Got it. Yes highlighting and trimming long titles should help with that. Thanks.
386 karma · joined August 13, 2007
Thanks!
Here's the paper "The role of deliberate practice in the acquisition of expertise": http://projects.ict.usc.edu/itw/gel/EricssonDeliberatePracti...
Also see the following: Didier Sornette and Daniel Zajdenweber, The economic return of research: the Pareto law and its implications, European Physical Journal B, 8 (4), 653-664 (1999)
here: http://xxx.lanl.gov/abs/cond-mat/9809366
There's also an excellent (and old) study on whether restaurants with specialized menus did better than restaurants with diverified menus. Turns out the specialized ones did well in the short term, but were vulnerable to sharp changes in taste by patrons. The more diversified ones had much higher longevity but did relatively poorly in the short term. I can't find the reference right now.
Finally, this "Fox vs. Hedgehog" problem is widely studied, and the research shows that the decision is very dependent on the market and domain - i.e it is difficult to generalize.
"You correctly assume that the distribution follows a power-law, but you apply an arbitrary constraint on the maximum ($5bn). You constrained the maximum and then concluded that the distribution is not scalable!
Even if historically VCs haven't had a larger than $5bn exit, there is no reason it won't happen in the future. Unless VCs have some sort of self-imposed upper-bound on exits, this asset class is perfectly scalable as long as VCs have the intestinal fortitude to hang on to some hits for longer than they have in the past. (Also they need to invest in earlier stage startups and diversify more than they think they need to, to benefit even more from scalability). "
Taleb does give credit to them in terms of them understanding the presence of fat tails and focussing on the failure of the gaussian. However, both Bouchaud and Potters, coming from physics backgrounds, believe that the tail-exponent can be calibrated accurately from a finite sample-set. When in fact estimating the tail-exponent (the 'alpha' of a power law process) is fraught with small-sample effects and one cannot reliably make decisions even when you do come up with an estimate of the tail-exponent. This is Taleb's main beef with the breed of physicists with power-law models.
The best paper to understand this is: Weron 2001: "Levy-stable distributions revisited: Tail index > 2 does not exclude the levy-stable regime" http://citeseer.ist.psu.edu/448515.html
More on my website if you're interested: navanitarakeri.com
A Black Swan is not just a rare event - it is defined as a rare event with _high impact_.
Statistical NLP may handle rare occurrences well (are you talking about smoothing?), but, by it's very nature, a rare event in a block of text is not going to wipe out a library now, is it? So the appearance of a rare word in a block of text is hardly in the same class of problems as epidemics, wars, market crashes etc.
No, I'm not just repackaging the results from the APIs. There's significant NLP layered on top of that. It's not even a core offering of the app actually.
W.r.t investments, I did not mean investments into my startup; I was referring to the risk of Twitter (or other API offerer) running out of cash (not being able to raise new rounds) and thus shutting down the API.
I'm more interested in ideas about mitigating the risk, not the probability of it occurring. One possible answer is that it cannot be mitigated.
It has day-by-day descriptions of symptoms etc. I was back to normal faster than expected, but I used certain strategies to help get there.
And if you're a blogger, would you be willing to participate if we were putting out an anthology in your niche?
Lumifi looks interesting, looking into it now.
Thanks.