Measuring Complexity using Markov Chains
Elizabeth Andreas (Dept. of Mathematical Sciences, MSU)
02/23/23 3:10pm
Abstract:
The goal of this research is to develop metrics that quantify the complexity of an adversary decision-making process, as well as measure complexity imposed on an adversary by United States Air Force (USAF) actions. This project is built on work previously developed by RAND and uses absorbing Markov chains (AMC) to model an adversary decision-making process to quantify three types of complexity identified by RAND: response impairment, leveraging non linearities and spanning boundaries. These complexity types are conceptualized as the effect that the USAF can have on the overall outcome of an adversaries' decision-making process, how difficult it is for an adversary to make decisions, the likelihood that an adversary will have to repeat decision steps, and the interdependencies in an adversaries' decision-making process.