CCK0809

Wikipedia isn't written and edited by the "crowd" at all. In fact, 1% of Wikipedia users are responsible for half of the site's edits. Even Wikipedia's founder, Jimmy Wales, has been quoted as saying that the site is really written by a community, "a dedicated group of a few hundred volunteers."

I think your headline is misleading and Vassilis Kostakos should read the book before poking holes. Surowiecki is very clear about the conditions necessary for a wise crowd to prevail and those conditions are: 1. Diversity of opinion 2. Independence 3. Decentralization 4. Aggregation If your crowd possesses those qualities then it is wise and then it will be better at making decisions under Surowiecki's paradigm. The crowds used in the research (and the crowd in general) doesn't possess those qualities and therefore is an unfit data set. We should be trying to create the ideal crowd before we can obtain superlative results and not try to get good results from any random crowd.

Limitations in predictions market are well documented (and include Muhammad's points above), and constrain their practical application to a well-defined number of situation. Crowdsourcing suffers from the same limitations, which is not a problem, as long as you limit its application correspondingly. The problem occur when you stretch it outside the required constraints and yet present the results as "scientific", i.e. as a good proxy for what the crowd thinks. That's what professor Vassilis Kostakos's theory ultimately comes down to (or should - I don't know, I haven't read his report). Apps like Digg or Amazon's review are not scientific applications of crowdsourcing, and thus their results should not be seen as precise representation of our collective thinking.

how they differ

I believe that the underlying facts about the Wikipedia phenomenon -- that the general public is actually intelligent, interested in sharing knowledge, interested in getting the facts straight -- are so shocking to most old media people that it is literally impossible for them to report on Wikipedia without following a storyline that goes something like this: "Yeah, this was a crazy thing that worked for awhile, but eventually they will see the light and realize that top-down control is the only thing that works."

At the heart of scientific thinking has to be a strong desire not to fool yourself, coupled by an understanding of how to actually put that desire into practice.

this emerging scientific culture that bizarrely believes that if you can produce a model that fits the data that inspired you to build the model, you've actually shown that your model accurately captures the system. This culture floods the scientific literature with zero-impact papers, dazzles the computationally naïve, captures a lot of air time in the news.

The computer models can be dazzling, but unless they produce a demonstrated string of successes that end up changing the way everyone in the field thinks - the molecular biologists, the sociologists, the economists, then the sciences of complexity will be dismissed as unfruitful. In the end, your model has to inspire a someone to pick up a pipette and design an experiment.