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分析人工智能对财富分配的影响:一种动态随机一般均衡方法。

Analyzing wealth distribution effects of artificial intelligence: A dynamic stochastic general equilibrium approach.

作者信息

Liu Fang, Liang Chen

机构信息

Economic School of Changzhou University, China.

Financial Technology School of Shenzhen University, China.

出版信息

Heliyon. 2025 Jan 13;11(2):e41943. doi: 10.1016/j.heliyon.2025.e41943. eCollection 2025 Jan 30.

Abstract

This study explores the often-overlooked influence of artificial intelligence (AI) on wealth distribution. Using a continuous-time heterogeneous agent dynamic general equilibrium model, we investigate AI's impact on production technology as a form of biased technological progress. Our findings highlight a temporal dichotomy in AI's effects on wealth inequality: in the short term, AI exacerbates disparities in wealth distribution, while the long-term outcomes depend on the extent of AI's influence across different technological domains. The nuanced nature of AI-driven technological progress leads to distinct consequences for the rate of return on capital-showing short-term increases across all forms of technological progress, but with varying long-term effects. Based on these results, we propose policy recommendations for China to leverage AI for economic growth while mitigating inequality. This research provides a comprehensive analysis of AI's role in wealth distribution, offering valuable insights for both academic inquiry and policy formulation.

摘要

本研究探讨了人工智能(AI)对财富分配这一常常被忽视的影响。我们使用连续时间异质性主体动态一般均衡模型,研究人工智能作为一种偏向性技术进步形式对生产技术的影响。我们的研究结果凸显了人工智能对财富不平等影响的时间二分法:短期内,人工智能加剧了财富分配的差距,而长期结果则取决于人工智能在不同技术领域的影响程度。人工智能驱动的技术进步的细微差别导致了资本回报率的不同后果——在所有形式的技术进步中,短期内回报率都会提高,但长期影响各不相同。基于这些结果,我们为中国提出了政策建议,以利用人工智能促进经济增长,同时减少不平等。这项研究对人工智能在财富分配中的作用进行了全面分析,为学术研究和政策制定提供了有价值的见解。

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