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?
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
Could you elaborate on that?
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.
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.
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?
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.
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.
face-palm Ah yes. Thanks.