Uncertainty management (aggregation and propagation of support) plays an important role in multi-criteria decision processes. The support for a decision may depend on supports for (or degrees of satisfaction of) several different criteria, and the support for each criterion may in turn depend on degrees of satisfaction of other sub-criteria, and so on. Thus, the decision process can be viewed as a hierarchical network, where each node in the network aggregates the support for a particular criterion. The inputs to each node are supports for each of the sub-criteria supplied by knowledge sources, and the output is the aggregated support for the criterion. In order to aggregate and propagate supports in such networks, one needs to know the nature of each node in the network (i. e., the proper type of aggregation connective at each node), as well as the structure of the network (i. e., the connections between the nodes). In this paper, we examine an iterative scheme for determining the structure and nature of such networks, given the desired behavior of the network. We propose the use of aggregation functions based on fuzzy set theory, as they have some very attractive properties.