The Economist on crowds

The Economist has a couple of interesting recent articles about crowds. This should hardly be a surprise, given that economics is all about the aggregate behavior of large numbers of people.

The first article deals with crowdsourcing, its benefits and limitations. It mentions several more interesting crowdsourcing examples I forgot (even though I read the article before I wrote that post), including the 1714 longitude prize and Google's initiative to have volunteers in India carry around GPS units to help map India's roads.

It also wonders whether crowdsourcing and business are a good match. There are plenty of examples of volunteers joining in a project for fun and the chance to be part of something important, but this seems inherently hard to monetize. People don't seem so keen on volunteering their efforts so someone else can make money. The counterexample would be the various prize competitions, including the longitude prize and the various X prizes, but in those cases the participants are in it to be part of something important and to make themselves money.

As an aside, if you're interested in the general topic of crowdsourcing before the web, I'd recommend Longitude, by Dava Sobel, and Simon Winchester's The Meaning of Everything, about the Oxford English Dictionary.

The second article doesn't explicitly mention the wisdom of crowds, but cites a study finding that "when individual drivers each try to choose the quickest route it can cause delays for others and even increase hold-ups in the entire road network." Interestingly enough, closing down sections of road can actually make things run faster, not too surprising if you consider that taking a little-used side street generally involves merging back onto the main road at some point. That will tend to gum up the main road, but if the side street saves you enough time you won't particularly care.

This is yet another example of a "local optimum," a fundamental problem with "dumb is smarter" approaches. Deliberately ignoring the big, complex picture and concentrating on small, local features can lead to solutions that look good on the small scale but bad on the large scale. This is another of those lessons we get to learn as many times as we like. Genetic algorithms provide another fairly recent example. The "no free lunch" theorem is also worth keeping in mind.