Optimal incentives for collective intelligence

Mann and Helbing devise a game-theoretic model of collective prediction showing that an antidote to groupthink and conformity is to reward those who have shown accuracy when the majority opinion has been in error:

Significance

Diversity of information and expertise among group members has been identified as a crucial ingredient of collective intelligence. However, many factors tend to reduce the diversity of groups, such as herding, groupthink, and conformity. We show why the individual incentives in financial and prediction markets and the scientific community reduce diversity of information and how these incentives can be changed to improve the accuracy of collective forecasting. Our results, therefore, suggest ways to improve the poor performance of collective forecasting seen in recent political events and how to change career rewards to make scientific research more successful.

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.

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