The results of this study suggest that different hierarchical orderings may lead to alternative spatial classifications even when based on identical probabilistic inputs. This implies that two models, both statistically consistent and conditioned to the same data, may support different decisions depending solely on how the decision tree is structured. Such divergence is particularly relevant in threshold-based decisions, where small spatial differences may result in changes in classification boundaries or resource categorization. Under this perspective, hierarchical ordering can be interpreted as an implicit decision policy embedded within the simulation algorithm. Early-stage decisions in the hierarchy prioritize certain categories over others, effectively allocating spatial opportunity in a way that reflects modeling assumptions about importance or precedence. As a consequence, the choice of hierarchy influences not only spatial patterns, but also the distribution of decision-relevant outcomes. Recognizing hierarchical ordering as a decision variable rather than a fixed modeling parameter opens new possibilities for uncertainty analysis. Instead of evaluating uncertainty solely through multiple realizations under a fixed hierarchy, alternative hierarchies may be explored as competing decision scenarios. This approach enables a more comprehensive assessment of uncertainty, explicitly incorporating the impact of structural modeling choices on downstream decisions. 6. CONCLUSION This study demonstrates that hierarchical ordering in Hierarchical Sequential Indicator Simulation is a core modeling decision rather than a secondary operational choice. By isolating the decision tree hierarchy as the only variable component in a controlled experiment, the results show that different orderings yield distinct spatial allocations, even under identical probabilistic and geostatistical conditions. Interpreting HSIS as a stochastic decision tree evaluated in space provides a useful conceptual framework to understand this behavior. Within this framework, hierarchical ordering defines the sequence in which uncertainty is resolved and therefore governs how competing propositions are translated into spatial outcomes. Small changes in hierarchy definition, such as those induced by declustering, can propagate into large scale differences in spatial patterns. These findings emphasize the need for greater transparency and for critically evaluating decision structures in spatial modeling workflows. Responsible and efficient mineral development depends not only on the quality of data and models, but also on how questions are hierarchically organized within decision driven simulations. Explicitly acknowledging hierarchy within the model contributes to more interpretable, robust, and trustworthy spatial decision-making under uncertainty. Beyond its methodological implications, this work highlights the importance of explicitly considering decision structure within spatial modeling workflows. In practical mining applications, where models are used to support classification and planning decisions, hierarchical ordering may influence not only how uncertainty is represented, but also how it is acted upon.
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