Cooperation, clustering, and assortative mixing in dynamic networks

Area of Science:

• Social Sciences
• Behavioral Economics
• Network Science

Background:

• Human cooperation is fundamentally linked to social networks.
• Network clustering, where individuals form tightly connected groups, is a key factor promoting cooperation by protecting conditional cooperators.
• Dynamic networks, allowing for relationship changes, can facilitate the emergence of cooperation clusters.

Purpose of the Study:

• To disentangle the effects of network dynamics and reputation on cooperation.
• To investigate how network clustering and dynamics influence cooperation in a large-scale experiment.
• To examine the impact of individual partner choices versus a single group decision on cooperation rates.

Main Methods:

• A large-scale experiment (n=2,675) with participants in static or dynamic networks, which were either clustered or random.
• Inclusion of varying levels of reputational information.
• Comparison of cooperation rates under individual partner choice versus a single group decision.

Main Results:

• Initial network clustering predicted cooperation in static networks but not dynamic ones.
• Reputation influenced partner choice, but cooperation levels were primarily driven by network dynamics.
• When participants made individual choices, cooperation rates in static networks matched those in dynamic networks.

Conclusions:

• Network dynamics, particularly when allowing for individual partner choices, are crucial for sustained cooperation.
• Structured social relations, enabled by individual decision-making, are as effective as dynamic networks in promoting cooperation.
• Future research should consider realistic individual choices when studying cooperation in networks.