Abstract

The future of work is increasingly uncertain. With new technologies such as generative artificial intelligence (AI) and the transition to a green economy driving unprecedented change, workers are navigating an evolving labor market that is constantly being pushed out of equilibrium. While labor economics has traditionally emphasized the importance of labor market efficiency, this chapter stresses the importance of analyzing the resilience of labor markets in the face of disruption. We discuss how a complexity-based approach opens up new and important avenues for studying the labor market from an evolutionary perspective, and we highlight recent work that has combined increasingly granular occupational data with network analysis, agent-based modeling, machine learning and other techniques to provide important and policy-relevant insights into labor market resilience. This approach is especially advantageous for modeling transition periods of significant job disruption when labor markets are constantly changing. During economic change, these models can simulate policy outcomes before they are implemented, thus offering the best opportunity to shape resilience through policy interventions.

Keywords: technological disruption, artificial intelligence, net-zero transition, sustainability, employment, inequality, networks, agent-based modeling, complex systems

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