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

TL;DR

This work addresses how heterogeneity in both individual learnability and network adaptability shapes the emergent topology of socio-economic networks. It introduces a dual-learning framework combining agent-level learning rates and a network rewiring rate, analyzed through Prisoner's Dilemma interactions under Quantal Response Equilibrium. The study shows that low, homogeneous learning speeds favor scale-free structures, while higher or more heterogeneous learning—and faster network rewiring—drive core-periphery topologies, with metrics such as gamma, Estrada heterogeneity, and assortativity tracking these transitions. The framework offers a unified lens for understanding adaptive behavior, systemic organization, and resilience in complex socio-economic systems, with implications for designing and analyzing real-world networks.

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.

Figures (21)

• Figure 1: Average node rationality under uniform learning rates.
• Figure 2: Scale-free correlation under uniform learning rates.
• Figure 3: Power-law correlation under uniform learning rates.
• Figure 4: Estrada heterogeneity under uniform learning rates.
• Figure 5: System rationality under uniform learning rates.