创作者经济平台上实现收益最大化的动态流量分配
Dynamic Traffic Allocation for Revenue Maximization on Creator Economy Platform
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中文总结 AI 辅助
该研究针对创作者经济平台的流量分配困境，构建动态优化模型，提出兼具筛选与有序增长的最优策略，发现简单启发式策略会造成25%收益损失，并给出接近最优的实用启发式策略。
中文摘要 AI 辅助
创作者经济平台面临战略困境：将流量分配给成熟明星创作者以获取即时广告收益，还是培育新兴创作者以积累粉丝基础实现未来变现。我们构建连续时间动态优化模型，刻画平台管理拥有广告与直接粉丝贡献双收益流的异质性创作者时的最优流量分配策略，通过分析揭示该策略由前瞻性激活集驱动的“最有价值创作者优先”规则。在Bass扩散动态下，该策略呈现复杂的“条件反转”策略，即平台会暂时优先扶持滞后创作者以利用口碑效应。关于生态系统结构，我们发现最优策略扮演选择性守门人的角色：不同于短视策略导致的残酷“赢家通吃”市场，或朴素公平驱动的启发式策略造成的低效“无差别增长”，最优策略设置严格能力阈值筛选可获得平台流量的创作者，同时对成功进入者实施有序增长，将其粉丝基数限制在最优上限以避免过度投资。最后，我们证明简单启发式策略会导致高达25%的显著收益损失，并提出实用的“粉丝增长调整”启发式策略，该策略利用观测到的增长动量实现接近最优的表现。
英文摘要
Creator economy platforms face a strategic dilemma: allocating traffic to established stars for immediate ad revenue versus nurturing emerging creators to build a follower base for future monetization. We develop a continuous-time dynamic optimization model to characterize the optimal traffic allocation policy for a platform managing heterogeneous creators with dual revenue streams (advertising and direct follower contributions). We characterize the optimal policy analytically, revealing a ``most-valuable-creator-first" rule driven by a forward-looking activation set. Under Bass diffusion dynamics, this policy exhibits a sophisticated ``conditional reversal" strategy, where the platform temporarily prioritizes lagging creators to capitalize on word-of-mouth effects. Regarding the ecosystem structure, we find the optimal policy acts as a selective gatekeeper. Unlike myopic policies that lead to a harsh ``winner-take-all" market, or naive fairness-driven heuristics that foster inefficient ``indiscriminate growth," the optimal policy imposes a strict capability threshold that screens which creators receive platform traffic. Furthermore, it enforces disciplined growth for successful entrants, capping their follower bases at an optimal ceiling to prevent over-investment. Finally, we demonstrate that simple heuristics can lead to significant revenue losses (up to 25\%) and propose a practical ``follower-growth adjusted" heuristic that achieves near-optimal performance by leveraging observed growth momentum.
