Socio-Economic Model of AI Agents
This work addresses the impact of AI integration on economic productivity for researchers and policymakers, though it appears incremental as it builds on existing agent-based modeling frameworks.
This paper tackles the problem of understanding how AI agents affect social output in socio-economic systems by developing five agent-based models that progressively incorporate AI collaboration, network effects, and independent production. The results show that AI agents significantly increase aggregate social output, with network effects leading to nonlinear growth and independent production providing higher long-term growth potential.
Modern socio-economic systems are undergoing deep integration with artificial intelligence technologies. This paper constructs a heterogeneous agent-based modeling framework that incorporates both human workers and autonomous AI agents, to study the impact of AI collaboration under resource constraints on aggregate social output. We build five progressively extended models: Model 1 serves as the baseline of pure human collaboration; Model 2 introduces AI as collaborators; Model 3 incorporates network effects among agents; Model 4 treats agents as independent producers; and Model 5 integrates both network effects and independent agent production. Through theoretical derivation and simulation analysis, we find that the introduction of AI agents can significantly increase aggregate social output. When considering network effects among agents, this increase exhibits nonlinear growth far exceeding the simple sum of individual contributions. Under the same resource inputs, treating agents as independent producers provides higher long-term growth potential; introducing network effects further demonstrates strong characteristics of increasing returns to scale.