The previous posts have recast scientific objects as perspectival actualisations within structured systems of possibility, and experiments as cuts that force such systems to instantiate particular regions of that space.
If this is how science works, then a familiar expectation quietly dissolves: the expectation that a mature science should eventually close — that its laws, models, or theories might one day exhaust the domain they describe.
This post argues that such closure is not merely unattained, but structurally impossible.
The dream of closure
Scientific progress is often imagined as convergent.
On this picture:
early theories are partial and approximate,
later theories refine them,
and the long arc of inquiry tends toward a complete account of the domain.
Whether this ideal is framed as final laws, a theory of everything, or total predictive power, the underlying aspiration is the same: that the system might one day close over its own possibilities.
This aspiration is understandable.
It is also incompatible with how scientific systems actually function.
Systems as structured potential
To see why, recall the shift already made.
A scientific system is not a catalogue of facts or a collection of objects. It is a structured potential: a specification of what kinds of distinctions, variations, and actualisations are admissible.
Such a system does not enumerate its instances. It constrains them.
And crucially, no system of this kind can fully specify the conditions of its own application.
The reason is not technical. It is relational.
Incompleteness as a feature, not a flaw
This structure will be familiar from formal contexts.
Any sufficiently rich system that can generate instances cannot, from within itself, determine all of the ways it may be instantiated. There will always be further cuts that were not anticipated, further configurations that were not foreseen, further perspectives that were not fixed in advance.
This is not because the system is badly designed, but because to function as a system of possibility at all, it must leave something open.
Closure would not be an achievement. It would be a collapse.
Laws do not totalise
Scientific laws are often treated as candidates for closure.
But on a systems-as-potential account, laws do not totalise domains. They delimit spaces of admissible actualisation.
A law does not say: “this is everything that happens.”
It says: “outside these constraints, nothing can count as an instance of this system.”
This is why laws can be both powerful and revisable, universal and defeasible. Their force lies in what they exclude, not in what they exhaustively list.
Models open as much as they close
The same is true of models.
Models are often praised for their ability to simplify, idealise, and predict. Less often noticed is that they also create new questions.
By stabilising one way of cutting a system, a model simultaneously reveals:
edge cases,
breakdown conditions,
alternative regimes,
and previously unthinkable distinctions.
Every successful model expands the surrounding space of possibility.
Progress, here, does not reduce openness. It redistributes it.
Why closure would end science
A fully closed scientific system would admit no new cuts.
No new experiments would be possible, because every possible instantiation would already be fixed.
No new distinctions would matter, because all relevance would be settled.
No new phenomena could appear, because the space of phenomena would be exhausted.
Such a system would not be complete.
It would be inert.
Science persists precisely because its systems cannot close without ceasing to function as systems of possibility.
The productive openness of science
This structural openness is not a defect to be managed.
It is the engine of scientific change.
Because systems cannot close:
experiments can surprise,
models can fail productively,
laws can be re-cut,
and new domains can emerge.
Scientific revolutions do not occur when closure is finally achieved, but when the limits of a system’s possibility space become visible.
Toward transformation
If scientific systems cannot close, then science cannot be understood as a march toward final truth.
It must be understood as a practice that continually reconfigures the space of what can be actualised and thought.
The next post turns to this directly, asking how science does not merely operate within possibility, but actively transforms it.
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