Females boost collective intelligence more than men, study finds 

Having more women in a team or group can boost the overall ‘collective intelligence’ for decision making, when compared to a male dominated group, study reveals. 

Researchers from Pennsylvania‘s Carnegie Mellon University examined 22 studies covering 5,349 individuals engaged in group and individual activities.

The team found that individual skill, group gender composition, and group collaboration were all predictors of collective intelligence, or the ability of a group to work together and solve a range of problems that vary in complexity. 

The research, that involved running machine learning algorithms over multiple large sets of data, revealed that the success of a group activity could be better predicted using collective intelligence measures than on individual member skill.

These collective measures included social perceptiveness of individual members, group composition, particularly female proportion and age diversity, and group size.

Having more women in a team or group can boost the overall ‘collective intelligence’ for decision making, when compared to a male dominated group, study reveals. Stock image

In order to address issues ranging from climate change to developing complex technologies and curing diseases, science relies on collective intelligence.

The data demonstrated that group collaboration processes were about twice as important for predicting CI than individual skill.

They found that group composition, including the proportion of women in a group and group member social perceptiveness, are also significant predictors of CI. 

‘This paper introduces some computational metrics for evaluating collaboration processes that could be foundational for studying collaboration moving forward,’ said Anita Williams Woolley, study co-author.

Williams Woolley added that they continue to find that having more women in the group raises the overall level of collective intelligence. 

The team discovered that this improvement in collection intelligence applied regardless of whether the collaboration was face-to-face or online. 

In earlier research, the same team found that a group’s ability to perform a wide range of tasks could be predicted by a single statistical factor – that is where they came up with the concept of collective intelligence. 

In follow up work, by this team and others, experts found that this CI factor was weakly linked to individual intelligence levels, but strongly linked to social sensitivity and the number of women in any group. 

For this new study, using machine learning techniques, Woolley and colleagues determined the relative importance of different variables for predicting CI.

They observed that group collaboration process measures were about twice as important as individual member skill.

Other important predictors were social perceptiveness, group composition (particularly female proportion and age diversity) and group size.

The team discovered that this improvement in collection intelligence applied regardless of whether the collaboration was face-to-face or online. Stock image

The research advances the science of collective performance both conceptually and methodologically. 

By using a metric of collective intelligence based on a variety of tasks, a group’s score should predict future performance in a broader range of settings. 

The authors say that by having a robust measure of a group’s capability to work together can help managers assemble groups for high collective intelligence.

The findings have been published in the journal Proceedings of the National Academy of Sciences.