Evolutionary Game Analysis of Construction Worker Safety Supervision Based on Complex Network

To further enhance the management of infrastructure construction projects and safeguard the lives and property of the public, effectively motivating and guiding construction workers' behaviors has become a critical issue in workplace safety. This study constructs a dynamic game model among construction workers using the Newman-Watts small-world network as a framework, based on the evolutionary game theory of complex networks. It systematically analyzes the effects of reward and punishment mechanisms on workers' safe behavior decisions. The results show that reasonable rewards and penalties, dynamic incentive-based compensation systems, and strict supervisory mechanisms can significantly enhance the diffusion of safe behavior. Compared with existing solutions, the proposed model more accurately simulates the evolution of construction workers' safe behavior within complex social networks, providing deeper insights into how reward and punishment mechanisms influence safe behavior decisions. The findings offer theoretical support for construction worker safety supervision and provide practical guidance for formulating more targeted safety management policies and reducing safety risks.