2016/11/30 by Richard P. Mann, Dirk Helbing · 112 citations
Computer Science · Mathematics · Physics and Astronomy · Psychology · Social Sciences · #Artificial intelligence #Collective action #Collective behavior #Collective intelligence #Computer science #Diversity (politics) #Economics #Evolutionary Game Theory and Cooperation #Experimental Behavioral Economics Studies #Face (sociological concept) #Group (periodic table) #Herding #Incentive #Microeconomics #Opinion Dynamics and Social Influence #Political science #Psychology #Social psychology #Sociology #cs.GT #math.DS #stat.AP
paper · pdf · doi:10.1073/pnas.1618722114
published in Proceedings of the National Academy of Sciences 114(20), 5077-5082 (National Academy of Sciences)
openalex publication_date 2017/05/01 · arxiv created 2017/10/17 · arxiv updated 2017/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.