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

2025-10-28

AI Summary

This study investigates how individual learning heterogeneity—specifically, the distribution of learning rates—and dynamic network rewiring jointly drive the emergence of socioeconomic network topologies. We propose a “dual-learning framework” that unifies agent-level strategy updating (modeled via quantum response equilibrium) and network-level topology evolution (governed by adaptive rewiring), grounded in the prisoner’s dilemma game. Using rigorous topological metrics—including degree distribution exponent, Estrada heterogeneity index, and assortativity—we find that low and homogeneous learning rates foster scale-free networks, whereas high or strongly heterogeneous learning rates induce core-periphery structures. Crucially, this work identifies the learning-rate distribution as a pivotal control parameter for macroscopic network architecture—a previously unrecognized mechanism. Our findings provide a novel theoretical foundation for understanding the coevolution of bounded rationality and structural resilience in socioeconomic systems.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Modeling interaction between individual learning behavior and network structural dynamics
• Investigating how heterogeneous learning rates affect emergent network topologies
• Analyzing how learning speed and variability shape system rationality and architecture

Innovation

• Dual-learning framework with individualized agent rates
• Rewiring rate for network structural adaptability
• Simulation model using Prisoner's Dilemma equilibrium