Composable, Knowledge-Driven Disservice Propagation for Complex Interdependent Services with a Flow-Allocation Module Validated Against Hydraulic Simulation
Abstract: Modeling cascading failure across interdependent critical infrastructure requires either a composable physical simulator per domain or a topological abstraction that discards quantity awareness. Simulators are costly, data-hungry, and require detailed calibration per domain; topological abstractions stay data-parsimonious while missing quantity-based behaviors that govern real dynamics. We present, instead, a knowledge-driven propagation architecture in which heterogeneous service dependencies are identified by categories: a general logical propagation resolves multi-dependency and redundancy structures purely from how nodes are categorized, while quantity-aware categories plug in dedicated mechanisms - here, a max-min fair-share flow module for water distribution. Both share one discrete functionality scale and a monotonic update algorithm guaranteeing convergence in finitely many rounds. We assess the flow module by comparing its results with those we get from preexisting pressure-driven simulation with WNTR (Water Network Tool for Resilience) on eight realistic networks across four failure families (clustered damage, capacity-targeted attack, source failure, tank loss). Under capacity-targeted attack and source failure a topological-reachability baseline misses 46% of the truly critical junctions, where the flow module misses 10% and 8% at an average critical-class precision of 0.73. Supplementary materials, code and results are available at: https://github.com/cascade-platform-org/CASCADE