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Comment author: badger 01 May 2013 11:30:31PM 4 points [-]

See Douglas Knight's comment and my reply. These are more like dimensions, not categories. It makes sense to talk about the distribution on each dimension (i.e. most people tend to be high on the anti-naturalism scale, with long tail on the low end), but everyone has sits somewhere on the dimension.

Comment author: badger 01 May 2013 11:21:42PM *  7 points [-]

Just about to say this. 'Cluster' is very much the wrong word to use to describe the components. A reasonable word would be 'dimension'. Someone can be more or less realist/anti-realist, rationalist/anti-rationalist, externalist/anti-externalist, and each of those dimensions is relatively independent of the others.

The main point of conducting a principal component / factor analysis is dimension reduction. A philosopher can be fairly well described by how strongly they endorse each component rather than keeping track of each individual answer. This is the same math behind the Big 5 personality model.

This confusion seems to be why the post claims the LW-ish position isn't represented. It's not that there is a well-defined group of anti-naturalists that LWers don't fit into; instead, the dimension just happens to be defined with anti-naturalist on the high end. Then, LW roughly endorses being low on each dimension, except maybe externalism and objectivism.

Comment author: badger 19 January 2013 10:00:22PM *  8 points [-]

"90 percent of the variation" is misleading when comparing the levels of one time-series against another. It's very easy to find two time-series that regress almost perfectly on one another because both steadily increase. Looking at first-differences is more informative about possible causal relations. The image Cyan posted is effectively two data points from a first difference perspective: both went up and then both went down.

Another graph from Nevin's website is slightly more persuasive:

There you can see 4-6 corresponding changes in trend. Still not that impressive, but maybe enough to start looking more closely.

Comment author: badger 17 January 2013 03:52:39PM 1 point [-]

Ah, I didn't notice since I could access it through a university connection. I'll edit to note that.

Comment author: badger 09 January 2013 10:36:36PM *  18 points [-]

The list doesn't include anything in the way of game theory, social choice, or mechanism design, which is going to be crucial for an AI that interacts with other agents or tries to aggregate preferences.

Relevant book recommendations (all available at links as pdfs):

[Link] On the Height of a Field

11 badger 02 January 2013 11:20AM

Mark Eichenlaub posted a great little case-study about the difficulty of updating beliefs, even over trivial matters like the slope of a baseball field. The basic story of Bayes-updating assumes the likelihood of evidence in different states is obvious, but feedback between observations and judgments about likelihood quickly complicate the situation:

The story of how belief is supposed to work is that for each bit of evidence, you consider its likelihood under all the various hypotheses, then multiplying these likelihoods, you find your final result, and it tells you exactly how confident you should be. If I can estimate how likely it is for Google Maps and my GPS to corroborate each other given that they are wrong, and how likely it is given that they are right, and then answer the same question for every other bit of evidence available to me, I don’t need to estimate my final beliefs – I calculate them. But even in this simple testbed of the matter of a sloped baseball field, I could feel my biases coming to bear on what evidence I considered, and how strong and relevant that evidence seemed to me.  The more I believed the baseball field was sloped, the more relevant (higher likelihood ratio) it seemed that there was that short steep hill on the side, and the less relevant that my intuition claimed the field was flat. The field even began looking more sloped to me as time went on, and I sometimes thought I could feel the slope as I ran, even though I never had before.

That’s what I was interested in here. I wanted to know more about the way my feelings and beliefs interacted with the evidence and with my methods of collecting it. It is common knowledge that people are likely to find what they’re looking for whatever the facts, but what does it feel like when you’re in the middle of doing this, and can recognizing that feeling lead you to stop?

Edit: Title changed from "An Empirical Evaluation into Runner's High," the original title of the article, to match the author's new title.

Comment author: badger 02 November 2012 05:03:09PM *  5 points [-]

I've heard "three up up up three", which is concise and not easily confused with other operations. If I heard "three threes", I'd interpret that as meaning 9.

Comment author: badger 26 September 2012 04:56:21PM *  1 point [-]

Bug report:

I voted in this poll, and after reloading the page, I don't see the results. Sensibly, it won't let me vote again, but now I'm stuck with the survey form. I did see the results immediately after voting.

Edit: I can view the results now. Not sure what changed.

Comment author: badger 19 September 2012 07:45:58PM 3 points [-]

I'll be there!

Comment author: badger 16 September 2012 03:11:39PM 1 point [-]

Thanks about the links.

Flesch would argue it's largely because there is so little justice in the books. We're interested in tracking others through stories to see who deserves punishment. We remain emotionally involved to see what happens to them, and more injustices mean even more reason to keep tabs on what they're doing. Anticipation of justice is more satisfying than seeing justice itself.

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