Abstract
A collaborative group can often outperform a single individual in complex problem solving, even when information is limited. This phenomenon, called collective intelligence, can be achieved by engineering a central planner who assigns subtasks distributed across the group. But such algorithms cannot explain how natural populations, which often lack sophisticated central control, can nonetheless evolve collective intelligence. In fact, the process of social learning by imitating successful peers will typically reduce diversity and inhibit collective intelligence. Here, we consider a prediction task where the true outcome each round is a continuous quantity that depends linearly on a large number of random causal factors. Each individual can observe only one factor, and the collective prediction is generated by aggregating personal predictions across individuals. We propose two classes of reward structures that guarantee the emergence of collective intelligence through social learning. One scheme provides greater rewards to those individuals (called experts) whose personal predictions are more accurate. The other scheme provides greater rewards to those individuals (called reformers) whose predictions have greater potential to reduce the collective error, even though their personal predictions may be far from the truth. Although both of these payoff structures can provably maintain diversity and establish collective intelligence, we show that rewards based on collective error are more robust to diverse problem settings than rewards based on personal accuracy. Our results show that identifying reformers is more effective than identifying experts in promoting the emergence of collective intelligence.