Optimally tuned Markov chain simulations of battles for real time decision making

We show how a Markov chain provides a simple representation of the underlying character of a Blue versus Red battle engagement. Fixed time-step simulation provides a natural practical implementation of such a representation. We demonstrate how such an implementation can be optimally tuned to model and capture the most important aspects of a given battle whilst still enabling simulations to be carried out sufficiently fast to be useful in a real-time context. Thus such an approach could potentially be used by field commanders as an aid in real-time battlefield decision making. A realistic example is provided based on a real tactical conflict drawn from recent history.