Basics of Possibilistic PSYOPS for Decoy/Countermeasure Methods

Abstract:
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 (psyops). These are typically represented as a directed “acyclic” graph and utilize joint probability distributions, which are typically based on incomplete information as to the probabilities involved in 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 general questions or queries are required which cannot be specifically addressed by the Bayesian probabilities. Also, Bayesian methods tend to be computationally intensive. Here we look at fuzzy possibilistic methods that are more robust and less computationally intensive than standard Bayesian methods.

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