Optimal incentives for collective intelligence

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.collective intelligence | game theory | democracy | diversity | markets T he financial crisis and its aftermath have reopened longstanding debates about the collective wisdom of our societal organizations (1-3). Financial and prediction markets seem unable to foresee major economic and political upheavals, such as the credit crunch or Brexit. This lack of collective foresight could be the result of insufficient diversity among decisionmaking individuals (4). Diversity has been identified as a key ingredient of successful groups across many facets of collective behavior (5-7). It is a crucial condition for collective intelligence (6-10) that can be more important than the intelligence of individuals within a group (11). Because collective behavior ultimately results from individual actions, incentives play a major role for diversity and collective performance (12, 13). Although most previous research has focused on explaining how collective intelligence emerges (14), less is known about how to optimize the wisdom of crowds in a quantitative sense.Harnessing collective wisdom is important. Global systems of communication, governance, trade, and transport grow rapidly in complexity every year. Many of these real world problems have a large number of contributing factors. For example, predicting future economic fluctuations requires integrating knowledge about credit markets and supply chains across the world as well as the ramifications of political developments in different countries and the shifting sentiments of individual investors and consumers. Political developments are themselves the result of many factors: both direct (e.g., political parties' strategies) and indirect (e.g., technological change). Scientific questions are also increasingly complex. For instance, building a complete model of an ecosystem requires bringing together expertise on many scales from individual animal behavior to complex networks of predation and codependency (15). In each case, knowledge about the diverse contributing factors is dispersed. For these highdimensional problems, it is becoming impossible for any single individual or agency to gather and process...

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