研究预测模型与用户行为的反馈循环，揭示真实条件下用户共存的可能。
Evolutionary Prediction Games
- 用进化博弈论建模用户与预测算法的动态互动
- 有限数据下可实现不同用户群体稳定共存
- 适合关注模型公平性与长期生态的从业者
当预测算法服务于多个用户时，预测质量差异可能引发反馈循环：用户因精准预测而增加参与、邀请好友或追随趋势，导致模型与用户群体共同演化。本文提出进化预测游戏框架，基于进化博弈论将此类反馈循环建模为群体间的自然选择过程。理论分析表明，在理想无限数据与算力条件下，重复学习会引发竞争并导致竞争排斥；但在有限数据、计算资源受限或过拟合风险存在等现实约束下，我们证明不同用户群体可实现稳定共存甚至互利共生。文中分析了其稳定性与可行性，提出了维持共存的机制，并通过实证验证了结论。
原文摘要 · Abstract (English)
When a prediction algorithm serves a collection of users, disparities in prediction quality are likely to emerge. If users respond to accurate predictions by increasing engagement, inviting friends, or adopting trends, repeated learning creates a feedback loop that shapes both the model and the population of its users. In this work, we introduce evolutionary prediction games, a framework grounded in evolutionary game theory which models such feedback loops as natural-selection processes among groups of users. Our theoretical analysis reveals a gap between idealized and real-world learning settings: In idealized settings with unlimited data and computational power, repeated learning creates competition and promotes competitive exclusion across a broad class of behavioral dynamics. However, under realistic constraints such as finite data, limited compute, or risk of overfitting, we show that stable coexistence and mutualistic symbiosis between groups becomes possible. We analyze these possibilities in terms of their stability and feasibility, present mechanisms that can sustain their existence, and empirically demonstrate our findings.
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