2025-05-22-兰德-全球竞争时代战略自动化政策研究报告_55页_2mb

Report Summary

Objective

The report examines AI-driven automation policy strategies to balance economic growth and distributional inequality in an era of global technological competition.

Key Methods

• Employs Robust Decision Making (RDM) to simulate thousands of potential futures for AI automation.
• Differentiates between vertical automation (enhancing existing automated processes) and horizontal automation (displacing human labor).
• Evaluates policies through metrics including average growth rates, inequality growth, robustness (probability of success), and regret (cost of suboptimal choices).

Main Findings

• Favoring vertical automation policies yields robust outcomes across diverse scenarios, supporting growth and stability.
• Horizontal automation should be moderately restricted to mitigate inequality, but aggressive support is needed for transformative growth, though with higher risk.
• High automation success is contingent on factors like task complementarity and diminishing returns in automation.
• Trade-offs exist between aggressive growth policies (requiring comprehensive support) and moderate objectives (allowing more stable, asymmetric strategies).

Recommendations

Policymakers should:

• Strongly incentivize vertical automation to enhance productivity.
• Modestly disincentivize horizontal expansion to manage inequality.
• Leverage institutional tools like labor standards and tax structures to implement policy changes efficiently.
• Use RDM frameworks to guide policy design under deep uncertainty.