Interplay of network structure and talent configuration on wealth dynamics

Area of Science:

• Agent-based modeling
• Economic complexity
• Network science

Background:

• Individual economic success is a complex interplay of innate talent, chance, and social support.
• Understanding how these factors interact within social structures is crucial for economic analysis.

Purpose of the Study:

• To introduce an agent-based model integrating talent, luck, and social interaction to study capital accumulation dynamics.
• To analyze the impact of network structure and talent distribution on economic indicators.
• To investigate the "talent configuration effect" and its relationship with talent assortativity (TA) and talent-degree correlation (TD).

Main Methods:

• Development of an agent-based model incorporating talent, luck, and social interactions.
• Analytical and numerical methods to explore model dynamics.
• Analysis of economic indicators: growth rate (n_rate), Gini coefficient (n_Gini), and meritocratic fairness (n_LT).
• Evaluation of talent assortativity (TA) and talent-degree correlation (TD) across different network topologies.

Main Results:

• A positive short-term correlation between TA and TD was observed for all economic indicators.
• The influence of TA versus TD on economic indices is dependent on network topology: TD dominates in scale-free networks, while TA is more significant in lattice-like networks.
• Hub monopolization by talented agents makes economic growth highly sensitive to their performance.

Conclusions:

• Network structure and talent configuration significantly influence capital accumulation dynamics.
• High socioeconomic homophily can lead to a trade-off between economic growth and equality.
• The findings highlight the importance of considering both individual attributes and network structures in economic modeling.