Differential equation models for sharp threshold dynamics

• Incorporates stochastic thresholds to general deterministic systems.
• Applies the SIR model of infectious disease to Cyber-Conflict.
• Considers the tradeoff between speed of detection and speed of vaccination.
• Also applied to Lanchester model of Armed Conflict.
• Extends differential equation models to a new class of problem.

We develop an extension to differential equation models of dynamical systems to allow us to analyze probabilistic threshold dynamics that fundamentally and globally change system behavior. We apply our novel modeling approach to two cases of interest: a model of infectious disease modified for malware where a detection event drastically changes dynamics by introducing a new class in competition with the original infection; and the Lanchester model of armed conflict, where the loss of a key capability drastically changes the effectiveness of one of the sides. We derive and demonstrate a step-by-step, repeatable method for applying our novel modeling approach to an arbitrary system, and we compare the resulting differential equations to simulations of the system’s random progression. Our work leads to a simple and easily implemented method for analyzing probabilistic threshold dynamics using differential equations.