NSF Grant Investigates the Effect of Group Size on Collective Intelligence

Collaborative Research: Is larger smarter? Investigating the Effect of Group Size on Collective Intelligence
The National Science Foundation
Advanced Cyberinfrastructure Division, Virtual Organizations as Sociotechnical Systems Program
Award Amount: $3,600 | Effective Dates: 10/01/2013 – 09/30/2016 | Award ID: ACI-1322214
Project Personnel: Principal Investigator Christopher Chabris (Psychology)
Project Summary: Building on previous work by the investigators, the project will first develop an online test for collective intelligence. Then it will compare the results of online and face-to-face groups taking this new test with previous results for groups taking an offline version of the test. This will help clarify the degree to which online and off-line groups differ in their general effectiveness on a wide range of different tasks. Next the project will use this test to systematically measure the collective intelligence of online groups that range in size from 2 to 20 people. This will lay the foundation for exploring whether larger online groups can take advantage of the increased resources that more people bring, without suffering as much from the process losses that usually accompany increased group size in face-to-face groups. Finally, the project will systematically measure the collective intelligence of online groups with varying proportions of women. In doing so, the project will also test one particularly promising explanation for a gender effect on group performance: that groups with more women are less interpersonally competitive, and that this lower intra-group competitiveness leads to higher collective intelligence. The work proposed here uses the perspective of collective intelligence to investigate, not just the ability of a group to perform a single task, but the group’s general ability to perform a wide range of tasks. Since many real-world groups must cope with a wide range of problems, just such a perspective may be needed to systematically predict their performance. In addition, the approach developed here can provide a significant economy of effort in evaluating potential ways of improving online group effectiveness. Instead of testing interventions on many different specific tasks, researchers will be able to test the interventions once with this general measure, and then have some basis for predicting the effects of the intervention on many other tasks.