Digitized terrain data corresponding to a given geographical area are generally available in a rectangular grid at some fixed resolution. In the modeling and simulation of processes that interact with terrain, it is often desirable to model the terrain at a higher resolution. Unfortunately, it is impossible to discern terrain features smaller than the resolution of the data. In this report the author uses the variable resolution terrain model to do the next best thing. He constructs a continuous terrain surface that matches the terrain data to a prescribed tolerance and simultaneously includes in that surface realistic terrain features smaller than the resolution of the data.

This Note examines the differences in combat outcomes predicted by models of different resolution applied to identical combat situations. First, hypothetical combat situations are posed, then several models of varying degrees of resolution in the spatial representation, aggregation of forces, and time step are used to predict losses and battle winners. Both stochastic and deterministic simulations are used. Comparison of outcomes provides important insights into the problems of aggregation. Observations from this set of experiments are as follows. Intuition regarding outcomes, causes, and effects is frequently wrong, leading to bad approximations in the aggregate. Scaling for different levels of resolution is possible, but a method of predicting the appropriate scaling technique and factors has not been found. The differences in outcomes between stochastic and deterministic models are most pronounced in the 'fair-fight' regime, in which the force balance (accounting for situational factors) is almost even. Because defense analysis frequently operates in this regime (getting just enough force to a theater or because constrained defense budget allocations may not permit overwhelming odds), this implies that great care should be taken to understand the possible variance in outcomes.