An Integrated Risk-Informed Optimization Framework for Disinformation Interdiction in Coupled Information-Physical...

The manner in which society consumes and responds to information has significantly evolved in recent years, increasing the potential risk of interacting with, reacting to, and spreading misinformation and disinformation. In this paper, we introduce a novel integrated optimization framework that simultaneously models the dynamics of an information layer (where correct information or false information spreads) and a physical layer (where users interact with a physical infrastructure system). We consider an over-the-horizon problem in which an adversary seeks to disrupt physical infrastructure by spreading false information about its operation and performance, prompting users to behave in ways that degrade system performance. Focusing on public transit, we study how disinformation about station closures alters user behavior and system efficiency, and develop an optimization model to identify effective interdiction strategies that mitigate these impacts. The information layer incorporates personality-driven susceptibility and simulates dis/information diffusion, while the physical layer uses a multicommodity network flow model to capture user movements and expose the effects of disinformation on travel time. Our integrated approach enables the joint analysis of both layers, revealing complex interdependencies that are overlooked when studied separately. A case study of the New York City subway system shows that even limited protective interventions can significantly reduce travel delays. In contrast, unmitigated disinformation can substantially degrade system performance. This research highlights the importance of integrated optimization-based strategies for enhancing the resilience of transportation systems to information-based threats and provides a foundation for future studies on dynamic disinformation influence on physical infrastructure systems.