In our conversations about how to design our Hydractinia, we focused on several key questions:

- Agent-based or cellular automata based simulation?
- How realistic (both in terms of implementation and model realism) is basing fight-resolution on the global property of colony size?
- Were we to calculate colony size, how would we determine the size of a colony after it fuses with another?
- What neighborhood (von Neumann or Moore) should be used for a) counting nearby mat cells b) reproducing and c) fighting?
- With passive rejection (between two fused mat colonies), should failing to allorecognize the other colony inhibit reproduction, as it does with aggressive rejection?
- Is dying on a given round considered an 'action', i.e. if a Hydractinia is flagged to die in a given time-step, should it be allowed to do something else before giving up the ghost?

- Change to a
*dead*agent with probability at each timestep - In a random order, check each of the cells in the adjacent nov Neumann neighborhood for successful allorecognition. The first failure results in a fight which is immediately resolved.
- With probability , spawn. If reproducing, check in a random order the von Neumann neighborhood of adjacent cells for one that is empty. The first empty cell found becomes the location of the new agent.
- If none of the above actions were taken, do nothing.

As such, we decided to attempt to subsume the idea that a larger colony has more resources to devote to fights into other aspects of the model and have local interactions dictate all of the model dynamics rather than a mixture of both global and local properties. As a result, we defined a set of model probability parameters which represented the rules given in the assignment description.

- , the probability that a stolon-stolon or unfused-unfused fight will be won by the aggressor
- , the probability that a stolon-unfused mat fight will be won by the stolon cell

Unfused mat cells obviously have an advantage over all other colonies. They can fight and, due to the increased reproductive potential that comes with nearby mat cells, reproduce faster. Setting gives the stolon colonies a slight advantage, which is exacerbated by having a larger colony size; however, this advantage should be mitigated by an unfused mat cell colony's greater reproductive rate. Otherwise, our model follows exactly the one described in the assignment.

We also defined the following other paramaters for our model:

*N*, the length of an edge of the square lattice on which the agents may move*t*, the number of time steps over which the model runs- , the probability at each time-step that an agent will die. The distribution of lifespans is thus a binomial one centered around
- , the base probability of reproducing for any morphological cell type at each time step. A binomial distribution with average
- , the probability added to for each mat cell within Manhattan distance of the spawning agent.
- , the probability of choosing a random new morphology for the daughter cells when a cell splits
- , the probability of choosing a random new chromosome (for each of the two) for the daughter cells when a cell splits

For visualization, we used the gd libraries to write color PNG image files of the cell-array at each timestep. Using the unix utility *convert*, we collapsed the set of image files into a MNG movie file. You can view the movies of the experiments we cite here at *http://www.cs.unm.edu/brainsik/cnid/*. The hues *red*, *blue* and *yellow* were assigned to the three morphologies, *stolon*, *unfused mat* and *fused mat* respectively. Combinations of saturation and value were used to differentiate the nine different genotypes possible for each of the three colors.

A link to the source code for our Hydractinia model can be found on the main Hydractinia page.

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