PSD.10:4.4 - Preserve dependency and model disagreement

Recover shared data, common causes, conditional assumptions, and other dependencies when they affect the consequence comparison. Evidence repeated by several models is not several independent confirmations. Expert or model aggregation needs its own justified Method; the number of voices alone does not determine a probability.

Keep incompatible assumptions separate until a direct result supports their combination. For example, a scenario with a failed access route and a scenario with a functioning route can test different claims; combining their most favorable outcomes produces no realizable case.

Where alternative models disagree, identify the overlapping proposition and what explains the difference: scope, input, representation, mechanism, or unresolved evidence. A new measurement may discriminate the models; sometimes the honest answer is that the present use must retain both. Do not turn disagreement into a narrow average solely because a downstream table expects one number.