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Reading Reflection 1
Throughout the first portion of this semester, we set up many ideas essential to
understanding self-organization. I found the most compelling ideas to be those that looked at
examples self-organization from it's simplest processes and expanded these processes to produce
complex phenomenon. As I studied these processes I began to feel driven, as I suspect many
others did, to use build on these ideas to create something purposeful - something that is built as
a complex system but can be made practical through artificial interaction. Kauffman is a unique
individual in that he saw these complex systems and used his extensive medical knowledge and
intuition to rigorously test and apply the ideas behind complex systems to medical science and
our understanding of the human brain. Through the exploration of fractals and complex systems I
believe we may be able to create several practical systems based on complex systems.
My experience with fractals did not begin with the Cantor or Koch curves as many
others who were being newly introduced to fractals did. Instead my experience began with a
program called "Mandelbulber" which I discovered by chance reading an article online a couple
years ago. I instantly fell in love with the unique complexity of 3 dimensional fractals. Although
these fractals were not composed of systems I could easily, if at all, understand, I loved the idea
behind generating this seemingly endless 3d world. Luckily I'm not alone in this sentiment many believe fractals to be very natural and unique in an fascinating way.
It wasn't until this class that I discovered the concepts behind how the mandelbulber
was created. The Koch and Cantor curves taught me that simple concepts (i.e. repeating a curve
over each segment of the previous curve) can create natural looking systems. Immediately
following the several classes dedicated to understanding the basics of fractals I attempted to
implement my own koch curve in the Google Dart web programming language. I was actually
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surprised at the amount of trigonometry and math required to create the custom koch curves that
we experimented with in class. I would think replacing a line with a curve would have an easy or
at least mathematically simple implementation. Hopefully my investigation of the koch curve
and two dimensional fractals will eventually lead me to a mathematical formula that is simple
enough where I feel it could compose some kind of universal law (even if this law is restricted to
the abstract 2d space of a screen).
Perhaps it's this same expectation of a simple solution to everything that creates such
resistance to fractals and leading theories that just seem too intricate to be a universal
phenomenon. Although I cannot claim to be completely cleansed of this inclination, I feel that I
can now understand that a complex system can be just as natural or universal as a simple system.
The readings regarding the self organization of the immune system and ant colonies greatly
changed the way I perceive the intricacy of a system.
Considering the function of any individual member of a system, it's difficult to see why
anything it does serves a purpose. For example, the ant in an ant colony leaves behind a scent
when it is returning food to a nest, wanders off randomly (often to die) and switches jobs within
the colony seemingly unprompted. However, look at the system from a broader perspective it
makes perfect sense that an ant would leave a trail for other ants back to food it found, wander in
random directions to find food, and switches jobs when it receives a chemical signal indicating
revised priorities. So this ant colony system, much like a flock of bird, is guided by a very simple
set of rules that, when followed, create complexity. This idea is easy to accept because we can
understand all the parts of the system and how they interact. We can look at the system from
both the bottom up and the top down, this makes it easy to understand.
Looking at a complex system where we cannot possibly understand the interactions
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between the components yields entirely different results in credibility. For example, the neurons
of the brain that Kauffman tried to model with his binary networks represent a system that has so
many players that despite understanding the basic principles of a neuron is still impossible to
derive any function from. I believe (and this is one of the biggest conclusions I've come to so far
in this course) that there are systems that can be easily modeled in their most simplistic state but
cannot possibly be fully modeled due to massive amount of inputs over time and the incredible
amount of computational energy that has been exhausted through thousands upon thousands of
years of evolution. In biology courses in high school it seemed like it would be a gradual process
of studying and mapping the brain to eventually learn how it worked. But I now know this is not
the case.
Complex systems with thousands of players may prove nearly impossible to model, but
surely we can predict simple systems comprised of only a few equations. When studying chaos
theory I learned that even simple systems composed of simple equations can exhibit behavior
that is too complex to be modeled. Some equations have a chaotic attractor, this means that they
approach a state of near randomness as the input variable changes. Other equations exhibit the
butterfly effect, that is, they have an extremely high sensitivity to initial conditions which means
that output will drastically change with even a small change in input. It's this phenomenon that
makes weather so difficult to predict.
Mitchell illustrated in the chapter about genetic algorithms that, faced with a
complicated problem, it's possible to simulate the natural selection process with solutions as the
individuals in a population. This was one of the most interesting chapters in the book simply
because the applicability of genetic algorithms was immediately obvious. Mitchell showed that
the genetic algorithm developed a method that was counter-intuitive but much better and faster
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than the solution she had made. This definitely shows a practical application of a genetic
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algorithm, but it was a bit disappointing to see that the genetic algorithm had to be created in a
way that seemed extremely bulky and unnatural. Complex organisms start from a bunch of small
functioning sub-systems then grow and become more complex and adapted. The genetic
algorithm that Mitchell described had essentially an array of how an individual in the population
would react to any environment. In many ways this seems to not really belong in the book, self
organization and complexity is all about bottom-up processing - simple systems composing more
complicated systems. But Mitchell uses the genetic algorithm to do top-down processing, where
she looks at all situations an organism will face and simulates how each will react.
Another huge change in my thought process that has occurred as a result of this course
is my thoughts as to how I would go about building a complex system. Before taking this course
I may have looked at what the system has to do, simple input and output, a broad view of the
situation. Now I think I would look at the simplest components that compose the system and how
they would interact and sort of form the rest of the system.
I feel like this perspective is underutilized for modeling many modern systems. As I
studied these systems and finished readings, I would get ideas for programs I could create that
would utilize these ideas to create interesting phenomenon.
One of these ideas is a 3d koch curve. Making the 2d koch curve just just a matter of
projecting a series of lines across another line and I don't see why this could not be done in 3d
space. Unfortunately, my attempts at creating the 3d koch curve have failed, I believe this is
primarily due to my lack of mathematical background with 3d vectors and lines. In the future I
hope to see how this koch curve could be used to model 3d objects, just as a 2 dimensional fern
could be modeled by the 2d koch curve.
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Many organisms besides the fern can probably be modeled with a 2d koch curve.
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Another idea for a program would be to find the koch curve for a given picture of an organism. If
it was effectively able to mimic the given photo, it is very likely that it would have a smaller
memory footprint and could be used to store the picture more effectively (I acknowledge that the
chances of this working are very slim).
Although I know I have only scratched the surface of genetic algorithms, I'm anxious
to attempt to implement some kind of system that uses it. Recently I've seen people test genetic
algorithms on online games such as "flappy bird." Hopefully I'll get the opportunity to learn
some new techniques for genetic algorithms and be able to apply them to a project by the end of
this course.
There are also many ideas similar to cellular automata that would be entertaining and
possibly informative to implement. For example, creating a small game with interacting cells
(hopefully cells that are more pleasing to look at than those in Conway's Game of Life) could
help educate kids on self organization and show how it is applicable to many everyday systems.
The concept of educating people at a high school level about self organization is both exciting
and, to my knowledge, a novel concept.
The first portion of this semester has introduced me to an entirely new field of
mathematics and computation that I never even really knew existed. Many of the concepts within
self-organization have fundamentally changed how I look at developing processes and systems.
Through the readings and knowledge I've gained in this class I can hopefully do more
experimentation with self organization and build an application that contributes to the unusual
but fascinating world of complexity.
Great essay, well written and thoughtful. I agree that google dart is the future—java
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is dying as we speak…. Grade = A
Information compiled from:
Mitchell, Melanie. Complexity: A Guided Tour. Oxford: Oxford UP, 2009. Print.
"Interview with Stuart Kauffman." Scientific American Global RSS. N.p., n.d. Web. 06
Mar. 2014.
Notes and in-class lectures
Classroom discussions
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