The world that we live in is filled with large scale agent systems, from diverse fields such as biology, ecology or
finance. Inspired by the desire to better understand and make the best out of these systems, we propose an approach
which builds stochastic mathematical models, in particular G-networks models, that allow the efficient representation of
systems of agents and offer the possibility to analyze their behavior using mathematics. This work complements our
previous results on the discrete event simulation of adversarial tactical scenarios. We aim to provide insights into
systems in terms of their performance and behavior, to identify the parameters which strongly influence them, and to
evaluate how well individual goals can be achieved. With our approach, one can compare the effects of alternatives and
chose the best one available. We model routine activities as well as situations such as: changing plans (e.g. destination
or target), splitting forces to carry out alternative plans, or even changing on adversary group. Behaviors such as
competition and collaboration are included. We demonstrate our approach with some urban military planning scenarios
and analyze the results. This work can be used to model the system at different abstraction levels, in terms of the
number of agents and the size of the geographical location. In doing so, we greatly reduce computational complexity
and save time and resources. We conclude the paper with potential extensions of the model, for example the arrival of
reinforcements, the impact of released chemicals and so on.
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