The evidential reasoning (ER) algorithm for multi-criteria decision making (MCDM) performs aggregation of the assessments of multiple experts, one each for every attribute (or subsystem or criterion) of a given system. Two variants of ER are proposed, that handle a scenario where more than one expert assesses an attribute. The first algorithm handles the case of multiple experts who assess an attribute of a larger system. Experiments compare a modification of ER for this scenario which results in poorer detection. The second algorithm is used when experts have overlapping areas of expertise among the subsystems. A comparison is made with a variant of ER in the literature. Both algorithms are examples of novel ‘exclusive’ and ‘inclusive’ ER