Optimal incentives for collective intelligence

TL;DR

This paper examines how incentive structures influence diversity and collective intelligence, revealing that traditional market incentives cause herding, while a new scheme rewarding minority accuracy enhances group performance.

Contribution

It introduces a novel incentive scheme that promotes diversity and improves collective prediction accuracy, addressing limitations of market-based incentives.

Findings

Market incentives cause herding and reduce diversity.

Rewarding minority accurate predictions enhances collective intelligence.

Proposed scheme outperforms traditional incentives in simulations.

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. We investigate the role incentives play in maintaining useful diversity through an evolutionary game-theoretic model of collective prediction. We show that market-based incentive systems produce herding effects, reduce information available to the group and suppress collective intelligence. In response, we propose a new 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 demonstrated accuracy when majority opinion has been in error.