Exploring Emergent Topological Properties in Socio-Economic Networks through Learning Heterogeneity
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| Format: | Preprint |
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2025
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| author | Karavita, Chanuka Lyu, Zehua Kasthurirathna, Dharshana Piraveenan, Mahendra |
| author_facet | Karavita, Chanuka Lyu, Zehua Kasthurirathna, Dharshana Piraveenan, Mahendra |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_24107 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Emergent Topological Properties in Socio-Economic Networks through Learning Heterogeneity Karavita, Chanuka Lyu, Zehua Kasthurirathna, Dharshana Piraveenan, Mahendra Physics and Society Computer Science and Game Theory Social and Information Networks Adaptation and Self-Organizing Systems 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. |
| title | Exploring Emergent Topological Properties in Socio-Economic Networks through Learning Heterogeneity |
| topic | Physics and Society Computer Science and Game Theory Social and Information Networks Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2510.24107 |