治理干预下的平台适配：参与者最优反应建模与外部公开案例基准
Platform Adaptation Under Governance Interventions: Actor Best-Response Modeling and an External Public-Case Benchmark
- The Institute of Energetic Paradigm（活力范式研究所）
- College of Plant Protection, Southwest University（西南大学植物保护学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究构建了平台适配模型，在72个公开案例上验证其适配质量优于多种基线方法，提出了信息系统理论框架以解释平台治理评估失效的原因。
AI中文摘要：
数字平台通过改变规则进行治理，包括排名、货币化阈值、审核标准、验证系统、披露要求、申诉流程及访问政策，这些干预措施极少被被动接受。创作者、卖家、广告商、审核员、用户、开发者及战略运营者会对新的奖励机制做出适配。本文构建了一种平台适配模型，用于将治理干预评估为自适应多参与者信息系统中的过渡过程，该模型可表征参与者最优反应、战略博弈机会、审核负担、用户激励变动、执行响应、外部性形成及下游平台稳定性。我们在72个外部公开平台治理案例上对该模型进行评估，这些案例涵盖媒体货币化、排名系统、验证、配送平台、市场、应用商店、社区平台及创作者生态。在9种方法和648次方法-案例评估中，完整的平台适配模拟器的平均适配质量为0.836338，而风险登记基线为0.669731，因果循环分析为0.589457，通用治理批判为0.492750，仅参与度优化为0.369492，基线政策审查为0.331965。配对比较显示，其相对于所有测试基线及通道消融的胜率为1.00。本研究的贡献在于提出了一种信息系统理论与测量框架，阐明了当平台治理评估将政策规则视为静态控制而非对自适应参与者反应领域的干预时，评估为何会失效。
英文摘要：
Digital platforms govern by changing rules: rankings, monetization thresholds, moderation standards, verification systems, disclosure requirements, appeal processes, and access policies. These interventions are rarely absorbed passively. Creators, sellers, advertisers, moderators, users, developers, and strategic operators adapt to the new reward surface. This paper develops a platform-adaptation model for evaluating governance interventions as transitions in adaptive multi-actor information systems. The model represents actor best response, strategic gaming opportunity, moderation burden, user-incentive movement, enforcement response, externality formation, and downstream platform stability. We evaluate the model on 72 external public platform-governance cases covering media monetization, ranking systems, verification, delivery platforms, marketplaces, app stores, community platforms, and creator ecosystems. Across 9 methods and 648 method-case evaluations, the full platform-adaptation simulator achieves mean adaptation quality of 0.836338, compared with 0.669731 for a risk-register baseline, 0.589457 for causal-loop analysis, 0.492750 for generic governance critique, 0.369492 for engagement-only optimization, and 0.331965 for baseline policy review. Paired comparisons show a win rate of 1.00 against all tested baselines and channel ablations. The contribution is an information-systems theory and measurement framework showing why platform governance evaluation fails when it treats policy rules as static controls rather than interventions into adaptive actor-response fields.
