Open thread, Nov. 10 - Nov. 16, 2014

MrMind

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When I was trying to make sense of Peter Watts' Echopraxia it has occurred to me that there may be two vastly different but both viable kinds of epistemology.

First is the classical hypothesis-driven epistemology, promoted by positivists and Popper, and generalized by Bayesian epistemology and Solomonoff induction. In the most general version, you have to come up with a set of hypotheses with assigned probabilities, and look for information that would change the entropy of this set the most. It's a good idea. It formalizes what is science, and what is not; it provides the framework for research, and, given the infinite amount of computing power on a hypercomputer, extract the theoretical maximum of utility from sensory information. The main problem is that it doesn't an algorithmic way to come up with hypotheses, and the suggestion to test infinitely many of them (aleph-1, as far as I can tell) isn't very helpful either.

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, and you use it to try to find any regularities, predictability, clustering, etc. in the sensory data. Then you just check if any of the discovered knowledge is useful. That barely (if at all) can distinguish correlation and causation, that does not really distinguish scientific and non-scientific beliefs, and it doesn't even guarantee that the findings will be meaningful. However, it does work algorithmically, even with finite resources.

They actually go together rather nice, with data-driven epistemology being the source of hypotheses for the hypothesis-driven epistemology. However, Watts seems to be arguing that given enough computing power, you'd be better off spending it on data-driven pattern matching than on generating and testing hypotheses. And since brains are generally good at pattern matching, System 1, slightly tweaked with yet-to-be-invented technologies, can potentially vastly outperform System 2 running hypothesis-driven epistemology. I wonder to which extent it may actually be true.

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 ... (read more)

9Kaj_Sotala12y

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: [...]