CONSISTENT PATTERNS IN LARGE - SCALE COLLABORATION - Collaborative Computational Technologies for Biomedical Research - page 100

magnitude, surely we are poised for a positive revolution based on facile col-
laboration? Yet we are scientists, and a good dose of data is always healthy to
calibrate our expectations and help us set strategy. In this chapter we will see
that large-scale voluntary collaboration systems show remarkably consistent
patterns in contributors' behavior within scientifi c collaborations in the phar-
maceutical industry and extending to every other company and industry exam-
ined. This behavior has all the signatures of a power law, driven by positive
feedback from the individual and the group, and with a “long tail” such that
approximately half of all contributions come from people who contribute only
once to any given campaign. Interestingly the evidence also suggests that
networks of acquaintanceship are not an essential driving force, which makes
us revise our concept of “community.” Finally we review the data, not just for
collaborative idea generation, but for collaborative evaluation and decision
making, and see that the most popular methods are prone to strong bias by
minority views.

6.2 BACKGROUND

From late 2005 to 2010 I created and then managed the “Idea Farm,” an
online collaborative problem-solving system within Pfi zer, the world's largest
pharmaceutical fi rm. The underlying model was the campaign, or challenge,
in which a business need is identifi ed with a specifi c sponsor, the problem or
opportunity is reframed for the online medium, then in the “diverge” phase
broadcast (usually via e-mail) to a large diverse audience who then may
contribute using an easy-to-use system designed to support the challenge
model [2]. In the subsequent “converge” phase, the entries are collected,
organized, built upon (by the crowd and/or an assigned review team), evalu-
ated, and trimmed and decisions made on implementation. The challenge
model also underpins Innocentive, DARPA challenges (e.g., robot vehicles
crossing the desert), X-Prizes, the Netfl ix Prize, and many more.* Arguably
the fi rst and most successful challenge was the longitude problem, in which
the late-eighteenth-century Parliament and the Admiralty sponsored an
apparently impossible problem to which John Harrison, an unknown clock-
maker from the north of England, dedicated his life; he won by inventing the
marine chronometer [3]. If we take innovation to consist of inspiration (the
stereotypical “aha”), followed by invention (the proof of concept), followed
by implementation (the scaling of the invention and dissemination to its cus-
tomers), there is no question that implementation is the most lengthy, costly,
and likely to fail [4]. The challenge model succeeds because it addresses this
at the outset by selecting only those challenges where a serious need is
matched by a serious and specifi c individual who already has the mandate

* See http://www.darpa.mil/grandchallenge/index.asp , http://www.xprize.org/ , and http://www.

netfl ixprize.com/ for examples.