For understanding the legislators’ difficulties in regulating the behaviors in CANSs (like the behaviors in the global, multi-level and multi-niche cluster of networked personal-data devouring and producing individuals and institutions), we need a systemic perspective that relates responsible individuals with the communities they co-evolve with.
An important concept therefore is `system’ in general (as complex adaptive systems form a species). Of course there are many definitions. We opt for a combination of the insights of Meadows 2008 and Holland 2012 which leads to this system concept:
A set of things – people, cells, molecules, or whatever – interconnected in such a way that they produce their own pattern of behavior over time. Systems have boundaries that concurrently separate them from their environment and are selectively permeable for communication. The system may be buffeted, constricted, triggered, or driven by outside forces. But the system’s response to these forces is characteristic of itself.
We will use the term community to refer to a special type of systems, whose units are predominantly formed by responsible individuals that share a common goal or interest. Of course, communities may interact and or overlap, which complicates things.
We show again an overview of important characteristics often mentioned as building blocks for the CANS concept: (i) diverse agents with (ii) behavioral parameters; (iii) dynamics; (iv) emergence; (v) replication; (vi) metabolism; (vii) survival; (viii) selection; (ix) recursion, or scale free phenomena; (x) bottom-up and (xi) top-down causation; (xii) network structures, multi-level feedback loops; (xiii) self-organization; (xiv) critical transitions. These fourteen characteristics form and make the CANS concept into a family concept, and make real-life complex adaptive networked systems ready for complexity theory.
A Simulated Example CANS
We give a live example of a very simple Complex Adaptive Networked System. A toy CANS, actually: MIT’s phantom traffic jam simulation model (Flynn 2009). Its behavior is filmed and the resulting comic is made available on internet (visited on December 27, 2014)). We show a still in Figure 1 for visualizing a few of the CANS characteristics mentioned.
Figure 1: A Toy CANS: MIT’s Phantom Traffic Jam
In the Figure we see a part of a desert with a circular road that is populated by cars, driven by drivers that are all driving in the same direction. A driver will brake to avoid crashing into the car before and a driver will accelerate to a certain max speed limit when crashing risks are low. Jams will predictably emerge when the braking power of cars exceeds their acceleration power and the density of cars on the road exceeds a certain treshold.
A jam is actually forming in the front stretch of Figure 1, where the cars are clustering together. Such jams are called phantom traffic jams. “Phantom,” as they are not caused by any single and clearly observable event.
In such a simulation we can revisit the CANS characteristics mentioned. As this may be useful, we will do so in another Paragraph.