On the Unknowable Limits to Prediction

2024-11-28

arXiv.org

AI Summary

This paper addresses the misconception that “irreducible error” in predictive uncertainty represents an absolute fundamental limit. Methodologically, it introduces a systematic error decomposition framework integrating error partitioning, aleatoric–epistemic uncertainty discrimination, construct validity assessment, and multi-source data augmentation modeling. For the first time, errors traditionally labeled “irreducible” are traced to concrete sources—including measurement imperfections, construct misalignment, and modeling inadequacies. The primary contributions are threefold: (1) it dispels the myth of irreducible error’s absoluteness, establishing an iterative theoretical paradigm for progressively approaching the true predictive upper bound; (2) it proposes a novel metric for evaluating predictive potential grounded in error source attribution; and (3) empirical validation across diverse domains demonstrates that synergistic improvements in data quality and algorithmic design can consistently overcome established predictive performance ceilings.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

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.

Problem

• Uncertainty Quantification
• Prediction Error
• Knowledge Deficiency

Innovation

• Error Identification
• Uncertainty Distinction
• Accuracy Improvement