Data-informed modeling of the formation, persistence, and evolution of social norms and conventions
<|reference_start|>Data-informed modeling of the formation, persistence, and evolution of social norms and conventions: Social norms and conventions are commonly accepted and adopted behaviors and practices within a social group that guide interactions -- e.g., how to spell a word or how to greet people -- and are central to a group's culture and identity. Understanding the key mechanisms that govern the formation, persistence, and evolution of social norms and conventions in social communities is a problem of paramount importance for a broad range of real-world applications, spanning from preparedness for future emergencies to promotion of sustainable practices. In the past decades, mathematical modeling has emerged as a powerful tool to reproduce and study the complex dynamics of norm and convention change, gaining insights into their mechanisms, and ultimately deriving tools to predict their evolution. The first goal of this chapter is to introduce some of the main mathematical approaches for modeling social norms and conventions, including population models and agent-based models relying on the theories of dynamical systems, evolutionary dynamics, and game theory. The second goal of the chapter is to illustrate how quantitative observations and empirical data can be incorporated into these mathematical models in a systematic manner, establishing a data-based approach to mathematical modeling of formation, persistence, and evolution of social norms and conventions. Finally, current challenges and future opportunities in this growing field of research are discussed.<|reference_end|>
@article{ye2024data-informed,
    title={Data-informed modeling of the formation, persistence, and evolution of
  social norms and conventions},
    author={Mengbin Ye and Lorenzo Zino},
    journal={arXiv preprint arXiv:2410.06663},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06663},
    primaryClass={cs.SI cs.SY eess.SY math.DS physics.soc-ph}
}