Macroblog

About Me

Crowdsourcing: A Definition

  • I like to use two definitions for crowdsourcing:

    The White Paper Version: Crowdsourcing is the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call.

    The Soundbyte Version: The application of Open Source principles to fields outside of software.

The Rise of Crowdsourcing

  • Read the original article about crowdsourcing, published in the June, 2006 issue of Wired Magazine.
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May 13, 2008

Comments

Good post, Jeff. But I'm not sure I agree on the difference in importance between the MatLab-case and the AskTheAudience-case. While it would seem that the former can create differences of a much greater magnitude, the latter clearly delivers a usable level of difference/accuracy at much greater speeds. What I miss here is a discussion about how this could be harnessed and utilized. Some of the existing crowdsourcing-plays clearly does this, I'm thinking in particular about cases where human agents have been used to verify images or aid in scanning results. We're often overly impressed by huge improvements, and miss out on the fact that incremental innovation is the real lifesaver for companies.

Hmm, just thought of something. Couldn't Toyota's impressive innovation policy be read as a kind of internal crowdsourcing? See http://www.newyorker.com/talk/financial/2008/05/12/080512ta_talk_surowiecki

Venkat

As a long time MATLAB programmer and fan, let me first say I appreciate your using that example. I think there *is* a big big difference. I could write down a mathematical model of the jellybean or millionaire example in 5 minutes (they are mostly monte carlo type randomized algorithms with some assumptions that will drive reasonable convergence properties), but the Matlab one is a complex social phenomenon. I haven't participated in the contests, but basically the same thing happened to me on a project team, where I spent a couple of days coding a solution to a problem, which seemed perfect, but didn't work because of an obscure bug. When I finally gave up and quit, my friend and project team mate spent just an hour or so on my code and found the bug I'd missed. It was a single misplaced apostrophe :) The code ran beautifully after that.

Modeling the 'many eyeballs/shallow bugs' is hard. There may be things hidden there that get to the 'smarter in practice than in theory' phenomenon. But in general, the explanation usually given for 'unreasonable effectiveness' effects (such as in neural networks) is a combination of fundamental properties (NNs are universal function/patter recognizers) along with the 'future is like the past' statistical assumption (or similar ergodicity conditions). This works for crowds too.

And btw, you're going to have to work harder to get comments :) My current ratio on my blog is hovering around 2.5 per post for about 80 posts, and I know I put in a LOT of thought and effort to drive up the commenting culture of my readers.

Ilkka Peltola

We've been talking a lot recently with Tommi Vilkamo, the head of Nokia Beta Labs, about in what situations prediction markets actually work. I am sure there will be a large number of executives wanting to try this on something it might not suite. I'd like to therefore point out some of our thoughts.
There are "problems" where the solution information is too unevenly spread within the population for prediction to work. In such situations, an "open market" approach produces wrong conclusions, since it will be biased with the opinion that is available to majority, but which does not represent the whole picture. If a piece of critical information is only available to a handful, the prediction will be biased but narrowing the "crowd" too much on the other hand would lose its crowd-characteristic.
For example, imagine a company wanting to predict which of four possible new product concepts will sell most. In such a situation, if the consumer is let to predict, the result will only reflect mass opinion (e.g. "what would be coolest"). What if certain product has a major flaw that is only known to the development team? Even if it was a big company and the prediction was done in-house, the key information might be too unevenly spread.

Another thing that I would like to point out about crowd-problem-solving is, that when the quality of a solution can be instantly measured - as in the MATLAB-example - having a crowd to work on it is extremely powerful. Instead, if the delay of the feedback on a given possible improvement would be significant in contrast to the time span of the whole problem, the crowd loses much of its efficiency. In such cases it would be difficult to identify the current best candidate, which then would spread the resources on different solutions. Also, trial-and-error would not function, so that would leave out much of the script kiddies contributions ;)

Brilliant blog, will drop by frequently!

kraloyun

Another thing that I would like to point out about crowd-problem-solving is, that when the quality of a solution can be instantly measured - as in the MATLAB-example - having a crowd to work on it is extremely powerful. Instead, if the delay of the feedback on a given possible improvement would be significant in contrast to the time span of the whole problem, the crowd loses much of its efficiency. In such cases it would be difficult to identify the current best candidate, which then would spread the resources on different solutions. Also, trial-and-error would not function, so that would leave out much of the script kiddies contributions ;)

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