Exploring Emergent Topological Properties in Socio-Economic Networks through Learning Heterogeneity

TL;DR

This paper investigates how heterogeneous learning behaviors and network adaptation influence the emergence of different topological structures in socioeconomic networks using a dual-learning simulation framework.

Contribution

It introduces a novel dual-learning model that combines agent-specific learning rates and network rewiring, revealing their impact on network topology formation.

Findings

Lower, homogeneous learning rates promote scale-free networks.

Higher, heterogeneous learning rates lead to core-periphery structures.

Learning speed and variability shape network topology and system rationality.

Abstract

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.