Reminds me of "The Cactus and the Weasel".

The philosopher Isaiah Berlin originally proposed a (tongue-in-cheek) classification of people into "hedgehogs", who have a single big theory that explains everything and view the world in that light, and "foxes", who have a large number of smaller theories that they use to explain parts of the world. Later on, the psychologist Philip Tetlock found that people who were closer to the "fox" end of the spectrum tended to be better at predicting future events than the "hedgehogs".

In "The Cactus and the Weasel", Venkat constructs an elaborate hypothesis of the kinds of belief structures that "foxes" and "hedgehogs" have and how they work, talking about how a belief can be grounded in a small number of fundamental elements (typical for hedgehogs) or in an intricate web of other beliefs (typical for foxes). The whole essay is worth reading, but a few excerpts that are related to what you just wrote:

Where does [the fox prediction advantage], let’s call it the Tetlock edge, come from? I have a speculative answer.

It comes from eschewing abstraction and preferring the unreliable world of System 1 tools: metaphor, analogy and narrative; tools that all depend on pattern recognition of one sort or the other, rather than classification into clean schema. Fox brains are in effect constantly doing meta-analyses with unstructured ensembles, rather than projecting from abstract models.

That’s where the advantage comes from: eschewing abstraction.

Abstraction creates meta-knowledge via inductive generalization, and can grow into doctrinaire world views. The way this happens is that you try to formalize the interdependencies among all your generalized beliefs. Your one big idea as a hedgehog is an idea that covers everything, the whole T-box, so to speak. Abstraction provides you with ways to compute beliefs and actions in domains you haven’t even encountered yet, thereby coloring your judgment of the novel before the fact.

Pattern recognition creates meta-knowledge through linkages among weak views in multiple domains. The many things you know start getting densely connected in a messy web of ad hoc associations. Your collection of little ideas, densely connected, does not cover everything, since there are fewer abstractions. So you can only form beliefs about new domains once you encounter some data about them (which means you have an inclusion bias). And you cannot act decisively in those domains, since you lack strong metanorms. This means pattern recognition leaves you with a fundamentally more open mind (or less strongly colored preconceptions about what you do not yet know).

The way you slowly gain a Tetlock advantage, if you live long enough to collect a lot of examples and a very densely connected mind full of little ideas, is as follows: The more you see instances of a belief in various guises, the better you get at recognizing new instances. This is because the chances that a new instance will be recognizable close to an existing instance in your collection increases, and also because patterns color the unknown less strongly than abstractions.

As you age, your mind becomes a vessel for accumulating a growing global context to aid in the appreciation of novelty.

Abstraction offers you a satisfyingly consistent and clean world view, but since you generally stop collecting new instances (and might even discard ones you have) once you have enough to form an abstract belief through inductive generalization, it is harder to make any real use of new information as it comes in. There is already a strongly colored opinion in place and guides to action that don’t rely on knowing things. Your abstractions also accumulate metanorms, and give you an increasing array of reasons to not include new information in your world view. [...]

Foxes are fundamentally Big Data native people. They operate on the assumption that it is cheaper to store new information than to decide what to do with it. Hedgehogs are fundamentally not Big Data native. If they can’t structure it, they can’t store it, and have to throw it away. If they can structure it with an abstraction, they don’t need to store most of it. Only a few critical details to fit the Procrustean bed of their abstraction.

Because foxes resist the temptation of abstraction (and therefore the temptation to throw away examples of patterns once an inductive generalization and/or metanorm has been arrived at, or stop collecting), they slowly gains an advantage over time, as the data accumulates: the Tetlock edge.

We can restate the Archilocus definition in a geeky way: The fox has one big, unstructured dataset, the hedgehog has many small structured datasets.

But this takes a long time and a lot of stamp collecting, and foxes have to learn to survive in the meantime. Young foxes can be particularly intimidated by old hedgehogs, since the latter are likely to have accumulated more data in absolute terms.

On th other hand, you can imagine data-driven epistemology, where you don't really formulate any hypotheses. You just have a lot of pattern-matching power, completely agnostic of the knowledge domain

When you say "pattern-matching," what do you mean? Because when I imagine pattern-matching, I imagine that one has a library of patterns, which are matched against sensory data- and those library of patterns are the 'hypotheses.'

But where does this library come from? It seems to be something along the lines of "if you see it once, store it as a pattern, and increase the relevance as you see it more times / decrease or delete if you don't see it enough" which looks like an approximation to "consider all hypotheses, updating their probability upward when you see them and try to keep total probability roughly balanced."

That is, I think we agree; but I think when we use phrases like "pattern-matching" it helps to be explicit about what we're talking about. Distinguishing between patterns and hypotheses is dangerous!