Conformity biased transmission in social networks

Area of Science:

• Social network analysis
• Computational social science
• Behavioral economics

Background:

• Understanding how novel behaviors spread through populations is crucial for various fields, including sociology, economics, and public health.
• Previous research has explored the role of social network structure in diffusion processes, but the interplay with individual learning strategies remains less understood.

Purpose of the Study:

• To investigate how population structure and individual learning strategies influence the spread of novel behaviors.
• To analyze the impact of network topology and behavior origin on diffusion dynamics and cultural diversity.

Main Methods:

• Agent-based modeling simulations were conducted on four artificial and three real-world social networks.
• Frequency-dependent learning rules were implemented to model individual behavior adoption.
• Key metrics included the probability of novel behavior spread and the persistence of multiple behavioral variants.

Main Results:

• Network structure showed minimal impact on the overall likelihood of novel behavior spread or cultural diversity.
• The origin location of a novel behavior within a network significantly affected its spread probability.
• This location-dependent effect was modulated by the specific learning strategy employed by individuals.

Conclusions:

• For broad analyses of behavior diffusion, complex social network structures may be less critical than initially assumed.
• Network topology becomes important for more nuanced predictions, particularly when considering the origin of behaviors and diverse learning strategies.
• Future research should focus on the interaction between network position, learning mechanisms, and behavioral diffusion patterns.