Knowledge obsolescence, human capital inequality, and growth: A network perspective in an automated knowledge society

This paper suggests a micro-founded theory of human capital accumulation that is embedded in an endogenous growth model in which rational and cognitively constrained agents allocate time between production and networking activities within a knowledge-diffusion structure. The framework includes three important mechanisms: (i) learning in a knowledge network; (ii) possible skill downgrading due to knowledge obsolescence; and (iii) fear of technological unemployment due to automation. The analysis distinguishes between fully rational agents and cognitively constrained agents, demonstrating that limited cognitive capacity impedes optimal networking behavior and amplifies human capital inequality. Numerical simulations and comparative dynamics further indicate that higher rates of knowledge obsolescence slow human capital accumulation, weaken growth, and disproportionately burden cognitively constrained individuals, potentially trapping the economy in a low-skill, stagnant equilibrium. • This paper develops a micro-founded growth model highlighting how learning in knowledge networks directly influences economic growth rates. • It identifies a poverty trap arising from insufficient human capital, exacerbated by rapid knowledge obsolescence. • The study integrates the risk of technological unemployment due to automation into agents’ decision-making processes. • It quantitatively assesses how cognitive constraints amplify human capital inequality, especially after knowledge obsolescence shocks. • Cognitively constrained agents experience disproportionately negative impacts on skill levels relative to rational optimizers, worsening long-term inequality.