Dynamic Social Proof Modeling: From Static Cues to Adaptive Influence Systems

While Pei et al. (2024) began exploring dynamic aspects of social proof in epidemiology, and Liu et al. (2025) proposed LLM-based wargaming, this research develops comprehensive dynamic models of social proof influence in digital environments. Unlike existing studies that test social proof as a one-time intervention (Velten, 2017; Keizer, 2017), we propose modeling social proof as a complex adaptive system where influence effects cascade, decay, and interact over time. Using network analysis and machine learning, we would identify optimal timing and sequencing of social proof interventions, creating a framework for understanding influence as a process rather than a stimulus. This approach could resolve why social proof works in some contexts but not others (Schneider et al., 2023) by accounting for temporal dynamics and network effects.

References:

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