Basics of Possibilistic PSYOPS for Decoy/Countermeasure Methods

Situational/threat assessment strategies have been studied for generations. Typically, these threat assessments utilize Bayesian belief networks and inference engines, based on decision tree technologies, to determine the likelihood of different deployment strategies and prevention methods known as psychological operations (psyops). These are typically represented as directed “acyclic” graphs and utilize joint probability distributions, which are typically based on incomplete information in the form of probabilistic outcomes when analyzing various aspects of the current mission parameters. Bayesian believe network solutions are good at showing qualitative relationships between entities and have a compact and theoretically sound foundation. Problems arise when answers to questions are required which cannot be specifically addressed by the Bayesian probabilities. Also, Bayesian methods tend to be computationally intensive. Here we look at alternative robust fuzzy possibilistic methods based upon research in abductive learning models and mutual information theory which are less computationally intensive than standard Bayesian methods.