There is a recurring temptation when solving difficult problems: decide what kind of problem we're looking at as quickly as possible.
Sometimes that's exactly what we should do. A server is down, a pipe is leaking or a deadline is approaching. We know enough to act, and additional reflection may simply increase the cost of waiting.
Other problems punish early certainty.
We encounter a few examples, notice a pattern and build a rule around it. The rule works well enough that we stop examining the assumptions underneath it. Then an unusual case arrives and we have to decide whether the case is wrong, the rule is incomplete or the situation has changed.
I've often described this as premature closure, although even that phrase carries a bias. It assumes that remaining open would have been better.
Sometimes closure is exactly what makes action possible.
The harder question is when to commit and what kind of commitment the available evidence has earned.
That distinction matters because decisions don't all close possibility in the same way. Some are cheap to reverse. Others create dependencies, expectations or physical changes that make returning difficult. Choosing a restaurant with incomplete information is different from pouring a foundation with incomplete engineering. Publishing a tentative interpretation is different from writing that interpretation into a policy thousands of people will have to follow.
So uncertainty alone doesn't tell us to wait.
We also need to know the cost of waiting and the cost of being wrong.
This is one place where I've become more cautious about carrying lessons between domains. Overfitting in machine learning, premature optimization in software, dogma in ethics and impulsive decisions in personal life can all look superficially similar because each involves some relationship between limited information and a committed response.
That resemblance can generate a useful question.
It doesn't establish that they're the same failure mode.
Each domain has different evidence, different consequences and different ways of correcting mistakes. An optimization can be benchmarked. A technical design can sometimes be rolled back. An ethical decision may affect another person in a way no later correction can fully undo.
The analogy gets us to the question.
The domain has to answer it.
This also changes how I think about coherence. I've sometimes described coherence as though it were a universal behavior through which systems maintain themselves while adapting. That definition is attractive because it travels easily. It can be applied to organizations, software, conversations, beliefs and living things.
Traveling easily isn't evidence that the concept explains all of them.
A simpler use of coherence is enough for most of what I actually need from it. Something is coherent when its parts make enough sense together for the purpose we're examining. A software architecture can be coherent because its components have understandable responsibilities. An explanation can be coherent because its claims don't contradict one another. An organization can have coherent procedures because people can understand how decisions move through it.
Those are related uses of a word, not necessarily manifestations of one underlying process.
The interesting part begins when coherence encounters change.
A design that made sense under one workload may become awkward under another. A procedure built for five employees may fail with fifty. An explanation that fit the available evidence may encounter evidence it can't accommodate.
Now we have choices.
We can preserve the existing structure and treat the disturbance as noise. We can modify the structure. We can replace it. Sometimes we discover that the apparent contradiction came from misunderstanding the new information and nothing needed to change at all.
Deviation doesn't tell us which response is correct.
It gives us something to investigate.
That is one reason const