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Rachel Tashjian's picture


Drs. Caitlin and Rachel, and Andy "Workaholic" Kim

Our first "game" was the segregation game. We had several data, but we were most interested in what occurred when we set "difference" as our preference.

For starting with random distribution, and setting a 50% preference for difference with 2500 blocks, we started with 49.7% similar, and 38% unhappy. When we had 0% unhappy, the similarity percentage was 34.9. Interestingly, we noticed that our resulting "world" was a maze-like pattern.

Starting with segregated distribution (which looks like a blobby pattern like a map), again with a 50% preference for difference and 2500 blocks, we had 87.7% similar and 94% unhappy. After running the model, 0% were unhappy and 33.9%. Again, we saw the maze-like pattern.

Our story is that diversity itself is a pattern (counterintuitive to the idea that segregation is a pattern and diversity is random). Also interesting is that the ending percentage of similarity in diversity/difference is always approximately 1/3.

The second mode/game we ran was the predator/prey wolf thang. We turned grass on, and when running the model on its default (100 sheep, 50 wolves), the environment was stable. We changed the numbers of animals, making 100 sheep and 100 wolves, predicting that sheep would become extinct (or at least endangered/much lower) because of the increased predator population.

HOWEVER, we were AMAZED to find that in the end, there were 156 sheep and 73 wolves. Our story to explain this is that initially, the increased wolf population meant that there were not enough sheep to go around, and some wolves died. With some wolves dead, the sheep had less predators and therefore more of their offspring survived. But with more sheep, and less wolves, the wolves could now get food more easily and recovered (though not to the extent that the originally did), resulting in the CIRCLE OF LIFE.... This displayed to us the pattern of the food chain/the need for equilibrium of resources...constantness, or something.



Caitlin and Rachel (and Andy)


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