PROCEEDINGS, CI2012 MANAGING THE SOCIAL DYNAMICS OF COLLECTIVE INTELLIGENCE

Successful instances of collective intelligence are created by those who set clear goals, develop effective process models, and clarify leadership. This talk presents collective intelligence strategies to guide research and practice. Lowering the cost and barriers to collaboration opens up numerous possibilities for collective intelligence and action. These exciting possibilities are rapidly changing political organization, scientific research, scholarly projects, disaster response, citizen science, medical practice, and economic development. The growing understanding of what strategies are appropriate in each context promises to improve the success rate of future projects. The intellectual challenge is substantial since traditional replicable controlled experimental techniques and reductionist models are less relevant in large dynamic communities. The rich variety of contexts and interaction among factors (e.g. community size, diversity, goals, history, etc.) produces a challenge to traditional methods. However, the good news is that novel ethnographic and case study methods, coupled with social media data analyses [Hansen et al. 2011] can produce evidence to validate hypotheses, support theories, and guide community managers. As research on collective intelligence matures, the appearance of mature process models facilitates research and practice. Innocentive’s [2011] description of its methods or the Social Discovery Framework [Table 1, Shneiderman 2011] clarify temporal sequencing of stages for initiating requests, creating capacity, and seeking solutions. Malone et al. [Table 2, 2011] genome approach invites community managers to describe what, who, why, and how. The Reader-to-Leader Framework [Figure 1, 2009] suggests typical paths as community participants become more engaged. A detailed study of scientific collaborations led to a useful taxonomy of contexts and goals [Table 3, Bos et al. 2007]