  The economic success of individuals is often determined by a combination of
talent, luck, and assistance from others. We introduce a new agent-based model
that simultaneously considers talent, luck, and social interaction. This model
allows us to explore how network structure (how agents interact) and talent
distribution among agents affect the dynamics of capital accumulation through
analytical and numerical methods. We identify a phenomenon as ``talent
configuration effect", which refers to the influence of how talent is allocated
to individuals (nodes) in the network. We analyze this effect through two key
properties: talent assortativity (TA) and talent-degree correlation (TD). In
particular, we focus on three economic indicators: growth rate ($n_{\rm
rate}$), Gini coefficient (inequality: $n_{\rm Gini}$), and meritocratic
fairness ($n_{LT}$). This investigation helps us understand the interplay
between talent configuration and network structure on capital dynamics. We find
that, in the short term, positive correlations exist between TA and TD for all
three economic indicators. Furthermore, the dominant factor influencing capital
dynamics depends on the network topology. In scale-free networks, TD has a
stronger influence on the economic indices than TA. Conversely, in lattice-like
networks, TA plays a more significant role. Our findings address that high
socioeconomic homophily can create a dilemma between growth and equality, and
that hub monopolization by few highly talented agents makes economic growth
strongly dependent on their performances.
