Assumptions & result sets

Overview

Use this category when you need to control how assumptions affect a node’s values or when you want to combine result sets. These functions help you compare “with vs without assumptions”, append non-overlapping outputs, and support legacy behavior during migration.

Start here if…

• You want to see a node without assumptions applied (for comparison/debugging).

• You want to combine result sets from multiple nodes (append non-overlapping slices).

• You’re migrating older models and encounter legacy disaggregation logic.

Not here if…

• You want to allocate values down to more detail → see Dimensionality & hierarchies

• You want to filter to a subset, fill gaps, or rank → see Filtering & data shaping

Mental model

• BASELINE and NONSIM help you control assumptions:

  • BASELINE removes assumptions on the node and upstream.

  • NONSIM removes assumptions on the node itself but keeps upstream assumptions.

• UNION combines non-intersecting result sets on the same levels.

• DISAGGREGATE exists for legacy compatibility and should be replaced by DISTRIBUTE in new models.

Common patterns

• Compare “with assumptions” vs “baseline” → BASELINE('Revenue')
  Use to validate how assumptions impact a result.

• Remove assumptions on the node only → NONSIM('Revenue')
  Use when you want to isolate the effect of assumptions placed on the node itself.

• Append disjoint time slices → UNION('Actuals2024', 'Actuals2025')
  Use when each input covers different periods (no overlap) but you want one continuous output.

• Enforce “same levels” as a guardrail → UNION('NodeA', 'NodeB')
  Use when you want the model to fail fast if level structure diverges (instead of silently combining).

• Migrate legacy disaggregation logic → DISAGGREGATE(...) → replace with DISTRIBUTE(...)
  Use DISAGGREGATE only for backwards compatibility; prefer DISTRIBUTE for new models.

Functions in this category

Choosing between similar functions

• BASELINE vs NONSIM

  • Use BASELINE to remove assumptions on the node and upstream.

  • Use NONSIM to remove assumptions on the node only, while keeping upstream assumptions.

• UNION vs +

  • Use UNION when you want to append disjoint slices and enforce same levels + no overlap.

  • Use + when overlap is allowed/expected (it’s not constrained like UNION).

• UNION vs reshaping (ROLLUP/EXPAND/…)

  • Use UNION to combine result sets from multiple nodes.

  • Use reshaping functions when the goal is to change granularity or dimensionality.

• DISAGGREGATE vs DISTRIBUTE

  • Use DISAGGREGATE only for legacy compatibility.

  • Use DISTRIBUTE for new models and ongoing development (Dimensionality & hierarchies).

Pitfalls & troubleshooting

• Baseline looks “too different”: verify where assumptions are applied (on the node vs upstream) and choose BASELINE vs NONSIM accordingly.

• UNION fails because levels don’t match: confirm both inputs have the exact same level set (same dimensions/levels, same structure).

• UNION fails because rows overlap: ensure the same intersection does not exist in more than one input; split inputs into disjoint slices (often by time or member subsets), or use + if overlap is intended.

• Legacy function in active use: if you still rely on DISAGGREGATE, plan migration to DISTRIBUTE to avoid legacy limitations and reduce maintenance risk.

Related sections

• Operators using + when overlap in UNION is intended

• Dimensionality & hierarchies DISTRIBUTE and shaping before combining

• Filtering & data shaping reduce result sets before combining

• Formula basics evaluation order and how nested functions behave

• Troubleshooting guide assumptions, missing values, unexpected results