AI赋能视角下的直播生态治理：多元主体演化博弈与协同监管机制研究

The Governance of the Live Streaming Ecosystem from the Perspective of AI Empowerment: Research on the Evolutionary Game of Multiple Subjects and the Collaborative Regulatory Mechanism

摘要: 在数字经济转型背景下，AI技术正重塑网络直播行业的治理逻辑。本文聚焦政府、平台与主播三方在AI赋能下的动态博弈关系，构建引入时滞微分机制的三方演化博弈模型，探讨平台响应滞后与主播行为调整之间的演化路径。通过参数校准与仿真模拟，识别“监管–激励–处罚”政策组合在实现系统稳定均衡中的关键作用。研究发现，政府主导的“监管介入–平台响应–主播合规”路径为唯一有效的帕累托最优演化路径，而平台自发治理与主播自觉合规路径则因激励不足难以持续。进一步敏感性分析表明，政府监管收益、平台管理成本与主播合规收益是影响行为演化的核心变量。在此基础上，本文提出构建“AI + 制度”双轮驱动的分级监管体系，优化激励兼容机制，推动直播生态智能化、协同化转型。研究为直播治理体系现代化与平台责任制度建设提供了理论支撑与政策建议。

Abstract: In the context of digital economic transformation, AI technology is reshaping governance mechanisms in the live streaming industry. This study focuses on the dynamic game interactions among governments, platforms, and streamers under AI empowerment. A tripartite evolutionary game model with time delay differential equations is constructed to capture the lag in platform responses and streamer behavior adjustments. Through parameter calibration and simulation analysis, the study identifies that the “regulation-incentive-punishment” policy combination is critical to achieving systemic equilibrium. The results indicate that only the government-driven path—“regulatory intervention, platform response, and streamer compliance”—leads to a Pareto-optimal outcome, while platform-led or streamer-initiated compliance is unsustainable due to insufficient incentives. Sensitivity analysis further reveals that government regulatory benefits, platform management costs, and streamer compliance rewards are the key factors influencing behavioral evolution. Based on these findings, the study proposes a dual-driven regulatory framework integrating AI and institutional design, along with incentive-compatible mechanisms, to advance the intelligent and collaborative transformation of the live streaming ecosystem. This research offers theoretical support and policy insights for the modernization of live streaming governance and platform accountability.