Guten tag, all.

I just had an awesome weekend.  I attended San Francisco Science Hack Day  and it was awesome!  It was two full days, so let me lay it out for you:

about me: programming & science n00b
mission: to make & hack things with SCIENCE
vision: to hack data from Switzerland’s Large Hadron Collider for better accessibility and understanding, esp for enthusiasts but n00bs like me.

results:

• my knowledge of particle physics went from 0 to … some? probably 1 if it’s on a scale of …100.
• crash course in python and javascript programming
• +5lbs from bountiful free food and alcohol
• Increased tolerance to caffeine
• I made THIS!
• The group collectively put THIS website together!
• 10 GBs of LHC data, out of the gazillion terabytes of data both available & unavailable to the “public” (I say with quotes because not exactly sure where I could even get the publicly released data…)

Note: If you’re interested in the source code, it can be found on GitHub under mattbellis.

So, what those two graphs mean that I put together is [I THINK, IF I REMEMBER CORRECTLY] a reflection of two different ways to calculate mass of muons. Muons produce when two protons collide together, along with other particles, including mesons. The second graph (using Einstein’s equations/special relativity), you can see a second “hump”, signaling that there actually is a second meson existing that the first graph didn’t show.

All ‘n all, thanks to my intro course to computer science, I was able to have somewhat of an understanding of what was going on, as well as built upon my skills (especially for my final project coming up!).   All I have to say for those n00bs to any new skill: Dive right in.  That’s the best, most fun way to learn!

♠

Other hacks that were done over the weekend here.

While occupying my usual spot at the library, I came across a posted quote on an adjacent table attributed to Albert Einstein:

“The monotony and solitude of a quiet life stimulates the creative mind.”

I’m quite fond of it, and I think it encapsulates my current attitude.

While I am still adjusting to the horrific transit system in the Greater Seattle area, I’m trying to spin a positive on it.  I’ve been given the gift of time, woo…

So now I’m reading.  A lot.  But it’s great – I’m completely enthusiastic about enhancing the field of economics through borrowing from other science disciplines, i.e. psychology, decision sciences, physics and thermodynamics.  A while ago I came across complexity economics, which is based on complexity science and the notion that there is no sense of equilibrium within closed systems (the traditional view of economics) but rather open, dynamic, adaptive and complex system that evolves.

In an effort to educate myself on the foundations of complexity science (I am still trying to get a grasp on entropy), I picked up Deep Simplicity by John Gribbin.  With his digestible prose, I breezing through this, but fully involving myself with an eye for application with economics.  This sh*t is awesome!

More specifically, he draws up Lorenz and his discovery/development of the butterfly effect (coincidentally, or not, there’s a behavioral sciences/econ book called the Butterfly Effect that draws on complexity sciences.  I should pick that up again).  What was interesting with Lorenz is his discovery was that it is painfully difficult to forecast weather without the precise initial conditions.  Lorenz was playing on these new fangled devices called computers in the 50s when he made the realization that if he inputs numbers that aren’t precise, that is, that aren’t to the “correct” or appropriate amount of decimal places, the forecast/predictions will progressively be off of the more precise input in a nonlinear fashion.  If he couldn’t get the exact initial conditions (which would be decimal places off to infinity), he could not predict weather beyond a few days.

This is what came to be known as the Lorenz/butterfly effect.  I’m not sure how best to describe it…..so, I won’t.

My actual point is that I can see the Lorenz effect being applicable to the realm of economics.  First, it has to be impossible to model “the economy.”  Macro/micro economic models simply can not contain every agent, every being, every cent, every decision.  At best, models use probabilities and samples, and supposedly economists settle for this.  But as I read this book, models based on probability, samples and approximations are crude, so much so that it hardly gives a solution pointed in the right direction.  What if economists were to push the mathematical limits of the foundation of their science and rather than have the idea of economics be a social science, have a sense that it’s a hard science, like physics.

What if economics had the assumption of chaos, rather than equilibrium?

Oh.Em.Gee.  My mind is being blown and I <3 it.