How to know the network?

"Network science" has been pitched as an alternative to "reductionist" science. That is, after dissecting processes into ever smaller units, and trying to understand the process by understanding the units, "knowing" the process still eluded scientists.

Now we are close to knowing just about everything there is to know about the pieces. [an absurd claim, no? - jd] But we are as far as we have ever been from understanding nature as a whole. Indeed the reassembly turned out to be much harder than scientists anticipated. [!] The reason is simple. Riding reductionism, we run into the hard wall of complexity. (Barabasi, Linked, p6)

"Network science" then comes to the rescue, revealing the laws of self-organization: per Barabasi, complexity has a "strict architecture", and its name is network.

One might argue though that this is just a deeper reductionism. Instead of just the parts -- now called "nodes", we add another abstract fundamental, the "link" to represent the richness of interconnection (reducing that to a bundle of discrete threads, each of which can be examined and presumably understood). Simple mechanics are enhanced to include another order of properties. Concerning ecology, a discipline that is heavy on interconnection, William Brinton writes "ecological perspectives within the sciences often only strengthen reductionistic directions, since they provide important details about relationships, which in turn help 'fine tune' the existing mechanical models." ("Environment as Data versus 'Being': Is Goetheanism possible in the West?", http://www.ifgene.org/brinton.htm)

In a harsh critique of "complexity", Steve Talbott argues that the more abstract theories and observations become, the more they become about nothing. "The problem with a scientific method based on maximum generalization and abstraction is that the more it succeeds -- that is, the more general and abstract its results become -- the shallower they tend to be. They tell us less and less about the particular contexts we wish to understand... In our drive toward generality and abstraction, we end up with what we ask for... We will get a theory that 'connects' diverse things, but in the process loses the things we are connecting." ("The Lure of Complexity", http://www.natureinstitute.org/pub/ic/ic7/complexity.htm)

How much simpler can the abstraction "network" become? The network diagram is to the actual process or phenomena as a stick figure is to a painting, or really, to the phenomena represented in the painting.

In general, modeling and abstraction are useful tools and part of the dialectical process of coming to understand phenomena, assuming that the researcher makes the return trip to the phenomena. Goethe's assertion that "the phenomenon is the theory" is a powerful insight. The abstraction is not the phenomenon.

A phenomenon cannot be understood as a "network". The "network" reduction is an abstraction. Certainly (I think) the "network" reduction, as part of a bigger project of isolation, focus, and then re-assembly, re-contexting and re-imagination, can lead to a deeper understanding of the phenomenon. Ultimately, though, "knowing the network" is a process of imaginative participation in the phenomenon. In part this is because the "complexity" of the interactions can only be grasped imaginatively, and in part because the network is a process in time, developing, changing, growing or dying or both, and likewise only graspable in the imagination. "Knowing the network" is a challenge that requires a holistic approach, a holistic scientific method.

Okay, so I am struggling through the ideas above. See The Nature Institute's website for more on what has been called a phenomenological approach to Nature, or holistic science, qualitative science, Goethean science, etc.

- jd