One-shot learning via strong categorization in the economy

The economy is able to adapt to novel conditions extremely quickly. If the conditions of supply and demand change in some historically novel way, or if a new technology, good, or service is introduced to the economy, the economy nevertheless usually adjusts quickly. For example, if there’s a supply shock, the economy rapidly engages in a sequence where prices rise, quantity demanded falls, and substitution occurs. The economy behaves as if it understands the shock without ever needing to represent it in some model. Instead, the appearance of understanding comes from the rapid construction of a strong category that successfully manages the situation.

Strong categories enable one-shot learning because they’re simply a constraint structure that organizes behavior. Weak categories describe the world—this is a dog, that is a cat, etc.—but strong categories format interactions with it.

In the economy, categories are instantiated as price signals most obviously, but also as institutional norms and practices, legal conventions, etc. These things all function to reduce novel details into something familiar.

Prices provide a particularly good explanation for how novel events are quickly categorized. The basic function of a price as an order parameter for a market is to reduce high-dimensional novelty into a low-dimensional actionable variable. That is to say, when something new happens, economic agents don’t need to understand or even know anything about the novelty because the effects of the novel event are transmitted as updates to the price system. So to deal with the novel event, economic agents just need to deal with the things they’re already familiar with, like relative prices, budget constraints, and incentives.

So when something new happens, like a new technology, the economy is able to rapidly one-shot learn how to deal with because it doesn’t need to think about the new event and compare it to various concepts until a good match hopefully results. The economy instead just immediately embeds the new event into existing constraints, such as prices. Thus, the economy achieves one-shot learning by forcing novelty into preexisting action spaces, an outcome that arises from the natural dynamics of the economy.

The mystery of one-shot learning is how rapid generalization is possible with very little data. The solution is to realize that generalization isn’t required. The economy doesn’t infer the category from a set of examples; the category scaffolding is already present. The economy “learns” by reparameterizing its internal flows, not by update a weak internal model. There’s no need to represent novelty when you can just absorb it into the price system.

Sometimes the economy fails to learn quickly. This happens when the processes that produce strong categorization break down. When externalities are present, the economy by definition does not succeed in collapsing novelty into price adjustments. A failure to learn is the result of flawed categorization structures. The economy can only learn what its categories make visible.

In short, strong categorization enables one-shot learning because it formats novelty into a familiar, working structure. This is exactly what a category does: it gets rid of the extraneous details to prepare salient perception-action-cognition. But whereas weak categories are things you have to think about, strong categories emerge naturally from the dynamics of the system. The economy is able to behave as if it generalizes rapidly from minimal data because it never actually needed to generalize in the first place. Generalization is an artifact of weak categorization. One-shot learning isn’t really about speed; it’s about structure.

Moreover, this should be true of all systems, not just the economy, that run on cognitive glues that function as constraint-imposing mediums that collapse degrees of freedom in a task-relevant (action-coupled) way. Systems incapable of one-shot generalization probably are that way because they lack such a medium.