WHEN SHARING HURTS: A PRE-REGISTERED REFUTATION OF FIXED HIERARCHICAL CREDIT SHARING IN NON-STATIONARY STRUCTURED BANDITS

Non-stationary environments challenge learning systems because information that is useful in one regime may become misleading after the underlying process changes. Hierarchical mechanisms offer a natural way to share information across related decisions, but such sharing can also propagate stale or locally inappropriate evidence. We study this tension through a controlled comparison of fixed hierarchical routing and edge-local adaptation under explicitly defined non-stationary regimes. Rather than assuming that hierarchical sharing improves adaptation, we formulate and pre-register a falsifiable hypothesis and evaluate it under a fixed confirmatory protocol. The resulting evidence refutes the preregistered superiority claim: under the tested conditions, the fixed hierarchical routing update does not provide the expected advantage and can perform worse than the edge-local alternative. Additional diagnostic analyses examine how this behavior varies across regimes and experimental conditions, while separating confirmatory evidence from post-hoc interpretation. These results do not establish that hierarchical representations are intrinsically harmful; instead, they identify a concrete failure mode of a particular fixed credit-sharing mechanism under non-stationarity. More broadly, the study illustrates how seemingly beneficial information sharing can become detrimental when adaptation occurs across changing environments, and provides a reproducible framework for testing such claims in adaptive learning systems.