Incentivizing free riders improves collective intelligence in social dilemmas

Abstract

Collective intelligence challenges are often entangled with collective action problems. For example, voting, rating, and social innovation are collective intelligence tasks that require costly individual contributions. As a result, members of a group often free ride on the information contributed by intrinsically motivated people. Are intrinsically motivated agents the best participants in collective decisions? We embedded a collective intelligence task in a large-scale, virtual world public good game and found that participants who joined the information system but were reluctant to contribute to the public good (free riders) provided more accurate evaluations, whereas participants who rated frequently underperformed. Testing the underlying mechanism revealed that a negative rating bias in free riders is associated with higher accuracy. Importantly, incentivizing evaluations amplifies the relative influence of participants who tend to free ride without altering the (higher) quality of their evaluations, thereby improving collective intelligence. These results suggest that many of the currently available information systems, which strongly select for intrinsically motivated participants, underperform and that collective intelligence can benefit from incentivizing free riding members to engage. More generally, enhancing the diversity of contributor motivations can improve collective intelligence in settings that are entangled with collective action problems.

Tracing the mechanisms of cultural diversity through 2.5 million individuals’ music listening patterns

“…Nevertheless, the advent of online experimental platforms and advanced recruitment methods now enables large-scale and cross-cultural studies. These technologies enable experiments beyond traditional paradigms by incorporating complex topological designs in the form of iterative transmissions [61][62][63][64][65] and studying behaviour in artificial worlds 66,67 that are closer to real-world scenarios, proving to be promising avenues. In this respect, our large observational analyses provide important insights about real-world behaviour that can go in tandem with future experimental approaches in answering questions about the general mechanisms underlying cultural evolution and transmission.…”