The physical environment is the background in which the objects of our simulations act. The characteristics may define things such as the food and water supply, geology, and topography. Our current implementation represents the physical environment as background data that can be accessed by the biological members.
For these characteristics to have meaning, we need to have a geographical context--that is, we need to know where things are. In fact, location is probably the most fundamental characteristic of the environment. Our simulations currently represent location as a grid of squares, movement is thus defined as integer additions to the x- and y-axes. Each grid point is called a cell in our representations.
All interactions between biological inhabitants and the environment are in part described by the characteristics of the cell. For example, if we are simulating cattle grazing, we could represent grass as a characteristic of the cell. When cattle are allow to graze, we would alter the grass content of the cell according to the consumption rates of the individual cattle in the simulation.
We have not yet addressed the problem of fauna in our simulation.
Though we are using discrete event simulation to avoid representing simulation objects analytically or as uniform groups by representing them as individuals, there are instances where individual representation is impossible. In particular, extremely large populations cannot be represented because of the physical limits of computer memory and the time complexity of the problem.
Our Lyme Disease example requires representation of both thousands of mice and millions of ticks. If we could keep the size of the tick object to one hundred bytes, we would need 100Mb to represent the entire set of ticks for a single pass through the simulation. The processing time for a sweep through the simulation with such a representation would increase by several minutes. To aviod the additional time, we can represent the ticks as a characteristic of the environment.
In the Lyme Disease simulation we represents ticks as a two by two matrix. The indicies represent maturity of the ticks (larval or nymphal) and the carrier status of the ticks (infected or not infected). When a mouse interacts with its environment it alters both the ticks it picks up and the ticks it leaves behind.
We have not yet addressed the problem of flora in our simulation.