Data-informed modeling of the formation, persistence, and evolution of social norms and conventions

Abstract: 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.

• The paper presents a range of mathematical approaches, including population and agent-based models, to capture the dynamics of social norm adoption and stability.
• The paper integrates empirical data to validate tipping points and threshold effects, enhancing the realism of theoretical models with observed behavioral patterns.
• The paper outlines future directions such as incorporating machine learning and cyber-physical-human systems to improve predictions and guide societal interventions.

Overview of "Data-informed Modeling of the Formation, Persistence, and Evolution of Social Norms and Conventions"

The paper, authored by Mengbin Ye and Lorenzo Zino, explores mathematical modeling approaches for understanding the dynamics of social norms and conventions. It underscores the significance of these constructs in shaping behaviors within social groups and addresses how mathematical models, augmented by empirical data, can elucidate their formation, stability, and transformation over time.

Mathematical Modeling of Social Dynamics

The paper delineates several mathematical approaches to modeling social norms:

1. Population Models: These models describe population-wide dynamics of norm adoption. The Bass diffusion model is highlighted for its application in explaining S-shaped adoption curves via innovation and imitation mechanisms. While these models offer analytical tractability, they overlook individual-level interaction intricacies.
2. Agent-Based Models (ABMs): ABMs address the limitations of population models by incorporating heterogeneous agent interactions on networked structures. These models capture complex contagion dynamics, where influence is nonlinear and context-dependent. The paper categorizes agent-based models into:
   • Threshold Models: Implement linear threshold rules where an individual's adoption depends on the proportion of adopting neighbors.
   • Evolutionary Dynamics Models: Characterize social conventions through competition and adaptation on networks.
   • Game-Theoretic Models: Emphasize strategic decision-making influenced by payoffs that encapsulate conformity, inertia, and trends.

Integrating Data into Models

Data plays a pivotal role in refining these models:

• Threshold Models: Validation with empirical data helps determine realistic thresholds for individuals, recognizing the importance of network structures in shaping adoption dynamics.
• Experimental Evidence: Studies validate the presence of tipping points in social conventions, highlighting the significance of a critical mass for societal change.
• Psychological Factors: Experiments reveal nuances such as inertia and trend sensitivity, underscoring the need for their incorporation in game-theoretic models for realistic predictions.

Implications and Future Directions

The paper discusses the critical role mathematical models play in predicting and potentially controlling social norm dynamics, with significant implications for guiding societal changes towards sustainability and resilience. It suggests a few paths for future work:

• Enhanced Data Access: There's a call for high-quality, individual-level data to calibrate and validate models accurately, moving beyond broad population-level observations.
• Machine Learning Integration: Machine learning techniques, like physics-informed neural networks and universal differential equations, present opportunities to augment traditional modeling frameworks.
• Cyber-Physical-Human Systems: The integration of social dynamics within cyber-physical systems could illuminate insights into socio-technical interactions, offering a comprehensive understanding of complex, technology-influenced societies.

In conclusion, the chapter provides a structured overview of the mathematical underpinnings and empirical integration needed to model the complex landscape of social norms, paving the way for interdisciplinary collaborations to enhance the real-world applicability of these models.