the same dataset, share identical variogram models, neighborhood parameters, and random seeds, the change in the decision tree ordering alone leads to markedly different spatial allocations. The divergence observed from the third simulation step onward highlights a key property of HSIS: once a category is allocated at a given node, it removes probability mass and spatial opportunity from all lower priority categories. Consequently, early decisions have a disproportionate influence on the final spatial configuration. This path dependency implies that hierarchical ordering implicitly encodes a modeling hypothesis about which propositions should be tested first and which uncertainties are resolved earlier in space. The comparison between hierarchies based on raw sample proportions and declustered proportions further illustrates how preprocessing choices propagate into decision structures. A relatively small change in global proportions, caused by declustering, was sufficient to swap the ranking of two categories and trigger a branching in the decision tree. This branching, in turn, produced spatial patterns that differ not only locally but across the entire modeled domain. Such sensitivity underscores that hierarchy definition aggregates assumptions about representativeness, support, and spatial bias, even when these assumptions are not explicitly stated. From a decision theoretical perspective, the HSIS workflow can be interpreted as a stochastic decision tree evaluated in space, where each indicator represents a binary proposition and each realization represents one possible resolution of uncertainty. Under this interpretation, different hierarchical orderings correspond to different decision policies applied to the same information set. The results presented here show that these policies are not equivalent and may lead to different spatial alternatives, even when all probabilistic inputs remain unchanged. These findings have practical implications for applications such as geological modeling, resource classification, and mine planning. In such contexts, spatial models are often used to support decisions that depend on the relative importance of competing categories, such as ore versus waste or alternative geological domains. Treating hierarchical ordering as a secondary or default choice may obscure its role in shaping spatial outcomes and, ultimately, decision risk. Explicitly acknowledging and testing alternative hierarchies can therefore improve transparency and robustness in spatial decision making workflows. It is important to emphasize that this study intentionally adopts a simplified experimental setup to isolate the effect of hierarchical ordering. The use of a single realization, isotropic variograms, and fixed parameters limits the scope of inference but strengthens causal interpretation. Future work may extend this framework by incorporating multiple realizations, geological rules, local proportions, or data driven tree optimization strategies, further exploring how decision structures interact with spatial uncertainty in more complex settings. From an operational perspective, the observed sensitivity to hierarchical ordering has direct implications for decision-making workflows in mining. Spatial models derived from HSIS are often used as inputs for downstream tasks such as resource classification, ore/waste discrimination, and mine planning. In these contexts, alternative spatial realizations are not merely descriptive, but prescriptive, as they influence economic and operational decisions.
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