I want to take a minute and talk about “systems”. When I speak of a system, I am talking about any collection of nodes and connections. Examples of systems include computers in a network, employees in an office, and puffs of air in a thunderstorm. A node is an individual unit in the system and a connection is the interaction between two nodes.
Let’s look at some generic systems.
Did you notice something about the four node example? It has more connections than it does nodes. In fact, four nodes is the minimum number of nodes needed in a system to achieve the distinction of the potential for more connections than nodes.
A partial and simple definition of a Complex System is a system that has more connections than nodes.
How does this relate to CIC? Well, with regards to CIC, the Creativity and Information reside in the nodes. Communication provides the connections. Let me say that last part again. The “Communication” component of CIC is equal to the connections the nodes in a Complex System.
Now let me introduce Metcalf’s law:
Metcalfe’s law states that the value of a telecommunications network is proportional to the square of the number of connected users of the system (n2) (emphasis added).
“Proportional to the square” means that as you increase the number of connections in a system, the value of that system increases exponentially.
Let me show you with some numbers. Don’t get hung up on the units of value. For now, let’s just see how the “value” changes in system as we add nodes and connections. Let’s use our example from above of a four node system.
Value of 3 nodes, 3 connections = 32 = 9 value units
Value of 4 nodes, 6 connections = 42 = 16 value units
Value of 5 nodes, 10 connections = 52 = 25 value units
Value of 6 nodes, 15 connections = 62 = 36 value units
We added 3 connections to the network and increased its value by 400% (36/9).
This is likely conservative. A more applicable law for our purposes might be Reed’s Law, which values a network based on potential sub-groupings and results in a 700% increase in value from 3 to 6 nodes.
Now let me ask you a question? What do you think happened when the internet went main stream and added what was in effect an infinite number of connections to the system of computer users?
Back to our example, what if instead of adding three nodes and the associated connections, we add 400 million nodes and the [400,000,000*399,999,999]/2 connections that go along with them. Remember, just adding 3 nodes increased the power of the network 400%.
Do you see why CIC works? Why adding C to CI makes such a huge difference? Do you think you might have underestimated its potential power?
Now, with regards to Metcalf’s law, that actually applies to any system and not just Complex Systems. So what is the big deal with Complex Systems? Before I get into that, let’s add a word. Let’s look at Complex Adaptive Systems.
A Complex Adaptive System is a special case of complex systems. They are complex in that they are diverse and made up of multiple interconnected elements and adaptive in that they have the capacity to change and learn from experience.
Examples of complex adaptive systems include the stock market, social insect and ant colonies, the biosphere and the ecosystem, the brain and the immune system, the cell and the developing embryo, manufacturing businesses and any human social group-based endeavor in a cultural and social system such as political parties or communities. This includes some large-scale online systems, such as collaborative tagging or social bookmarking systems.
So, again, what is the big deal about Complex Adaptive Systems? Everything. But let me pick just one aspect here and I can best explain that aspect using an example.
Imagine that I want to model a thunderstorm in a computer program. How would I do it? It turns out that I could set up a collection of virtual “puffs” of air. Then I would simply provide a few simple rules on how those puffs should interact with their neighbors.
For example, I might program in how a puff should change its temperature depending on the temperature of the puffs to its right and left. Same for pressure and humidity.
With the rules determined, I would begin by setting a starting value for the temperature, humidity and pressure for each puff of air. Then I would “turn on” my model and let each puff react. The way that would work is I would allow each puff to go through an iteration of the rules. Then I could stop and assess the system before going through iteration.
There are two very, very important things to notice about these rules.
- They are simple in that they apply at the level of the nodes or puffs or air.
- The same rules apply to all the puffs of air. That is, there is just one set of rules.
So what could I expect to observe? Here is the amazing thing. With certain set of initial conditions, I might observe not much at all –a beautiful, spring day. With a different set of initial conditions, however, I would observe storm fronts developing in my model. I would observe lightening and thunder. I would observe heavy wind.
So the obvious question is where did the storm fronts, lightening, thunder and wind come from? Remember, we only programmed in simple rules that applied to single puffs of air based on their neighbor. The answer is that the thunderstorm is an emergent behavior.
An emergent behavior is a “behavior” of a complex system that is neither programmed nor observable at the node scale.
In other words, you can’t see or predict the emergent behavior by looking at the nodes and their connections. It occurs at a different scale and comes from the interactions of the nodes.
Because emergent behaviors are not observable or predictable at the system level, they are often surprising. Can you think of a surprising event that occurred recently? How about the election of Scott Brown in Massachusetts?
While your mind wraps itself around all the repercussions of emergent behavior, let me go back to something you might have missed. Did you notice that in order to affect a complex system, you don’t need to program in a complex outcome? You only need to change the simple rules that apply to each node.
Here is a great example. Glenn Beck does this by teaching his viewers history. Here’s the rule: Teach node history. It is simple and it applies to all nodes equally. What is the emergent behavior? In April 2009, 3,000 people showed up at the Tea Party in Washington, DC. On September 12, 2009, just five months later, over 1,000,000 people showed up.
That’s enough for now. There is so much more.