基于Q学习的合作演化：我们需要多少信息？
Evolution of cooperation with Q-learning: how much information do we need?
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中文总结 AI 辅助
该研究针对合作演化的信息依赖问题，采用Q学习算法结合结构化种群模型，发现合作水平随邻域大小呈倒U型变化，证实适量信息才是合作涌现的最优条件。
中文摘要 AI 辅助
合作在自然和人类社会中普遍存在，但其演化基础仍是重大挑战。一个长期存在的谜题是：拥有更多信息是否会带来更好的决策，进而实现更高水平的合作。为解决该问题，我们采用了一种最新开发的强化学习框架，个体通过试错学习以最大化累积奖励，该范式已成功解释人类行为中多种涌现模式。具体而言，我们为结构化种群配备Q学习算法，并系统改变作为感知信息替代指标的互动邻域大小。有趣的是，在二维方格晶格和Barabási-Albert无标度网络中，我们观察到合作普及率与邻域大小之间存在非单调关系。这种倒U型依赖表明存在最优信息量，可产生最高水平的合作。机制分析显示，中等邻域大小使个体能在信息充分性与决策易处理性之间取得最优平衡，该平衡让个体能检测互惠机会，同时避免因信息过载导致决策质量下降。我们的发现挑战日常直觉，表明适量的信息——而非更多——才是合作涌现的最优条件。
英文摘要
Cooperation is ubiquitous in both natural and human societies, yet its evolutionary basis remains a major challenge. A long-standing puzzle is whether having more information leads to better decision-making and thus a higher level of cooperation. To address this question, we adopt a recently developed reinforcement learning framework in which individuals learn through trial and error to maximize cumulative rewards - a paradigm that has successfully explained diverse emergent patterns in human behaviors. Specifically, we equip a structured population with the Q-learning algorithm and systematically vary the size of the interactive neighborhood, which serves as a proxy for perceived information. Interestingly, we observe a non-monotonic relationship between cooperation prevalence and neighborhood size in both two-dimensional square lattices and Barabasi-Albert scale-free networks. This inverted U-shaped dependence reveals that an optimal amount of information exists, yielding the highest level of cooperation. Mechanistic analyses show that a moderate neighborhood size enables individuals to strike an optimal balance between information sufficiency and decision-making tractability. This balance allows them to detect reciprocal opportunities while avoiding the deterioration of decision quality due to information overload. Our findings challenge everyday intuition, suggesting that a proper amount of information - not more - is optimal for the emergence of cooperation.
