Agents in complex economic networks face intrinsic uncertainty regarding global network structure. As real networks are large and complex, even small network uncertainties can lead to huge uncertainties about the market values and risks (i.e., high parameter sensitivity) of firms and organizations in the face of network cascades. This raises the question of how organizations, regulators, or investors can utilize network models to assess the risks at the organization level despite imperfect information. We derive a solution to this challenge and a new unifying perspective. We apply perturbation theory based on conditioning to quantify the sensitivity of node values to uncertainty in network parameters in the presence of nonlinear cascade effects. Our main result is a new efficient algorithm to bound uncertainty in node values in models with nonlinear cascade effects given uncertainty ranges in parameters. We illustrate the application of this algorithm on a real world network. Our applied case study demonstrates the efficiency of our methods in uncovering valuable information into the risks faced by numerous nodes in the network, even in the presence of significant sensitivity challenges posed by network cascade models.” (Joint with Ariah Klages-Mundt)