A Data Farming Analysis of A Simulation of Armstrong’s Stochastic Salvo Model

Abstract

In 1995, Retired Navy Captain Wayne Hughes formulated a salvo model for assessing the military worth of warship capabilities in the missile age. Hughes’ model is deterministic, and therefore provides no information about the distribution of outcomes that result from inherently stochastic salvo exchanges. To address this, Michael Armstrong created a stochastic salvo model by transforming some of Hughes’ fixed inputs into random variables. Using approximations, Armstrong provided closed-form solutions that obtain probabilistic outcomes. This paper investigates Armstrong’s stochastic salvo model using data farming. By using a sophisticated design of experiments to run a simulation at thousands of carefully selected input combinations, responses such as ship losses are formulated as readily interpretable regression and partition tree metamodels of the inputs. The speed at which the simulation runs suggests that analysts should directly use the simulation rather than resorting to approximate closed-form solutions.