发表机构
Pfizer（辉瑞）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在人工生态中探究继承学习的收益，通过结构化随机控制与更新分配干预，揭示继承偏好价值需与控制设计区分，并强调公平比较方法的重要性。
AI 中文摘要
学习可以改善个体的行为，然而，当个体死亡并被替换时，种群可能会失去这种经验。继承习得的偏好提供了一种跨代保留有用行为的方式，这为人工种群提出了一个问题：继承何时能提高集体性能，以及如何公平地衡量其收益？挑战在于，继承不仅改变后代的行为，还影响生存、繁殖以及进一步遗传更新的机会。具有相同更新幅度的随机控制可能因改变不同状态或受制于不同稳定性约束而产生误导性比较。我们在一个资源有限的人工生态中研究此问题，将结构化随机控制与对新生偏好及遗传更新分配的干预相结合。保留随机更新的状态结构显著缩小了表观继承优势，而条件性的建立速度收益仍然存在。偏好擦除和更快学习的补偿支持了减少后代再学习的贡献。更新分配也改变了比较：事件配额和共同时间截止可以逆转排名，尽管它们也改变了实际更新量。在更新次数和累积幅度匹配的情况下，分阶段释放提高了占用率，但未达到预设的建立标准。这些发现提供了一个框架，用于区分继承偏好的价值与控制设计和更新分配的影响，阐明了在人工种群中应如何评估继承学习。
英文摘要
Learning can improve an individual's behavior, yet a population risks losing that experience whenever individuals die and are replaced. Inheriting learned preferences offers a way to preserve useful behavior across generations, raising a question for artificial populations: when does inheritance improve collective performance, and how can its benefits be measured fairly? The challenge is that inheritance changes not only offspring behavior but also survival, reproduction, and opportunities for further hereditary updates. Random controls with equal update magnitudes may therefore yield misleading comparisons if they alter different states or obey different stability constraints. We investigate this problem in a resource-limited artificial ecology, combining structured random controls with interventions on newborn preferences and the allocation of hereditary updates. Preserving the state structure of random updates substantially narrows the apparent inheritance advantage, while a conditional establishment-speed benefit remains. Preference erasure and faster-learning compensation support a contribution from reduced offspring relearning. Update allocation also changes the comparison: event quotas and common time cutoffs can reverse rankings, although they also change realized update amounts. With update count and cumulative magnitudes matched, staged release improves occupancy but does not achieve the prespecified establishment criterion. These findings provide a framework for distinguishing the value of inherited preferences from the effects of control design and update allocation, clarifying how inherited learning should be evaluated in artificial populations.
