People often ask me about what kind of problems best leverage the benefits of a collective intelligence (CI) approach. I always say it depends on several factors, but according to my experience I think I am able to advance here seven types of problems or challenges that that can be suitable for open and participatory project with good results:

• Creativity: CI is quite effective at generating ideas. The more people thinking, the more likely they will find a creative solution.

• Bias assessment: Activities those are highly susceptible to selection and assessment biases due to their inherent relativity or spurious interests. CI works well in data interpretation tasks subject to many different perspectives. Opening the analysis to a wide variety of points of view can help reduce the “expert bias” and achieve a more complete and balanced judgment.

• Distributed Surveillance: Activities in which the cost of failure is high. Any errors are best detected if more people are reviewing (Remember Linus’s Law enunciated ​​by Eric Raymond: “Given enough eyeballs, all bugs are shallow“).

• Prediction: Tasks that involve predicting the future or estimating the probability of events, because in principle the large numbers help to lower certain kinds of biases. For example, in response to: What product will be more successful? What technology is most suitable for…? Here I would like to clarify that the CI-based prediction works best as a complement or in combination with expert assessments. A better solution could be that the collective evaluation team is composed of a significant number of “experts“, that is, people who understand the problem well.

• Passion: Activities where enthusiasm and engagement make a difference. Working with highly committed people, with enthusiastic ‘pro-am’ people who are involved only because they are motivated by the challenge, can translate into an influx of quality that is difficult to measure.

• Sense of community: Activities that attract because of its strong collaborative nature and people pointing at them looking to enjoy socializing experiences. In this case what matters are the emotions and finding a sense of belonging. If the project moves through territories that awaken “socializing instincts” (share, talk, show, discuss, teach, share, etc.), it may be a good candidate for the use of collective intelligence.

• Multidisciplinary: Challenges whose solution requires a complex and diverse mix of knowledge inputs. The more multidisciplinary is the problem, the better CI works because the necessary mix of know-how and skills will self-select without leaving out any viewpoint that can add value to the analysis.

Note-1: The image of the post belongs to the album of Shirin Winiger in Flickr.

Note-2: Read this post in Spanish (Lee este post en Español)