The emergence and sustenance of cooperative behavior is fundamental for a society to thrive. Recent experimental studies have shown that cooperation increases in dynamic networks in which subjects can choose their partners. However, these studies did not vary reputational knowledge, or what subjects know about other's past actions, which has long been recognized as an important factor in supporting cooperation. They also did not give subjects access to global social knowledge, or information on who is connected to whom in the group. As a result, it remained unknown how reputational and social knowledge foster cooperative behavior in dynamic networks both independently and by complementing each other. In an experimental setting, we show that global reputational knowledge is crucial to sustaining a high level of cooperation and welfare. Cooperation is associated with the emergence of dense and clustered networks with highly cooperative hubs. Global social knowledge has no effect on the aggregate level of cooperation. A community analysis shows that the addition of global social knowledge to global reputational knowledge affects the distribution of cooperative activity: cooperators form a separate community that achieves a higher cooperation level than the community of defectors. Members of the community of cooperators achieve a higher payoff from interactions within the community than members of the less cooperative community.

Conley and Johnson (1) make a significant contribution to how the social sciences need to advance research on COVID-19. The authors suggest that studies on COVID-19 could learn from other similar one-time events to better understand causality and hence generalizability: Researchers need to be creative in their research design, due to the uniqueness of COVID-19, by constructing datasets that contain the same variables as the one-time event or studying possible effects over longer time periods. These approaches aim to study events that have occurred and examine their causal effects.

Letter in response to: Past is future for the era of COVID-19 research in the social sciences, (24 March 2021, Proceedings of the National Academy of Sciences)

Reply to Velu and Iyer: The promise and limits of “near-miss” pandemic-related research, (5 October 2021, Proceedings of the National Academy of Sciences, volume 118, number 40.)

This paper provides experimental evidence on the economic determinants of intermediation networks by considering two pricing rules – respectively criticality and betweenness – and three group sizes of subjects – 10, 50 and 100 subjects. We find that when brokerage benefits accrue only to traders who lie on all paths of intermediation, stable networks involve interconnected cycles, and trading path lengths grow while linking and payoff inequality remain modest as the number of traders grows. By contrast, when brokerage benefits are equally distributed among traders on the shortest paths, stable networks contain a few hubs that provide the vast majority of links, and trading path lengths remain unchanged while linking and payoff inequality explode as the number of traders grows.