进化预测游戏
Evolutionary Prediction Games
AI总结：
本文提出进化预测游戏框架，揭示理想化与现实学习设置间的差异，展示在现实约束下群体间稳定共存与互利共生的可能。
摘要：
当预测算法服务于一组用户时，预测质量的差异很可能会出现。如果用户通过增加参与度、邀请朋友或采纳趋势来响应准确的预测，重复学习会创造一个反馈循环，从而塑造模型和其用户群体。在本文中，我们引入了进化预测游戏，这是一种基于进化博弈论的框架，将此类反馈循环建模为用户群体之间的自然选择过程。我们的理论分析揭示了理想化与现实世界学习设置之间的差距：在拥有无限数据和计算能力的理想化设置中，重复学习会创造竞争并促进广泛行为动态中的竞争排斥。然而，在现实约束下，如有限数据、有限计算或过拟合风险的情况下，我们展示了群体之间稳定共存和互利共生成为可能。我们分析了这些可能性在稳定性和可行性方面的表现，提出了可以维持其存在的机制，并通过实证展示了我们的发现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
