Reading into the landscape

Written quickly and probably not very clear – it’s a workbook post not a polished-final-thoughts post. Vaguely inspired by this exchange between Julia Galef and Michael Nielsen.

One of my favourite things is the point in learning a new topic where it starts to get internalised, and you begin to be able to see more. You can read into a situation where previously you had no idea what was going on.

Sometimes the ‘seeing’ is metaphorical, but sometimes it’s literal. I go walking quite a lot, and this year I’m seeing more than before, thanks to an improved ability to read into the landscape.

I got this from Light and Colour in the Outdoors, a classic 30s book on atmospheric phenomena by the physicist Marcel Minnaert. It’s really good, and I’m now regretting being cheap and getting the Dover version instead of the fancy coffee-table book (note to self: never buy a black-and-white edition of a book with the word ‘colour’ in the title).

I’ve only read a few sections, but already I notice more. Last weekend I got the coach to London, and on the way out I saw a sun dog I’d probably have missed before. And then on the way back it was raining with the sun shining onto the coach windscreen in front, and I thought to myself, ‘I should probably look behind me’. I turned, and right on cue:

This is entry-level reading into the landscape, but still quite satisfying. Those with exceptional abilities seem to have superpowers. George Monbiot in Feral talks about his friend Ritchie Tassell:

… he has an engagement with the natural world so intense that at times it seems almost supernatural. Walking through a wood he will suddenly stop and whisper ‘sparrowhawk’. You look for the bird in vain. He tells you to wait. A couple of minutes later a sparrowhawk flies across the path. He had not seen the bird, nor had he heard it; but he had heard what the other birds were saying: they have different alarm calls for different kinds of threat.

This is the kind of learning that fascinates me! You can do it with maths as well as with sparrowhawks…

This has been on my mind recently as I read/reread Venkatesh Rao’s posts on ambiguity and uncertainty. I really need to do a lot more thinking on this, so this post might look stupid to me rather rapidly, but it’s already helping clarify my thoughts. Rao explains his use of the two terms here:

I like to use the term ambiguity for unclear ontology and uncertainty for unclear epistemology…

The ambiguity versus uncertainty distinction helps you define a simpler, though more restricted, test for whether something is a matter of ontology or epistemology. When you are missing information, that’s uncertainty, and an epistemological matter. When you are lacking an interpretation, that’s ambiguity, and an ontological matter.

Ambiguity is the one that maps to the reading-into-the-landscape sort of learning I’m most fascinated by, and reducing it is an act of fog-clearing:

20/ In decision-making we often use the metaphors of chess (perfect information) and poker (imperfect information) to compare decision-makers.

21/ The fog of intention breaks that metaphor because the game board /rules are inside people’s heads. Even if you see exactly what they see, you won’t see the game they see.

22/ Another way of thinking about this is that they’re making meaning out of what they see differently from you. The world is more legible to them; they can read/write more into it.

I think this is my main way of thinking about learning, and probably accounts for a fair amount of my confusion when interacting with the rationalist community. I’m obsessed with ambiguity-clearing, while the rationalists are strongly uncertainty-oriented.

For example, here’s Julia Galef on evaluating ‘crazy ideas’:

In my experience, rationalists are far more likely to look at that crazy idea and say: “Well, my inside view says that’s dumb. But my outside view says that brilliant ideas often look dumb at first, so the fact that it seems dumb isn’t great evidence about whether it will pan out. And when I think about the EV here [expected value] it seems clearly worth the cost of someone trying it, even if the probability of success is low.”

I’ve never thought like that in my life! I’d be hopeless at the rationalist strategy of finding a difficult, ambitious problem to work on and planning out high-risk steps for how to get there, but luckily there are other ways of navigating. I mostly follow my internal sense of what confusions I have that I might be able to attack, and try to clear a bit of ambiguity-fog at a time.

That sounds annoyingly vague and abstract. I plan to do a concrete maths-example post some time soon. In the meantime, have a picture of a sun dog: