Comment author: Stuart_Armstrong 11 March 2013 06:45:17PM 2 points [-]

If you have a strong discount factor, then even if you meet the same person infinitely often, your gain is still bounded above (summing a geometric series), and can be much smaller than winning your current round.

Comment author: Crystalist 11 March 2013 06:53:48PM 2 points [-]

face-palm Ah yes. Thanks.

Comment author: Stuart_Armstrong 11 March 2013 06:13:28PM 4 points [-]

The discount factor can mess things up - you'll meet someone again, but after how long?

Comment author: Crystalist 11 March 2013 06:36:09PM 0 points [-]

I'm not sure I see your point. My reasoning was that if you meet the same person on average every thousand games in an infinite series of games, you'll end up meeting them an infinite number of times. Am I confusing the sample space with the event space?

In response to comment by shminux on Memes?
Comment author: [deleted] 23 September 2012 01:41:53AM *  1 point [-]

There's certainly mathematical models of rumors, which is a similar enough but not quite the same concept.

From memory, they model similar to epidemics, which I'm not sure how that related to genetic drift and selection.

In response to comment by [deleted] on Memes?
Comment author: Crystalist 23 September 2012 02:16:37AM 1 point [-]

I seem to remember more elaborate techniques that I think were trying to capture genetic drift and selection, but I can't find them at the moment.

A quick google along the lines of "mathematical model meme propagation" does tend to pop up quite a few models. Here are two that seemed interesting: http://cogprints.org/531/1/mav.htm and http://cfpm.org/jom-emit/2000/vol4/kendal_jr&laland_kn.html

In response to Memes?
Comment author: jimrandomh 23 September 2012 01:07:35AM 6 points [-]

"Meme" is not a model, it's a reference class of models, many of which are informal. In order to talk about testing it, you must first zoom in.

In response to comment by jimrandomh on Memes?
Comment author: Crystalist 23 September 2012 02:02:33AM 2 points [-]

Could you elaborate on that?

Comment author: badger 22 August 2012 10:05:18PM 17 points [-]

This sounds like a map/territory confusion. "Intelligence" is a concept in the map, used to summarize the common correlations in success across domains. There is no assumption that fully general cross-domain optimizers exist; it's an empirical observation that most of the variance in performance across cognitive tasks happens along a single dimension. Contrast this with personality, where most of the variance is along five dimensions. We could talk about how each person reacts in each possible situation or "island", but most of this information can be compressed into five numbers.

We could always drill down and talk about more factors, ie fluid vs crystallized intelligence or math vs verbal. More factors gives us more predictive power, though additional factors are increasingly less useful when chosen well.

Though a single-factor model works well for humans, this isn't necessarily the case for more general minds. I suspect the broad concept of intelligence carves reality at its joints fairly well, but assuming so would be a mistake.

Comment author: Crystalist 28 August 2012 09:59:05PM 0 points [-]

Thanks for this! I've really found it helpful.

I suppose part of my confusion came from reading in Eyesenck about the alarmingly large number of geniuses that scored as prodigies, but over a longitudinal study, ended up living unhappy lives in janitor-level jobs. Eyesenck deals with this by discussing correlations between intelligence and some more negative personality traits, but I would have expected great enough intelligence to invent routines to compensate for that. In any case, I think this points to my further being confused about how 'success' was being defined.

I'm also puzzled at the apparent disconnect between solving problems in one's own life and solving problems on paper.

Comment author: Crystalist 28 August 2012 09:43:30PM 1 point [-]

The citations in this comment are new science, so please take them with at least a cellar of salt:

There are recent studies, especially into Wernicke's area, which seem to implicate alternate areas for linguistic processing : http://explore.georgetown.edu/news/?ID=61864&PageTemplateID=295 (they don't cite the actual study, but I think it might be here http://www.pnas.org/content/109/8/E505.full#xref-ref-48-1); and this study (http://brain.oxfordjournals.org/content/124/1/83.full) is also interesting.

Terrence Deacon's 'The Symbolic Species' also argumes that Broca's area is not as constant across individuals as the other subsections being discussed are; interpretations of Broca's area in particular are shaky (argues Deacon) because this region is immediately adjacent to the motor controls for the equipment needed to produce speech. I have seen no studies attempting to falsify this claim, though, so unless anyone knows of actual evidence for it, we can safely shuffle this one into the realm of hypothesis for now.

In any case, Wernicke's and Broca's areas may not be the best examples of specialization in brain regions; I think we have a much clearer understanding (as these things go) of the sensory processing areas.

Comment author: Crystalist 07 August 2012 12:47:18PM 7 points [-]

In my own experience, self skepticism isn't sufficient. It's bloody useful of course, but it's also an exceptional time sink -- occasionally to the point where I'll forget to actually think of solutions to the problem.

Does anyone have any algorithms they use to balance self-skepticism with actually solving the problem?

Comment author: Crystalist 03 August 2012 09:57:07AM 2 points [-]

Hi all,

Long time lurker, first time poster. I've read some of the Sequences, though I fully intend to re-read and read on.

I'm an undergrad at present, looking to participate in a trend I've been observing that's bring some of the rigor and predictive power of the hard sciences to linguistics.

I'm particularly interested in how language evolved, and under what physical/biological/computational constraints; What that implies about the neural mechanisms behind human behavior; and how to use those two to construct a predictive and quantitative theory of linguistic behavior.

I go to a Liberal Arts college (I started out with a bit more of a Lit major bent), where, after being disillusioned with the somewhat more philosophical side of linguistics (mid-term, no less), I ended up taking an extracurricular dive into the physical sciences just to stay sane. Then a friend recomended HPMOR, and thence I discovered LessWrong, where I've been happily lurking for some time.

I decided it would be useful to actually participate. So here I am.

Comment author: [deleted] 28 July 2012 04:20:29AM *  1 point [-]

Yes, but OTOH the “evolutionary processes operating over centuries, millennia, and longer” took place in environments different from where we live nowadays.

Comment author: Crystalist 03 August 2012 09:25:50AM 0 points [-]

I think, more to the point is the question of what functions the evolutionary processes were computing. Those instincts did not evolve to provide insight into truth, they evolved to maximize reproductive fitness. Certainly these aren't mutually exclusive goals, but to a certain extent, that difference in function is why we have cognitive biases in the first place.

Obviously that's an over simplification, but my point is that if we know something has gone wrong, and that there's conflict between an intelligent person's conclusions and the intuitions we've evolved, the high probability that the flaw' is in the intelligent person's argument depends on whether that instinct in some way produced more babies than it's competitors.

This may or may not significantly decrease the probability distribution on expected errors assigned earlier, but I think it's worth considering.