On the Unknowable Limits to Prediction

• URL: http://arxiv.org/abs/2411.19223v5
• Date: Mon, 10 Feb 2025 22:34:34 GMT
• Title: On the Unknowable Limits to Prediction
• Authors: Jiani Yan, Charles Rahal,
• Abstract summary: Many domains stand to benefit from iterative enhancements in measurement, construct validity, and modeling. Our approach demonstrates how apparently 'unpredictable' outcomes can become more tractable with improved data.
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• Abstract: We propose a rigorous decomposition of predictive error, highlighting that not all 'irreducible' error is genuinely immutable. Many domains stand to benefit from iterative enhancements in measurement, construct validity, and modeling. Our approach demonstrates how apparently 'unpredictable' outcomes can become more tractable with improved data (across both target and features) and refined algorithms. By distinguishing aleatoric from epistemic error, we delineate how accuracy may asymptotically improve--though inherent stochasticity may remain--and offer a robust framework for advancing computational research.

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