Serendip is an independent site partnering with faculty at multiple colleges and universities around the world. Happy exploring!

Reply to comment

Jen's picture

AIDS and Ants

First, we looked at the Ant Colony model, which consists of a square divided into three unequal parts: 25% brown, 25% gray and 50% green. There were three different jobs which ants performed in each area: in the brown area, there are midden workers (those responsible for cleaning up debris), in the blue area there are patrollers, and in the green area there are foragers. When you press Go, the ants go about their business; sometimes, ants will switch areas and thus switch jobs. The percentage of ants in each area always stays the same though; 1:1:2. The question is, why? In trying to figure out why, we were able to mess with a bunch of different factors (number of ants in each area, placing on the board only ants of a certain job), but these factors just resulted in nature returning to its natural distribution, or most probable state. The one factor that had an effect on the natural distribution was the number of hydrocarbons in any given area.

Hydrocarbons are hormones that tell ants what kind of job the other ants are performing. If an ant encounters too many hydrocarbons of one particular job, it decides to do another job that is less populated.

The program enabled us to place hydrocarbons within the different areas. In low doses, hydrocarbons did not have any noticable effect. But when an area was saturated with hydrocarbons, the ants soon moved out of that area and moved into another area.

The second computer model we testedwas the AIDS model. In this model, we were able to vary the size of the population of people, the average number of times people coupled, the average length of commitment (in weeks), the average condom use and the average number of times people were tested per year. The model assumed that those people who were tested would then practice safe se, which is obviously not reflective of the real world.

In our test, we decided to look at coupling rate. 

In our first test, we made all of the factors as low as possible (commitment low, no testing, no use of condoms) but average coupling was at 5.

trial 1: everyone has AIDs at 590 weeks

trial 2: 98.33% at 10,000 + weeks and no change

trial 3: 100 % infected wtihin 500 weeks

trial 4: 99% infected within 500 plus weeks

 

Initially, these trials implied that there is a part of the population which is naturally resistant to AIDS. Then we read that average coupling is about how often someone chooses to not have sex, not the percentage chance of them having sex.

 

Then, we decided to increase the sample size of the population to the max, which was 496 people. We measured similar results as compared to teh first population size.

 

 99.8 percent infected within 10000 weeks

100% infected within 500 weeks

100% infected within 500 weeks

100% infected within 500 weeks

100% infected within 500 weeks

99.8% infected within 5000 weeks

100% infected within 500 weeks

100% infected within 500 weeks

 

Then we decided to reduce the average coupling but maintain the population size. When coupling was reduced to 3, in trials, results topped off at 94.3%. When coupling was reduced to 2, in trials, results topped off at 64.92%

 

We concluded that the model showed that the lower the percentage of coupling rate, the more people who chose not to have sex, therefore the less people got AIDS. We don't think that this computer model is random, since people are active in who they decide when to have sex and when not to.

 

Reply

To prevent automated spam submissions leave this field empty.
8 + 10 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.