Abstract:

The increasing entwinement of predictive robotics, driven by artificial intelligence, is revolutionizing the labor economy by enabling automation of conventional jobs, especially in manufacturing sectors. Such a technological transition incites important questions regarding prospective employment scenarios and economic security. Traditional prediction techniques, including statistical pattern analyses and linear models, are not always able to account for complex, nonlinear interactions between human work, technological progress, and market forces. These conventional methods do not have the capability to simulate emergent behavior and adaptive reactions from both workers and firms. In order to overcome these shortcomings, this research suggests a framework through Agent-Based Economic Modeling (A-EM) in order to project job displacement trends in manufacturing. A-EM is able to capture the nonlinear and adaptive dynamics of the changing labor market driven by automation through simulating the self-governing choice-making process of policymakers, workers, and policymakers. The results highlight the need for proactive economic policies to build resilience against the pace of technological change, providing a useful resource for policymakers and business leaders to navigate the future of work.