Sequential Indicator Simulation (SIS) operationalizes this concept by simulating dependent binary events sequentially. The data are codified as indicators, 0-1 bits, and kriging is used to interpolate them, generating a value between 0 and 1 that represents the probability of that event in space. Then, for each realization, a draw is made according to that probability distribution, and a value of 0 or 1 is assigned. Hierarchical Sequential Indicator Simulation (HSIS) was then developed as a structured extension of SIS, in which indicator variables representing multiple competing propositions are evaluated according to a predefined hierarchical order (Deutsch & Journel, 1998). Rather than simulating all indicators independently or simultaneously, HSIS organizes the simulation process as a sequence of conditional decisions, in which each indicator is evaluated only if all higherpriority propositions have been rejected. This hierarchical structure ensures that mutually exclusive categories are honored and that each location in space is assigned a single outcome. A closely related logical structure is found in decision driven frameworks that explicitly organize uncertainty around discrete choices (Caers, 2011). Decision trees are used to decompose complex problems into ordered sequences of propositions. In this sense, HSIS can be interpreted as a stochastic decision tree evaluated in space: each indicator corresponds to a binary question, the hierarchical order defines the sequence in which these questions are posed, and each simulation realization represents one possible resolution of uncertainty. Under this interpretation, the hierarchical ordering is not merely an operational choice, but a fundamental modeling decision that governs how uncertainty is translated into spatial outcomes. Despite its structural role, the effects of hierarchical ordering on spatial outcomes and uncertainty representation are rarely examined explicitly. In this context, hierarchical ordering should not be regarded as a purely operational choice, but as an integral component of the spatial decision-making process. Different orderings correspond to different sequences of binary propositions and, consequently, to different ways of allocating space under uncertainty. 2. OBJECTIVES AND SCOPE By explicitly examining the impact of hierarchical ordering on spatial simulation, this study aims to clarify how methodological choices at the level of decision structure propagate into uncertainty representation. Such understanding is essential for developing spatial models that are not only statistically consistent, but also transparent, interpretable, and aligned with responsible decision making in mining applications. The study addresses the Geoscience and Next-Gen Exploration theme by focusing on spatial modeling under uncertainty and contributes to AI and Data-Driven Decision Making by analyzing the role of decision tree hierarchies within numerical simulation. 3. METHODOLOGY The experiment was implemented using programs and methodological guidelines described by Deutsch and Journel (1998). Two hierarchical orderings based on global proportions,
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