Understanding how individual learning behavior and structural dynamics interact is essential to modeling emergent phenomena in socioeconomic networks. While bounded rationality and network adaptation have been widely studied, the role of heterogeneous learning rates both at the agent and network levels remains under explored. This paper introduces a dual-learning framework that integrates individualized learning rates for agents and a rewiring rate for the network, reflecting real-world cognitive diversity and structural adaptability. Using a simulation model based on the Prisoner's Dilemma and Quantal Response Equilibrium, we analyze how variations in these learning rates affect the emergence of large-scale network structures. Results show that lower and more homogeneously distributed learning rates promote scale-free networks, while higher or more heterogeneously distributed learning rates lead to the emergence of core-periphery topologies. Key topological metrics including scale-free exponents, Estrada heterogeneity, and assortativity reveal that both the speed and variability of learning critically shape system rationality and network architecture. This work provides a unified framework for examining how individual learnability and structural adaptability drive the formation of socioeconomic networks with diverse topologies, offering new insights into adaptive behavior, systemic organization, and resilience.

翻译：理解个体学习行为与结构动态如何相互作用，对于建模社会经济网络中的涌现现象至关重要。尽管有限理性和网络适应性已得到广泛研究，但个体层面和网络层面的异质学习速率的作用仍未被充分探索。本文提出了一种双学习框架，该框架整合了智能体的个体化学习速率和网络的重连速率，反映了现实世界中的认知多样性和结构适应性。基于囚徒困境和量子响应均衡的仿真模型，我们分析了这些学习速率的变化如何影响大规模网络结构的涌现。结果表明，较低且更均匀分布的学习速率促进无标度网络的形成，而较高或更异质分布的学习速率则导致核心-边缘拓扑的出现。关键拓扑指标，包括无标度指数、埃斯特拉达异质性和同配性，揭示了学习的速度和变异性对系统理性及网络架构具有关键塑造作用。本研究为考察个体学习能力与结构适应性如何驱动具有多样拓扑的社会经济网络形成提供了统一框架，为适应性行为、系统组织与韧性提供了新的见解。