Our philosophical framing of value across connected elements creates a universal, abstracted perception of a network.
A network is the combination of various elements or values to create a collective, larger value. Every larger thing is a networked collection of smaller things. Each one of those elements are a networked collection in themselves. This persists until we start diving into the periphery of our understanding. On the other end, each network is one thing as part of a larger network.
However, we don’t immediately recognize this network belief. Our standard dialogue tends to only address networks as explicit concepts or for problem diagnosis.
The network’s structure
Networks consist of several base components:
- Nodes, which always represent as nouns.
- Connections, which can represent as absolutely anything connected with those nouns.
Connections are relationships with everything else, but the node’s relative use defines its connection with other nodes. If a node has the exact same qualities as its surrounding nodes, we won’t readily distinguish between nodes and connections.
Graphs and mental models may imply it, but each network node is not a static existence. If we inspect a node more closely, we’ll find another smaller network within it. For example, an organization within society is a network of people, composed of individuals. An individual has moods, viewpoints, decisions, and many other elements. This pattern repeats itself downward indefinitely, but also migrates upward, with each network as a node of a larger network.
Networks, however, are merely representations of understanding.
- Since they’re grounded in reality, they’re never fully exclusive if they overlap on even one node.
- They incorporate imagination, so they’re never completely overlapping (since everyone sees the world a little differently).
The differences come through the interpreted purpose of the nodes, so they will both present differently. An example would be how a body’s circulatory system and respiratory system use similar nodes, but are very different.
Reality, though, doesn’t understand purposes at all, so the network is always more vast than we can understand.
Changing networks
Beyond our perceived world, we develop creative ideas by filling in and changing networks. We do it through a few major pathways:
- Re-observing the patently obvious (e.g., how two different objects are relatively alike).
- Discovering newly observed nodes (e.g., detecting a new way to see things).
- Severing presumed connections (e.g., scientific discoveries that discover a lack of correlation).
- Constructing new networks toward new purposes with existing nodes (e.g., using a common technology for an uncommon use).
We feel these networks through intuition more than we can articulately understand or communicate them. This creates limits:
- We can specialize into using them for our purposes. It comes at the cost that we won’t know how to communicate the scope of the network.
- Alternately, we can learn to teach them, which means we won’t be as useful working with that network.
- If there are any security concerns, we will try to sabotage purposes from bad actors.
Network issues
Too many or too few connections can harm our ability to perceive:
- Not understanding the connections among elements is how people become technical idiots.
- We feel anxiety when we fail to reliably remove connections enough that the imagined possibilities don’t scare us.
Further, most interpersonal conflicts arise through failing to recognize the presence of networks and how much they’re subject to change:
- Each person’s understanding is a separate network.
- Every single existing object contains more networks within it than we are presently aware of.
- Every single object is part of more networks than we are presently aware of.
- Networks are constantly adapting without our awareness.
- The domain of image distortion is either making stories that remove key nodes, or simply fabricating nodes that don’t exist.
Finally, this network-based value system is precisely why skillful people are very good at fixing problems.