More Data, Worse Decisions? Preference Reversals in Neural Networks under Gram Incompatibility
Neural networks increasingly combine data across populations, time periods, and operating conditions to improve generalization. This raises a reliability question: whether a model refitted on pooled data preserves an action ordering supported by both sources. Case-Based Decision Theory (CBDT) formalizes this requirement through its composition axiom, which requires source-supported preferences to survive their union. We study when this property holds for fixed-representation neural networks with ordinary least squares (OLS) output heads. First, we show that pooled refitting recomputes the inverse-Gram geometry used to weight source evidence, which can reverse shared preferences, and deriv...
| Authors | Yanli Yan, Yuanzheng Li, Yong Zhao et al. |
|---|---|
| Primary category | stat.ML |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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