Military Simulation - Heuristic or Stochastic?

Heuristic or Stochastic?

Another method of categorising military simulations is to divide them into two broad areas.

Heuristic simulations are those that are run with the intention of stimulating research and problem solving; they are not necessarily expected to provide empirical solutions.

Stochastic simulations are those that involve, at least to some extent, an element of chance.

Most military simulations fall somewhere in between these two definitions, although manual simulations lend themselves more to the heuristic approach and computerised ones to the stochastic.

Manual simulations, as described above, are often run to explore a 'what if?' scenario and take place as much to provide the participants with some insight into decision-making processes and crisis management as to provide concrete conclusions. Indeed, such simulations do not even require a conclusion; once a set number of moves has been made and the time allotted has run out, the scenario will finish regardless of whether the original situation has been resolved or not.

Computerised simulations can readily incorporate chance in the form of some sort of randomised element, and can be run many times to provide outcomes in terms of probabilities. In such situations, it sometimes happens that the unusual results are of more interest than the expected ones. For example, if a simulation modelling an invasion of nation A by nation B was put through one hundred iterations to determine the likely depth of penetration into A's territory by B's forces after four weeks, an average result could be calculated. Examining those results, it might be found that the average penetration was around fifty kilometres — however, there would also be outlying results on the ends of the probability curve. At one end, it could be that the FEBA is found to have hardly moved at all; at the other, penetration could be hundreds of kilometres instead of tens. The analyst would then examine these outliers to determine why this was the case. In the first instance, it might be found that the computer model's random number generator had delivered results such that A's divisional artillery was much more effective than normal. In the second, it might be that the model generated a spell of particularly bad weather that kept A's air force grounded. This analysis can then be used to make recommendations: perhaps to look at ways in which artillery can be made more effective, or to invest in more all-weather fighter and ground-attack aircraft.