Our Goal

The Philoscientists are dedicated to bringing you original ideas and content related to the hard and social sciences. We are dedicated to bringing you food for though ranging from psychology and sociology to physics and chemistry while trying to tie it all together and look for meaning in it all.

Thursday, January 8, 2015

Chaos in Economics: Part 1

To kick off the start of Philoscientists, we the writers have decided to do a series on Chaos theory in the Social Sciences and try to make the claim to disprove neoclassical economics. Full parenthetical citations are included, so I will include the references on this first post that can be referenced for the rest of the Chaos in Economics Series. ALL work is original and all research has been verified as credible. Enjoy!

We live in a universe full of interacting dynamic systems where any particle has the ability to effect any other particle in existence.  These ideas come from some of the most basic tenants of Chaos Theory.  Human nature makes us want to figure out how all of these systems work, and through economics it becomes possible.  In today’s society , Chaos Theory is not taken into account nearly enough, and while many things in our world are modelable, large, long term economic models are relatively inaccurate for this reason.  Neoclassical economics should not be a lost field of study simply because it is not as reliable as we once thought, but instead we should try and take steps to include Chaos Theory principles and  it’s implementation.
Humans have long been operating within the field economics in regards to what we know about the economy and trends that apply to it.  In fact, neoclassical graphs and theories are utilized by the Federal Reserve and the United States Congress consistently for taking action to stabilize the economy during recessions, as well as to maintain sustainable growth during growth periods.  From the 1900’s and until the beginning of  the development of Chaos / Complexity theories, neoclassical economics was routinely modeled in a very linear universe, where few factors besides exchange of currency were evaluated.   Although Chaos theory is difficult to define it fundamentally incorporates observations and theorems that attempt to explain the chaotic nature of existence.  
A stylized Lorenz Attractor (discussed in later post)



Kurt Godel, a mathematician is seen as one of the great founders of Chaos Theory (Gleick, 1987).  In Godel’s day, the mathematical community was pushing to theoremize, or define all the rules, of mathematics so that there could be something of a mathematics bible that could be referenced to solve any problem in existence (Jones, 2014).  Mathematicians believed that if their field could be completely explained, all of existence down to the atom could be theorized in every scientific field.  Godel saw that this was completely wrong, and realized that we would never be able to find every rule, so after working several problems to prove his point, he published his Incompleteness Theorem.  It states that there are infinitely many theorems in existence, meaning that no matter how many we discover, we will never be any closer to putting all of mathematics into a book or even collection of books (Jones, 2014).  Other major pillars of Chaos Theory include sensitivity to initial conditions, commonly known as the “butterfly effect” and the idea that nothing is static and everything is constantly changing (Gleick, 1987).  A key feature of Chaos theory is that it has been theoremized to a certain extent, meaning that through an academic and scientific lens, essential  components of the theory have to be accepted as fact, and the way that chaos impacts all systems must be taken into account (Gleick, 1987).

References

Bertuglia, Cristoforo Sergio., and Franco Vaio.  Nonlinearity, Chaos, and Complexity: The Dynamics of Natural and Social Systems.  Oxford: Oxford UP, 2005.  Print.

Cassidy, David.  "Quantum Mechanics, 1925-1927: The Uncertainty Principle." The American Institute of Physics.  American Institute of Physics, n.d.  Web.  07 Dec.  2014.

Gao, Mingming, Jingchang Nan, and Surina Wang.  "A Novel Power Amplifier Behavior Modeling Based on RBF Neural Network with Chaos Particle Swarm Optimization Algorithm." Journal of Computers 9.5 (2014): 1138-143.  Applied Science & Technology Source.  Web.  21 Oct.  2014.  

Gleick, James.  Chaos: Making a New Science.  New York, NY, U.S.A.: Viking, 1987.  Print.

Jones, Judy.  "Gödel’s Incompleteness Theorem." N.p., 14 Jan.  2014.  Web.  8 Dec.  2014.

Oxley, Les, and Donald A.r.  George.  "Economics on the Edge of Chaos: Some Pitfalls of Linearizing Complex Systems." Environmental Modelling & Software 22.5 (2007): 580-89.  Web.  20 Oct.  2014.

Sohrabi, Sahar and Nikkhahan, Bahman.  "Chaos in a Model of Electronic Market." Journal of Convergence Information Technology 6.12 (2011): 171-76.  Ebscohost.  Web.  21 Oct.  2014.

Sorin, Vlad.  "Chaos Models in Economics." Annals of the University of Oradea 17.2 (2008): 959-64.  Economic Science Series.  Web.  20 Oct.  2014.

No comments:

Post a Comment