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Bio 103 Lab 11: From simplicity to complexity: homestasis, autonomy ... and randomness

Paul Grobstein's picture
Cells are interacting assemblies of macromolecules, multicellular organisms interacting assemblies of cells, populations and ecosystems interacting assemblies of organisms ... with in each case more complex behavior emerging from interactions among simpler elements. Could one make sense of more sophisticated aspect sof life in these terms, of social behavior, for example, or the meaning and origin of "purposive" behavior? An approach that we'll follow today is to explore computer models like one we looked at at the beginning of the course.

You and your partner will explore your choice of two or more of several models. Most are based on and/or run in a program called Netlogo, made available by Northwestern University's Center for Connected Learning and Computer Based Models. Both the program and the models can be used on-line and are also available for downloading, so you can continue to explore them on your own computers if you're so inclined. Pick one of the first three and any other additional model on the list below. Whichever models you choose to explore, think of doing so as a process of making observations in order to try and come up with a story to account for how the system behaves. Report your observations and story in the course forum area. How much can we make sense of using computer models? What role does randomness play in them?

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)

LaKesha's picture

Lakesha, Shanika, Sharhea

Today in lab, we played some games to compare simple rules with complex rules.

1. Thinking about Segregation and Intergration:

We initially thought about the world to create a hypothesis. We decided that the more open-minded the less segregation will occur. This is exactly what happened.

  • When the red dots wanted to be around 50% of their kind, it took them 12.5 years to accomplish.
  • Red dots wanted 75% of their "peoples" around them, it took 66.3 years.
  • When red dots were a little bit more open, at 25% segregation, it took only 7 years for them to get together
  • At 0%, the area/world remained random, with green and red together
  • When the red dots wanted 100% of themselves to be together, the computer program is still going, currently it is at 6000+ years. We are sure that it will continue going, meaning we could never really be fully segregated.

We tried going from segregation to randomness and the program is still going when placed on 75% different. At 25% it took them 5 years to be different.

Can segregation ever exist? Why was this word used in the 60s?

The second game we played was Cooperation. We had cooperative cows that were willing to eat only grass to a certain hieght, while the greedy cows wanted to eat everything. The more the cows eat, the more they reproduce. Initially when we started with the default settings, all the greedy cow kept on reproducing and maxed out at 4,260 cows. We actually thought they would die out, but they didn't. The next setting where we set the grass low, and the grass concentrate high, the cooperative cow continued to reproduce and the greedy cows eventually died out. The cooperative cow maxed out at 10,800.

ekoike's picture

Computer Simulation of Randomness

Crystal Reed and Eri Koike

Langton's Ant

Through this experiment, we attempted to discover what the overall purpose of Langton's ant is through a series of hypotheses and experiments.

We initially hypothesized that perhaps the size of the roadblocks would influence how long it would take it to start creating what we thought the intended purpose was of the ant: to create a road. Through our experiments, where we changed the size of the shaded square around the ant, we found the following data:

Smaller shaded square around the ant: The road had started early at around 7,000 steps.

While the bigger square had started creating the road at around 5,000 to 6,000 steps.

The biggest square had created an inverse road within the square at around 16,000 steps and had started a real road outside of the square at around 22,000 steps. It also had a strange pattern that wasn't previously observed of creating walls around the outside of the square before beginning to create the real road.

Therefore, we found that environment determines the results of the behavior and road blocks/environmental changes make the ant go off track for a while but it always returns to the intended purpose of creating a road. Since the ant is basically 4 simple programmed steps, we found that when there are specific ideal conditions around the ant, these instructions wille express themselves in the road pattern. We paralleled this ant experiment to the construction of our previous plant experiment by stating that perhaps if conditions and genes match up (such as right soil and light), it would reach its optimal condition of growing.

Wolf Sheep Predation

Through this experiment, we were trying to discover how variations in factors in an ecosystem can change and affect populations.

We found that it would be most effective to discuss population fluctuations through creating very extreme environments through the absence of a predator or a prey. We initially hypothesized that if we removed the wolves, the grass would disappear due to the overpopulation of the sheep and eventually the sheep would die off as well.

Through this experiment, we found that instead, there was a corresponding jump in amount of sheep and drop in amount of grass in the ecosystem due to the absence of predators (wolves), but the populations began to even out and ultimately neither died out.

On the contrary, when we removed the sheep from the ecosystem and only had wolves and grass, we found that the wolves quickly died out and the grass had a huge spike in population.

Our conclusion that if you remove an element of prey, you will greatly affect the remaining ecosystem, since there isn't nourishment and a balance in the environment. However, when we removed the predator, we found that it had much less of an effect. We found that perhaps the reason why some factors are more stable than others is because perhaps some animals (in our case the sheep) don't have to work as hard as others (the wolf). It also may have to do with the pace that certain preys (the grass) reproduce versus the sheep.

LuisanaT's picture

Luisana and Kate

Ant colony ants:

It appears as though each ant came with a predetermined occupation: forager, patroller, or midden worker, each in their own respective area. With random motion, the some ants leave their own region but maintain the same percentage of 50% (forager), 25% (patroller), 25% (midden).

However, the numbers did fluctuate with time because as an ant encountered ants with a different "occupation" it would adopt that said occupation. Even with this technicality, the ratios still remained relatively constant.

It appeared as though a director could have been present to organize the function of the ants, but the ants clearly used hydrocarbons to organize themselves into the distinct percentages. The introduction of these hydrocarbons as dud ants would mislead the ants into believeing that there were more ants then there appeared and allow them to leave that region.

Similarly, the introduction of intruders functioned as a signal for the other ants to react. In this case, the ants would abandon their original "occupation" and remain in the same region until the intruders were eliminated. Intruders override the original occupation, suggesting a hierarchial activity structure.

Wolves and Sheep and Some Grass:

There was an inverse relation between the sheep and grass because they work together in a prey and predator relationship, where the sheep eat the grass , causing the grass population to decrease. This inevitably causes the sheep to starve and decrease in population.

Along these same lines, the sheep and wolves are also inversely related. There is always a greater number of sheep then wolves and always a greater number of grass than sheep. It should be noted that there is also a hierarchial construction here because of carrying capacity. As you move along this food chain of grass to sheeps to wolves, there is a general decrease in the amount of available energy.This forms a triangular shape in biomass where the grass population is at larger, bottom portion, the sheep in the middle, and the wolve at the top.

kcough's picture

death by numbers

Kaitlin Cough, Elizabeth Harnett

1.) Ant Colonies: Social Organization

We saw that ants, while they do have a specific social organization, do not have a leader or director. Rather, they are all the same and all capable of doing each job in the colony. At any one time, there is generally a stable 50/25/25 ratio of ants doing each job, and when there are disturbances or changes in the environment the ant colony will try to maintain this ratio. This is due to the fact that ants emit hydrocarbons, each of which is slightly different for each of the different jobs. Every ant is capable of emitting each hydrocarbon and changing them at any time. Which one they emit, and which job they do, is dependent on them meeting 15 other ants within a set period of time--if they meet many who are doing the same job, they will switch jobs, and if they meet few doing their job, they will continue doing that job. Older colonies of ants adapt to disturbances, such as intruder ants, more quickly than younger ones and are able to stabilize the ratio faster simply because there are more of them, and therefore the 15 encounters happen sooner. They do not have a collective memory or history. Basically, we found that each ant is not different from the other, but by working together the colony has different traits (jobs). The different traits are made by the parts (ants) interacting with each other.

2.) Wolf Sheep Predation

The environment is stable when both the sheep and the wolves are dependent on a fluctuating energy source. The website mentioned that it was possible to create a stabilized environment without the sheep being dependent on the grass, however every time we tried to create a stabilized environment by either changing reproduction rate, energy rate or the number of initial sheep/wolves the sheep would usually grow exponentially whereas the wolves would become extinct. We could not figure out a way to create a stable environment when the sheep were not dependent on an energy source like the wolves. We concluded that to create a stable environment, both populations need to depend on a fluctuating food/energy source.

How much can we make sense of using computer models? What role does randomness play in them?

We think that randomness plays an important part in some of these systems. For example, in the Wolf Sheep Predation, though the beginning of the trial would fluctuate and look random in the end a pattern would be created. One population would increase whereas the other one would go extinct, or they would stabilize (with the grass). This reminded us of the Langton’s Ant that we looked at in class: though it started out looking random, eventually a pattern was created. Maybe on an individual level it is random, but as a group (on a larger scale) a pattern will be created, depending on the environment that the trial is taking place. Computer systems provided a good way for us to see the change of populations over time, and they account for a lot of different environmental variables.

ekim's picture

on ants and rabbits.

Vivian Cruz, Saskia Guerrier, Eurie Kim

We experimented with an ant colony simulation that dealt with tasks and behavoirs. From the "task" simulations, there were three ant-task-types: foragers, middle workers, patrollers. However, the ants did not stay in the task that they started out with, but were interacting with other ants and switching tasks, as well. There was an obvious pattern of distribution in task allocations: 50% foragers, 25% middle workers, 25% patrollers. Regardless of interactions and switching tasks, this pattern of distribution stayed the same, showing that all ants are created equal. However, what causes this stabilization? What appeared to be the work of a director was actually the case of ants interacting and communicating through chemicals. The chemicals differed according to task; and thus, the more interactions the ants made with a certain chemical, they would switch task, showing the autonomous movement of tasks and the stable pattern of task distribution of 50-25-25.
In respect to those interactions, their behaviors change over time, showing that older colonies interact more and can establish equilibrium (the stable pattern of task distribution) more rapidly due to experience and due to the higher number of interacting elements.

So from the ANT COLONY experiment, we learned that there is always change due to constant interactions, regardless of having similar or different individuals in a populations.

In this experiment, we observed the rabbit population oscillate in accordance to the amount of energy that allowed them to reproduce. So there was a constant cycle of rabbits eating the available grass, gaining energy, reproducing, which depleted the grass resources, which then decreased the rabbit population, which then increased the amount of grass availability, and then more rabbits, and so on and so forth.
This predator-prey system maintains the ecosystem.

Ruth Goodlaxson's picture

Samar and Ruth

Today in our exciting lab we explored an ant colony filled with tiny little creatures named 'ants'. The ants were distributed among 3 jobs: forgers, patrollers, and middens. 50% of the screen was the area in which was devoted to the forgers and 25% was distributed to the patrollers and the remaining 25% was distributed to the middens. At an even time, 50% of the ants were forgers, 25% were middens, and 25% were patrollers. We needed to account for this.

Our results are as follows:

1) When an ant meets 15 other ants doing the same job, it switches to another job.

2) When there is an increase in the area for any given job, more ants will have that job. The ratio changes because the concentration is lower and it takes longer to meet 15.

3) Larger colonies will adjust to change and the ratio stabilizes faster due to the fact that meeting 15 is now easier.

These rules help safeguard against intruders becuase the number of ants in a certain task can only drop to a certain point which allows for recovery.

Randomness is important becuase the ants move randomely but are ordered by the rules. The tendency to be evenly distributed is maintained by the rules of order but it's also the most probable state and adjusting the factors that order the ants change the distribution and act like membranes in that the assembly becomes less probable.


In the AIDS program we looked at how the rate of condom use affected the number of people who were infected with AIDS. Our results were as follows:

1) 0 condom use after 1083 weeks: 81.33% infected

2) 5 condom use after 1102 weeks: 78% infected

3) 10 condom use after 1068 weeks: 68% infected

Our results showed that there was not a great decrease in the amount of infection. We believe this is due to the fact that the variable were probabilistic and that randomness played a part.

Catrina Mueller's picture

Catrina Mueller and Rachel Mabe

Generally, things in nature tend to come to an equalibrium at some point or else something will "die out". With the ants, even when all the ants started in the same job, the ants eventually reached the 50%/25%/25% proportions. When disturbances such as extra hydrocarbons were added, eventally the proportions reached equalibrium, despite the fact that there were "dummy ants". For the bunny/weed/grass simulation, either the bunnies and plants reached equalibrium or all the rabbits died out.

Even though the ants moved randomly, there was always a 50%/25%/25% ratio, which makes it seem like they the ants were moving with intention. In fact, this ratio is the most probable, as the ants are less likely to bump into the set amount of ants when they are the most spread out. Likewise, for the rabbit simulation, as long as the rabbits, weeds, and grass were not messed with, the rabbit population eventually became stable (or else died out), despite the fact that the rabbits moved freely and randomly.

For the ants, we did not understand why the older colonies had a sense of "collective wisdom" and bounced back more quickly from adversity. This happened in every situation where the ants were faced with a test: the older colonies seemed to react more efficiently.

The rabbits seemed to make more sense. No matter how much energy and the amount of plants that were avaliable, the rabbit population could stabilize (as long as there were enough plants to initially support them).

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.


Kendra's picture

Randomness in Computer simulations

Kendra Sykes

Ashley Savannah

The first computer simulation we looked at "Ant Colonies: Social Organization without a director". There were three types of ants in this colony that did three different types of jobs. The red ants were the foragers, the purple ants were the midden workers and the blue ants were the patrollers. The website provided many simulations that involved these ants but the one that we found most interesting was the simulation that included hydrocarbons.

We found out that hydrocarbon in an ant is a chemical that secrete from their antennae and is modified by their task at hand. When two ants come into contact with one another, one would give off a signal using their hydrocarbon secretion telling what job they are doing which will in turn cause the other ant to switch the job they were originally doing. At the end of this lab, we realized that no matter how many times the ants came into contact with each other, that the ratio of ants (50% forager, 25% midden worker and 25% patrollers) remained constant. We realized that there is not much randomness involved in the work distribution of this colony, that over a period of time, the ratio becomes a constant.

The next computer model that we focused on was called "Virus". In this simulation, we followed the rate in which people were infected by a virus based on the percentage of infectiousness, chance of recovery as well as the duration of interaction. We observed that when the rate of infectiousness was lower than the chance of recovery that more people survived and more people became immune to the virus. But, when the rate of infectiousness was higher than the chance of recovery, more people became infected and eventually died. We feel that this a reflection on real life but is dependant on the type of virus and this makes a significant difference. There are three different types of viruses: if someone comes in contact with HIV, they will most likely get it. If a person gets influenza, they will keep getting it but can recover from it. Then there is the chickenpox, where you get it upon initial contact, but become immune to it there after.


OrganizedKhaos's picture

Computer Games of Life

Kerlyne Jean

Marie Sager

For this lab we observed two different games in which randomness was observed.

1.) Ants: Social Organiztion

We first observed the Ant Colonies game where we played with the idea that ants may have a destiny. There were three destinies hypothesized foragers, patrollers, and midden workers. We first viewed a group of ants and noticed that a balance was kept among the ratio of different workers. This led us to believe that they may actually have a destiny. We then looked at one ant to see if it did stay within its "detined" job. The one random ant that we observed moved from all three jobs randomly. So, we then thought it may not be destiny.

We then found that ants use hydrocarbons to communicate with one another. By playing with the level of encounters one ant had with another we were able to see that the job of an ant is not totally random and based upon the amount of other ants doing the same job around it. For eaxample, one ant may be a forager and happen to wander closer to the midden workers. If more midden workers come into its vicinity (approximately 15) that ant then begins to do the job of a midden worker. This shows that ants communicate and through that communication have designated jobs which change as threshol number changes.

2.) Daisy World:

In Daisy World there were two types of daisies. The white daisy preferred colder temperatures and had a lower albedo level. In contrast, the black daisies preferred higher t emperatures and had a higher albedo levels. We toyed with three different factors within this world to come up with some conclusions.

Firstly, we set the population of both daisy groups to be equal. We hypothesized that they would balance the temperature and the populations would stay equal. While observing we saw that they were able to keep the temperature stable because one was competing for high temps while the other competed for low temps. The two populations increased and decreased and took turns being on top. At one point the white daisy population was higher while the black daisy pop. was lower and vice versa.

The second aspect we played with was albedo. We wanted to see whether the amount of energy taken in by one group was a major factor. We increased the black albedo and noticed that it had a threshold where they would all die one point. We also noted that since the black albedo is naturally higher than the white daisy it absorbed more energy and was able to get the desired higher temp and kill off the white daisies.

Lastly, we experimented with solar-luminosity. We lowered the level which decreased the temp killing all daisies then did higher level of luminosity which also killed all the daisies. The last test was changing solar-luminosity in intervals in which we observed that they were able to recover after a temperature increase if the temp. decreased within enough time.