Prize-based contests can provide solutions to computational biology problems by Karim R Lakhani, et al. (Nature Biotechnology 31, 108–111 (2013) doi:10.1038/nbt.2495)
From the article:
Advances in biotechnology have fueled the generation of unprecedented quantities of data across the life sciences. However, finding analysts who can address such ‘big data’ problems effectively has become a significant research bottleneck. Historically, prize-based contests have had striking success in attracting unconventional individuals who can overcome difficult challenges. To determine whether this approach could solve a real big-data biologic algorithm problem, we used a complex immunogenomics problem as the basis for a two-week online contest broadcast to participants outside academia and biomedical disciplines. Participants in our contest produced over 600 submissions containing 89 novel computational approaches to the problem. Thirty submissions exceeded the benchmark performance of the US National Institutes of Health’s MegaBLAST. The best achieved both greater accuracy and speed (1,000 times greater). Here we show the potential of using online prize-based contests to access individuals without domain-specific backgrounds to address big-data challenges in the life sciences.
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Over the last ten years, online prize-based contest platforms have emerged to solve specific scientific and computational problems for the commercial sector. These platforms, with solvers in the range of tens to hundreds of thousands, have achieved considerable success by exposing thousands of problems to larger numbers of heterogeneous problem-solvers and by appealing to a wide range of motivations to exert effort and create innovative solutions18, 19. The large number of entrants in prize-based contests increases the probability that an ‘extreme-value’ (or maximally performing) solution can be found through multiple independent trials; this is also known as a parallel-search process19. In contrast to traditional approaches, in which experts are predefined and preselected, contest participants self-select to address problems and typically have diverse knowledge, skills and experience that would be virtually impossible to duplicate locally18. Thus, the contest sponsor can identify an appropriate solution by allowing many individuals to participate and observing the best performance. This is particularly useful for highly uncertain innovation problems in which prediction of the best solver or approach may be difficult and the best person to solve one problem may be unsuitable for another19.
An article that merits wider reading that it is likely to get behind a pay-wall.
A semantically diverse universe of potential solvers is more effective than a semantically monotone group of selected experts.
An indicator of what to expect from the monotone logic of the Semantic Web.
Good for scheduling tennis matches with Tim Berners-Lee.
For more complex tasks, rely on semantically diverse groups of humans.
I first saw this at: Solving Big-Data Bottleneck: Scientists Team With Business Innovators to Tackle Research Hurdles.