Pescetelli, Niccolo; Alex Rutherford; Albert Kao and Iyad Rahwan

In a complex digital space—where information is shared without vetting from central authorities and where emotional content, rather than factual veracity, better predicts content spread—individuals often need to learn through experience which news sources to trust and rely on. Although public and experts’ intuition alike call for stronger scrutiny of public information providers, and reliance on global trusted outlets, there is a statistical argument to be made that counter these prescriptions. We consider the scenario in which news statements are used by individuals to achieve a collective payoff—as is the case in many electoral contexts. In this case we find that a plurality of independent, even though less accurate, voices dominates over having fewer but highly accurate information sources. In this carefully controlled experiment, we ask people to make binary forecasts and reward them for their individual or collective performance. We find that when collectively rewarded (compared to when individually rewarded) people learn to rely more on local information cues, a strategy that accrues better collective performance. Importantly, and in accordance with existing collective reinforcement-learning models and the Condorcet theorem, these effects positively scale with group size. These findings show the importance of independent (instead of simply accurate) voices in any information landscape, but particularly when large groups of people want to maximize their collective payoff. Speculatively, these results suggest that, at least statistically speaking, emphasizing collective payoffs in large networks of news end-users might foster resilience to collective information failures.