Proc. Natl Acad. Sci. USA (2017)

Much has been said about how collective intelligence emerges in a group — be it a flock of birds or a scientific community — but recent events have highlighted our inability to optimize this wisdom. In particular, existing theories have proven insufficient for forecasting key financial and political changes. And the reason behind it may be a lack of diversity, which is known to be essential for the emergence of collective intelligence. Now, Richard Mann and Dirk Helbing have looked at how diversity might be enhanced in complex systems, and built a case for favouring minority viewpoints.

Mann and Helbing constructed an evolutionary model of collective prediction based on game theory, and built in an incentive scheme rewarding agents for making accurate, if unpopular, predictions. The model gave rise to an optimally diverse system capable of making shrewd collective decisions and predictions. The idea that accurate minority opinions should take precedence over wayward majority views may apply to scientific rewards structures, the authors argue, as favouring conformity by funding low-risk studies compromises diversity.