A three-way game analysis of technology-to-goodness-driven artificial intelligence marketization and application strategy

The commercialization of artificial intelligence (AI) technology has led to labor displacement and triggered resistance among workers with redundant skills, posing challenges to regional economic development. However, there remains a lack of systematic theoretical support regarding how local governments can use targeted regulatory measures to mitigate displacement effects and enhance job creation. To address this, this paper constructs a three-party evolutionary game model encompassing AI firms, workers with redundant skills, and local governments, systematically examining the strategic evolution paths and equilibrium stability conditions of each participant under different regulatory intensity scenarios. The study finds that when local governments enhance the precision of their regulatory measures, they can effectively incentivize firms to deepen their engagement in the local market and encourage workers to shift from resistance to actively seeking skill enhancement. At the same time, the evolutionary game system exhibits a critical threshold effect: when the costs of market resistance exceed the net benefits of local operations (including the sum of innovation subsidies and reputational gains), firms will choose to exit the local market, creating a “high-cost deadlock.” Furthermore, skill subsidies must cover the difference between workers' learning costs and perceived income losses, while additionally offsetting the resistance effectiveness premium generated by firms’ local operations, in order to effectively incentivize skill-enhancement behavior. Simulation analyses validate the robustness of these conclusions. This paper operationalizes the concept of “technology for good” into testable equilibrium conditions, providing a theoretical basis for the design of targeted subsidy mechanisms and the formulation of differentiated skills training policies, while also offering a new analytical framework for governments to address the challenges of technological unemployment.