categories

“There is nothing natural about classes, families and orders, the so-called systems are artificial conventions.” — Jean-Baptiste Lamarck

“Whatever you might say the object ‘is,’ well it is not.” — Alfred Korzybski

All categories are an imperfect depiction of reality. The truth always lies in the concrete.

Create abstractions from the concrete, to the degree they are useful for the task at hand.

There is probably no dead matter anywhere, only minimally active matter and lazy observers.

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Let’s also abandon the view that there are “just metaphoric ways of speaking” and then there are real scientific explanations. Everything is a metaphor – all we have are metaphors, some of which are better than others at helping us get to the next, more empirically interesting and generative metaphor. There are few to none inherently bad metaphors that we can detect from a philosophical armchair as errors that run afoul of some dusty old category; all we have are metaphors that facilitate (or hold back) discovery to various degrees, and categories that flexibly change with the science.

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Ask: Does it agree with observations? Does it make any testable predictions? Who cares about it? What function does it serve? Where does it break down? Do we have a better candidate model?

Of course, there is some subjectivity in almost all science, and natural categories cannot always be ‘defined’. In a biological theory about hearts it might not be possible to define where a heart stops and the rest of an organism starts, or whether an organ in an organism is a heart or not. Any particular (subjective) classification of hearts might cause results to favour one theory over another. That said, over time, widespread experimentation smooths out the effects of this subjectivity. And the theories that ultimately prosper are those that explain the highest proportion of (everyone’s) empirical observations.

The art of being wise, is the art of konwing what to overlook.

Noticing patterns of similarity, knowing what to overlook and what to put into the same category.

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The more data you have, the less you want to use inductive biases.

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Abstract vs general vs category hierarchy vs abstraction layer

Concrete → Abstract: Which? → What kind?
Specific → General: Narrow kind→ Broad kind

Specific → General operates within the space of categories.
Intension … defining properties of a category (what it is)
Extension … the set of things that fall under that category
Shrinking the intension → expanding the extension (and vice versa)

	Specific	General
Abstract	Int	Num
Concrete	42 :: Int a ⇒ a	42 :: Num a ⇒ a

Category hierarchy: A is-a B is-a C … requires only shared properties (e.g.: tabby $\subset$ cat $\subset$ mammal)
Abstraction layer: hides a mechanism, exposes an interface … requires near-decomposability (e.g.: the structure of reality)

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