Document Type
Poster
Publication Date
Fall 2025
Abstract
This study proposes an AI-based economic uncertainty metric built from disagreement among three frontier large language models (Claude, Google Gemini, and ChatGPT) that score daily market sentiment across five dimensions: equities, inflation, labor, consumer confidence, and forward guidance. We quantify both across-model and within-model disagreement using cosine distance between 5-dimensional sentiment vectors and assess whether these disagreement measures track or relate to established uncertainty benchmarks (VIX, EPU, and the Citi Economic Surprise Index). Results suggested that across-model disagreement moved more closely with new and policy-based uncertainty than with option-implied volatility, as it was strongly positively correlated with the Economic Policy Uncertainty Index and moderately positively correlated with the Citi Economic Surprise Index, but strongly negatively correlated with the VIX. In contrast, within-model disagreement varied substantially by model and date, indicating model-specific prompt sensitivity, rather than a uniform relationship with traditional uncertainty measures.
Recommended Citation
Dunson, Peter; McCloud, Andre; and Eisenbeis, Gwen, "Disagreement Among AI Models as a Metric of Economic Uncertainty: Testing AI Disagreement Against Traditional Uncertainty Metrics" (2025). IPHS 391: Interdisciplinary AI Frontiers. Paper 10.
https://digital.kenyon.edu/dh_iphs_391/10
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.