Mann, RP orcid.org/0000-0003-0701-1274 and Helbing, D (2017) Optimal incentives for collective intelligence. Proceedings of the National Academy of Sciences, 114 (20). pp. 5077-5082. ISSN 0027-8424
Abstract
Collective intelligence is the ability of a group to perform more effectively than any individual alone. Diversity among group members is a key condition for the emergence of collective intelligence, but maintaining diversity is challenging in the face of social pressure to imitate one’s peers. Through an evolutionary game-theoretic model of collective prediction, we investigate the role that incentives may play in maintaining useful diversity. We show that market-based incentive systems produce herding effects, reduce information available to the group, and restrain collective intelligence. Therefore, we propose an incentive scheme that rewards accurate minority predictions and show that this produces optimal diversity and collective predictive accuracy. We conclude that real world systems should reward those who have shown accuracy when the majority opinion has been in error.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | (c) 2017, National Academy of Sciences . This is an author produced version of a paper published in Proceedings of the National Academy of Sciences. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | collective intelligence; game theory; democracy; diversity; markets |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Pure Mathematics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Apr 2017 11:09 |
Last Modified: | 11 Sep 2020 08:39 |
Status: | Published |
Publisher: | National Academy of Sciences |
Identification Number: | 10.1073/pnas.1618722114 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:115700 |