Complexity lovers and learners alike will fall in love with the new Complexity Science magazine on Flipboard. With selections from Jake David and other contributors, this magazine aims to “observe complex and emergent phenomenon in science, society, and art.” In other words, it brings together articles that might not seem related to Complexity Science together for the complexity reader.
One important aspect of Complexity Science is the idea of “emergence” or “self-organization,” where simple rules or circumstances lead to events that are not scripted. In other words, where things seem to take on “a life of their own.” The iOS Apps Life Game Touch and LifeGameHD speeder do a good job of demonstrating the cellular automation “game,” Conway’s Game of Life.
Advantages and Disadvantages of Simulation Applications of simulation are helpful to scientists and researchers, but they come with a set of advantages and disadvantages.
Want to learn more about money? A U.S. Dollar probably isn’t what you think. This Mises Institute article is a good overview of the difference between a Dollar and a Federal Reserve dollar bill. Mises Institute: A Constitutional Dollar
It can be hard to find great live visualizations of the Genetic Algorithm, which is a popular and well studied method for providing adaptation to a Complex Adaptive System, but this is a superb example. Created by Rafael Matsunaga, this “Genetic Algorithm 2-D Car Thingy” simulation takes polygons of varying parameters (initialized randomly) and gives each two “wheels” of varying sizes. All of the “cars” are dropped in to the environment and the most fit cars are selected for the next round.
You always hear people complaining about a the fact that a Free-Market has boom and bust cycles, and how that is a bad thing. The reality is that you really do want the market to boom and bust, whether you know it or not.
The marketing world has changed. Previously we tended to assume a direct consequence of a marketing initiative on a consumer (hell, even proved it qualitatively and quantitatively), and the trick was to scale this as widely as we could. One consumer sees an ad we know they like, buys a product, therefore let’s get as many consumers to see the ad.
In the realm of Complexity Science, many people talk about how things are “Complex” but they aren’t “Complicated”. The problem is that the two terms can be hard to distinguish.
Evolutionary methods can be used to shine a light on the conditions for selfish or cooperative behavior. Imagine a situation, where you have to work together with a team of random strangers. The outcome will be depending on the sum of the individual efforts, but the success will be equally shared afterwards. In a computer experiment, we have investigated the evolution of cooperative behavior in two scenarios. Players were randomly divided into groups and had the chance to increase their investment by paying money into a pot where it was multiplied. The players were controlled by a neural network…
Behind anything that is truly complex there are simple rule sets. This video includes demonstrations of cymatics, which is the visualization of sound waves.
This video from TedTalks is a good overview of the concept of complexity. This is a video from www.ted.com.
In today’s global economy, we can’t begin to count how many transactions between buyers and sellers happen each day. But who decides the individual costs of all of these goods and services? Why do those costs change over time? Why are some things so ridiculously expensive? In a free-market economy, prices are so much more than the amount of money we pay when we buy something.
Before we discuss the role of prices, I will set up a thinking experiment that we will use in subsequent discussions about the role of prices.
If you take a purely determinate function and iterate it over time difficult-to-predict behavior can emerge. If you only use the same function, then the outcome is easily determined and therefore not complex. But if you use more than one determinate function or a set of rules with simple determinate functions then difficult-to-predict behavior may emerge.
In order to begin to understand complexity, we need to discover what complexity is not. Complexity is the region between the polar opposites, Determinate and Probabilistic. Something determinate can be predicted with certainty. Something probabilistic can be predicted within some statistical bounds (like the flipping of a coin). While complexity includes elements of both of these, it is neither determinate nor probabilistic.