Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Incentivizing free riders improves collective intelligence in social dilemmas


Jacoby,  Nori       
Research Group Computational Auditory Perception, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Supplementary Material (public)
There is no public supplementary material available

Tchernichovski, O., Frey, S., Jacoby, N., & Conley, D. (2023). Incentivizing free riders improves collective intelligence in social dilemmas. Proceedings of the National Academy of Sciences of the United States of America, 120(46): e2311497120. doi:10.1073/pnas.2311497120.

Cite as: https://hdl.handle.net/21.11116/0000-000D-E90F-D
Collective intelligence challenges are often entangled with collective action problems. For example, voting, rating, and social innovation are collective intelligence tasks that require costly individual contributions. As a result, members of a group often free ride on the information contributed by intrinsically motivated people. Are intrinsically motivated agents the best participants in collective decisions? We embedded a collective intelligence task in a large-scale, virtual world public good game and found that participants who joined the information system but were reluctant to contribute to the public good (free riders) provided more accurate evaluations, whereas participants who rated frequently underperformed. Testing the underlying mechanism revealed that a negative rating bias in free riders is associated with higher accuracy. Importantly, incentivizing evaluations amplifies the relative influence of participants who tend to free ride without altering the (higher) quality of their evaluations, thereby improving collective intelligence. These results suggest that many of the currently available information systems, which strongly select for intrinsically motivated participants, underperform and that collective intelligence can benefit from incentivizing free riding members to engage. More generally, enhancing the diversity of contributor motivations can improve collective intelligence in settings that are entangled with collective action problems.