Analytical reasoning task reveals limits of social learning in networks

Social learning -by observing and copying others- is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is our ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of lab-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions, and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an unreflective copying bias, which limits their social learning to the output, rather than the process, of their peers reasoning -even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behavior through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

Corruption Drives the Emergence of Civil Society

Peer punishment of free-riders (defectors) is a key mechanism for promoting cooperation in society. However, it is highly unstable since some cooperators may contribute to a common project but refuse to punish defectors. Centralized sanctioning institutions (for example, tax-funded police and criminal courts) can solve this problem by punishing both defectors and cooperators who refuse to punish. These institutions have been shown to emerge naturally through social learning and then displace all other forms of punishment, including peer punishment. However, this result provokes a number of questions. If centralized sanctioning is so successful, then why do many highly authoritarian states suffer from low levels of cooperation? Why do states with high levels of public good provision tend to rely more on citizen-driven peer punishment? And what happens if centralized institutions can be circumvented by individual acts of bribery? Here, we consider how corruption influences the evolution of cooperation and punishment. Our model shows that the effectiveness of centralized punishment in promoting cooperation breaks down when some actors in the model are allowed to bribe centralized authorities. Counterintuitively, increasing the sanctioning power of the central institution makes things even worse, since this prevents peer punishers from playing a role in maintaining cooperation. As a result, a weaker centralized authority is actually more effective because it allows peer punishment to restore cooperation in the presence of corruption. Our results provide an evolutionary rationale for why public goods provision rarely flourishes in polities that rely only on strong centralized institutions. Instead, cooperation requires both decentralized and centralized enforcement. These results help to explain why citizen participation is a fundamental necessity for policing the commons.

Time Critical Social Mobilization: The DARPA Network Challenge Winning Strategy

It is now commonplace to see the Web as a platform that can harness the collective abilities of large numbers of people to accomplish tasks with unprecedented speed, accuracy and scale. To push this idea to its limit, DARPA launched its Network Challenge, which aimed to explore the roles the Internet and social networking play in the timely communication, wide-area team-building, and urgent mobilization required to solve broad-scope, time-critical problems. The challenge required teams to provide coordinates of ten red weather balloons placed at different locations in the continental United States. This large-scale mobilization required the ability to spread information about the tasks widely and quickly, and to incentivize individuals to act. We report on the winning teams strategy, which utilized a novel recursive incentive mechanism to find all balloons in under nine hours. We analyze the theoretical properties of the mechanism, and present data about its performance in the challenge.