Modeling Group Behavior to Study Innovation Diffusion Based on Cognition and Network: An Analysis for Garbage...

Area of Science:

• Environmental Science
• Sociology
• Computational Social Science

Background:

• Environmental pollution from garbage is a significant global issue.
• Effective waste management systems, like garbage classification, are crucial for environmental sustainability.
• Innovation diffusion models help understand the adoption of new behaviors and technologies.

Purpose of the Study:

• To model the diffusion of innovation in garbage classification systems.
• To analyze the impact of individual bounded rationality and group structure on adoption rates.
• To identify factors influencing the speed and scope of innovation dissemination.

Main Methods:

• Mathematical statistics were used to define individual bounded rationality.
• Scale-free network structures characterized group dynamics.
• A group behavior model was developed and simulated using Python.

Main Results:

• Most individuals adopt garbage classification innovations with general cognitive ability and high-value innovations.
• Enhanced individual cognitive ability and higher innovation value significantly boost diffusion.
• Larger groups experience broader diffusion but require more time; increased interconnections reduce diffusion time.

Conclusions:

• Individual bounded rationality and network structure are key determinants of innovation diffusion in waste management.
• Optimizing cognitive abilities and innovation value can accelerate the adoption of garbage classification.
• Network enhancements can improve the efficiency of spreading environmental innovations like waste sorting.