The problem is that the popularity of products in a networked economy follows the power law:
http://www.aspeninstitute.org/policy-work/communications-soc...You might recognize the power distribution curve, as it's the same as the graph of the long tail: http://en.wikipedia.org/wiki/Power_law#mediaviewer/File:Long...
What happens is that you have a handful of people who make a LOT of money, a small few who do OK, and the vast, vast majority starve. Since our economy is increasingly becoming an online, globally networked one, these effects are becoming stronger, and are a contributor to economic inequality.
Power Law phenomenon on the net has been observed for ages. Here's a post from 2003 where Kottke notes the distribution in the popularity of blogs on Technorati: http://kottke.org/03/02/weblogs-and-power-laws
I imagine the curve fits similarly for things like app store rankings, Reddit and Hacker News post popularities, top Steam sellers, Amazon rankings, etc.
> The world still rewards value, even if it takes some time. The people clamoring that it doesn't are doing so because they want to believe that it isn't their fault they didn't succeed.
The funny thing is, this is not entirely true. Quality is only important up to a certain threshold, after which you're at the mercy of what are essentially chaotic network effects early in the lifecycle of your product.
Salgankik, Dodds, and Watts performed an experiment that begins to provide
some empirical support for this intuition [359]. They created a music download site,
populated with 48 obscure songs of varying quality written by actual performing groups.
Visitors to the site were presented with a list of the songs and given the opportunity to
listen to them. Each visitor was also shown a table listing the current “download count” for
each song — the number of times it had been downloaded from the site thus far. At the end
of a session, the visitor was given the opportunity to download copies of the songs that he
or she liked.
Now, unbeknownst to the visitors, upon arrival they were actually being assigned at
random to one of eight “parallel” copies of the site. The parallel copies started out identically,
with the same songs and with each song having a download count of zero. However, each
parallel copy then evolved differently as users arrived. In a controlled, small-scale setting,
then, this experiment provided a way to observe what happens to the popularities of 48 songs
when you get to run history forward eight different times. And in fact, it was found that the
“market share” of the different songs varied considerably across the different parallel copies,
although the best songs never ended up at the bottom and the worst songs never ended up
at the top.
Salganik et al. also used this approach to show that, overall, feedback produced greater
inequality in outcomes. Specifically, they assigned some users to a ninth version of the site
in which no feedback about download counts was provided at all. In this version of the
site, there was no direct opportunity for users to contribute to rich-get-richer dynamics, and
indeed, there was significantly less variation in the market share of different songs.
There are clear implications for popularity in less controlled environments, parallel to
some of the conclusions we’ve drawn from our models — specifically, that the future success
of a book, movie, celebrity, or Web site is strongly influenced by these types of feedback
effects, and hence may to some extent be inherently unpredictable.
http://www.cs.cornell.edu/home/kleinber/networks-book/networ...