that:
reality is not fully computable as an object, but becomes computable as an object within a contextual structure
More intuitively:
no thing exists in isolation, but only as a thing under conditions
Or, borrowing loosely:
not Ding an Sich, but Ding unter Struktur
When the conditions are not accessible, they appear fixed. When they become accessible, they become computable.
So:
accessibility determines what can be represented, and what can be represented determines what can be computed
In closing
What I believe this train of elaboration suggests is that we, tentatively speaking as science, seem to generally have been looking at reality and writing about it as if it were a stable object. Thinking about it well and beneficially under that assumption, and to great scientific benefit.
This work, suggests that it is that, but never fully just that alone, and instead we can and on evidence ought to representationally explicitly state that:
Reality is always an object under structure.
That structure may not always be visible, but it is always implicated, even within the representation itself.
Realistic computation of reality is therefore not only a matter of modelling state, but of gaining appropriate access to the contextual structures that determine admissibility.
Those structures have limiting and defining features, and those features reflect the use made of them.
The Dot theoretical framework proposes that such systems are best understood as objects that become computable within their own constraints, and become visible as such when compared across different contextual structures.
Progress and discovery then become coherent:
not as the discovery of a fixed object, but the progression of refinement of the structures under which reality becomes admissible as objects relative to the observer.
That is non-trivial. It can be accounted for. It can be tested. And it can be incorporated into our scientific frameworks.
It is therefore, in my view, worth doing.
Thank you for your time and hoping on your support,S.
