AI偏爱高增长低失业政策，对通胀债务不敏感
Left Leaning Models: How AI Evaluates Economic Policy?
- 用联合实验测试主流大模型对经济政策的偏好
- 多数模型倾向高增长、低失业、低不平等，忽视通胀与债务
- 即使在货币政策场景下仍偏好低失业和低不平等
随着人工智能在经济决策者、学者及市场参与者中的应用呈指数级增长，理解其对经济政策的偏好变得至关重要。然而，这些偏好尚未系统评估，仍属黑箱。本文对OpenAI、Anthropic和Google的主流大语言模型进行联合实验，要求其在多因素约束下评估经济政策。结果在不同模型间高度一致：大多数模型强烈偏好高增长、低失业和低不平等，而非传统宏观经济关注的低通胀和低公共债务。情境特异性实验显示，尽管模型对上下文敏感，但在货币政策设置下仍显著偏好低失业和低不平等。数值敏感性测试表明，模型对量化变化有直观反应，但也揭示非线性模式，如损失厌恶。
原文摘要 · Abstract (English)
Would artificial intelligence (AI) cut interest rates or adopt conservative monetary policy? Would it deregulate or opt for a more controlled economy? As AI use by economic policymakers, academics, and market participants grows exponentially, it is becoming critical to understand AI preferences over economic policy. However, these preferences are not yet systematically evaluated and remain a black box. This paper makes a conjoint experiment on leading large language models (LLMs) from OpenAI, Anthropic, and Google, asking them to evaluate economic policy under multi-factor constraints. The results are remarkably consistent across models: most LLMs exhibit a strong preference for high growth, low unemployment, and low inequality over traditional macroeconomic concerns such as low inflation and low public debt. Scenario-specific experiments show that LLMs are sensitive to context but still display strong preferences for low unemployment and low inequality even in monetary-policy settings. Numerical sensitivity tests reveal intuitive responses to quantitative changes but also uncover non-linear patterns such as loss aversion.
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