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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?

ktan's picture


Because so many ideas were discussed in lab today (from dropping out of school to purpose of education, to free will and  the prevalence of biology, from the social construct of morals to falling in and out of love), we found it very hard to articulate in clear, concise terms our varied opinions or to get it all "less wrong."

One of the models we looked at involved dots of red and green that changed color randomly and took on a "pattern" of movement. We observed the red and green dots eventually aligned themselves in particular arrangements, that we, as humans with a highly complex and developed brain, perceive as a pattern. "Patterns" seem to exist only because of the individual's power of perception and imagination. This is what leads us to believe a random alignment of colored dots is actually the emergence of a pattern. Humans impart meaning on situations that don't necessarily have meaning, because we tend to interpret alternate scales along our own. In other words, randomness and pattern are both constrcuts of the human brain, and this could also be responsible for why different people perceive the same occurence/phenomenon in different ways, why people observe different "patterns" among the same entity.

Another model we looked at involved choosing (maybe guessing?) between three doors to win a $5 prize. After playing with this application for a while, we observed we would figure out the "right" door and get the "wrong" door with a random probability. Because no set "pattern" was observed, we believe that there are some things beyond the capacity of the human brain, or beyond the comprehensability of science, as we understand it. We believe we can apply this same theory to beyond just the observance of patterns (or lack of?) to things on a much larger scale, such as abstract concepts of religion, love, morality, ethics etc (as discussed in class).

--Claire, Jesse, Krystel, Paoli, Emily, Yashaswini

mcasias's picture

Mariah and Heather Computer Model Lab Results

The cooperative cows died out at a moderate reproduction cost (54), though at first it seemed like the greedy cows would die out. The cooperative cows died out even after lowering or increasing the reproduction cost as well as lowering stride length. Even after increasing the max grass height, the greedy cows neared extinction before their population shot back up and the population of the cooperative cows declined until they died out. There didn’t seem to be a situation that was beneficial for the cooperative cows in the long term.  We had expected to find that the cooperative cow population would survive longer than the greedy cows, even if initially they had lower numbers. There still may be a situation in which the greedy cows die out, but it appears that cooperation provides no long term benefit to survival in this model. This computer model seems to be useful for understanding the relationship between cooperation and resource availability, but there still could be other variables that would promote cooperation in cows that are not included in this simulation.  However, there still is the possibility that there never is an advantage to cooperation or altruistic behavior and so using a computer model has given us reason to reevaluate our assumptions about cooperation.

One of the simulations we had the most difficulty understanding and in successfully manipulating the variables was the Simple Birth Rates model. At first we attempted to change the blue and red fertility rates, but found that the two fertility rates had to be the same or else the population with the lower fertility rate completely died out. We then altered the carrying capacity, but still found that if there were different fertility rates one group would always die out.  Even though this was not a very complex model, we thought the results showed the delicate balance between the coexistence of two populations. Practically speaking, we don’t see any evidence of this in our real world experiences, but perhaps this phenomenon occurs more slowly than we are able to observe.


jmstuart's picture

 Today we looked at two

 Today we looked at two different computer models that simulate population dynamics in animals. The first was a simulation of an ant colony with three different roles for the ants to assume: foragers, debris cleaners, and border patrol. Roles were determined by position in the ant world (i.e. ants on the left were always foragers, ants on the upper right were always debris cleaners, and ants on the lower right were always border patrol). All the ants in the simulation secreted task-specific hydrocarbons constantly: foragers secreted forager hydrocarbons, debris cleaners secreted debris cleaner hydrocarbons, and border patrol ants secreted border patrol hydrocarbons. When ants came in contact with the hydrocarbons of other ants they responded to them; forager ants were repelled by the hydrocarbons of debris cleaners and border patrol, etc. In this way the secretion of hydrocarbons became a kind of inter-ant communication.

Conclusions from this section are as follows: 

Ants inform/create their environment by secreting specific hydrocarbons. They are affected by the information (hydrocarbons) put into the world by other ants. We can logically extend these findings to a metaphor of life. Biology (represented by the hydrocarbons and their secretion, a totally blind, unconscious process) informs the environment of an organism. The change in environment initiated by the organism (and the organisms around it) affects the expression of that organism's biology. In this way, our experience of life (i.e. our identity) is a dialogue between biology and experience. It seems to be some sort of compromise.

The difference between human life and ant life is consciousness, or self-awareness. Consciousness would be responsible for the synthesis of both sides of experience (biology and environment) into a "story" of reality. This consciousness would also be responsible for the introduction of intention on a system. Intentionality would in turn affect the environment, further entwining the world and our experience of it.

The second computer model we examined was a predator/prey relationship between sheep and wolves. Grass could also be added as another variable to the environment. Unlike the ant population, this simulation shows the delicate natural balance between populations in an area, and how different factors such as reproduction rate and energy gained from food affected these dynamics. Unlike the almost automatically stabilizing population of ants, the sheep-wolf system was less likely to return to population equilibrium after an environmental shift, which in some situations resulted in the extinction of one of both populations. This difference in the probability of equilibrium between the two systems highlights the importance of reciprocal interactions (i.e. the sheep cannot "bite back," they can't affect the wolf population directly, and thus reducing the probability of equilibrium).

Two things struck us as interesting after observing both of these experiments. Both of these experiments stress a lack of a directional force, and show the random nature of biology. However, the ants ended up in an incredibly organized and effective pattern while half the time either the wolves or the wolves and the sheep ended up dead. One contributing factor to this difference is the presence of more variables and the interdependence of a ecological system as opposed to a single population.

Julia Stuart & David Richardson

dchin's picture

Lab 10

dchin, kalyn, karina
Ant Colony
From the Ant Colony simulation, we learned that the specific purposes of each ant group depend on environmental factors and the needs of the colony. In order for the colony to be completely functional, it necessary for different ants to have different tasks so that the overall needs of the colony can be efficiently met. They can manipulate the environment better when they are working together as a group. The simulation demonstrates the never-ending circle of life. The actions of the ants are dependent upon their environment just as the environment is affected by and ultimately meets the needs of the ants. While experimenting we discovered that altering the numbers did not affect the initial separation of the individual ant groups. The 50-25-25 ratio did not change even after we took out the middle and patroller groups. The gatherer ants all switched roles to meet the demands of the colony while maintaining their jobs as gatherer. This is an example of our assertion that the ants are constantly interacting with the environment as well as adapting to it. This is consistent with our understanding of evolution, especially in regards to the branching out and eventual narrowing of traits. Perhaps in the past, there was a larger variance among the different groups and numbers of ants, but as the ants explored different ratios of patroller, middle, and forager ants, they eventually settled on the 50-25-25 ratio because they found that it helped them to survive.
From the Wolf-Sheep simulation, we saw that there is a delicate balance between different parts of the ecosystem. Any time we adjusted any one factor, there was a dramatic surplus of sheep, grass, or wolves, but then eventually, it led to the extinction of everything. The wolf population controls the sheep population, and the sheep population controls the grass. You would think that having too many sheep would be good for the wolves, but in actuality, this decreases the amount of grass available which eventually kills the sheep population decreasing the wolf population. Therefore, we had to find a balance between the factors. For example, if we had more wolves than sheep, then the sheep had to have a higher reproduction rate. These ties into the ant colony which exist only when they manage to successfully manipulate their environment result in a circle of life with everything being dependent on everything else. The actions of the sheep coincide with the future of the wolf population. The sheep population reproduces at a higher rate in order to meet the demands of the wolves that eat the sheep for food. This is similar to the ant colony that finds its members switching roles when the need arises. Without the wolves there is no control over the number of sheep. We witnessed this when a never ending reproduction of sheep started when the wolf population was not present. One thing we did not understand was why the sheep were so resilient. Despite turning off the grass and increasing the number of wolves, the sheep still continued to survive and reproduce.

achiles's picture

While we looked at several

While we looked at several models to explore social patterns and randomness, there were two that were much more pertinent than the others.

In the case of the ant colony, it was established that ants, without any director, maintain an equilibrium among the three jobs in an ant colony. At all times, ant were striving to reach a balance of 50% to 25% and 25%.This was discovered by removing certain groups from the colony and observing that the remaining ants immediately compensate for the missing ants to maintain a balance of 50/25/25. It was noted that when hydrocarbons are introduced into the colony, ants perceived them as other ants and, thus, did not maintain equilibrium. Through this experiment, it is observed that ants have an inherent social awareness which drives them to establish certain work/social patterns that are larger than their understanding.

In contrast to the very prescribed, organized practice of worker organization in an ant colony, the case of the AIDS experiment previewed human's use of personal and individual choices to guide social patterns continue or stop the spread of AIDS. We observed that several aspects had varying outcomes on the number of "participants" who contracted and eventually spread AIDS to others. The measurable/controllable aspects were:

Coupling Tendency (out of 10), Average Condom Use (out of 10), n or number of participants in the dating pool, Average Commitment (in weeks),  and the Frequency of HIV testing. We found the following:

The more people, the faster the spread

The less condom use, the faster the spread

The higher the incidence of coupling, the faster the spread

The lower the average commitment, the faster the spread

The more frequent the testing, the slower the rate of spread

The less frequent/more realistic the testing, the faster the spread

Test situation: reality-based- given a small collegiate social environment

Coupling Tendency= 7

Avg. commitment= 4 weeks

Condom Use= 3

Testing= .35

100% HIV infected in less than 250 weeks

Why are our findings not 100% accurate? The problem with using computer technology to simulate social patterns is that free choice cannot be accounted for and complete generalizability cannot be achieved. Though love and attraction are a result of chemical/molecular reactions, sexual interaction can in no way be predicted.



Anna Chiles, Jen Pierre, Maria Miranda

Terrible2s's picture

Doors and stuff

Today in lab we did two different computer games (/experiments). We tried the "3 doors of serendip" game and the "sheep wolf predation" game. The sheep game seemed pretty straight forward, and the results were as expected. There were 3 general things: sheep, wolves, and grass. Wolves eat sheep, sheep eat grass. If you change certain variables (like amount of sheep, reproductive rate of wolves, etc) it affects the populations of the things. We played with this applet for a few minutes, but we were more interested in the three doors of serendip applet.

First, the screen showed us three doors, one labeled "1", one labeled "2" and one labeled "3." When you click one, the computer automatically eliminates one of the other two doors and says that there is no money behind the door it eliminated. Then, it gives you the option to "stay" with the door you chose in the beginning, or "switch" to the door that the computer did not eliminate. Either way you choose, you have the potential to lose money or win it. When we alternated between "stay" and "switch," it seemed pretty random; we both won and lost money. However, the game said that there was a pattern and we wanted to figure out what it was. After extensive discussion, testing, and help from Professor Grobstein, we learned that there was a 2/3 probability of winning money if we "switched" and a 1/3 probability of winning if we "stayed." Professor Grobstein gave us two "stories": The first story was more verbal and conceptual, while the second involved numbers. It was interesting for us because neither of us like math, but the number story made a lot more sense!

Lili, Terrible2s