So i’m reading a paper today by Chris Langton on the subject of Artificial Life (available online at http://www.aec.at/en/archiv_files/19931/E1993_025.pdf), in which he points out the fundamental differences between approaches to studying linear and non-linear systems. I would say that in researching Interaction Culture, we must more fully understand ourselves to be dealing with non-linear systems, and adjust our epistemology accordingly. I’ll leave it up to Chris to elaborate (from the paper, pgs 12-13):
“..the distinction between linear and nonlinear systems is fundamental, and provides excellent insight into why the principles underlying the dynamics of life should be so hard to discover. The simplest way to state the distinction is to say that linear systems are those for which the behavior of the whole is just the sum of the behavior of its parts, while for nonlinear systems, the behavior of the whole is more than the sum of its parts. Linear systems are those which obey the principle of superposition. We can break up complicated linear systems into simpler constituent parts, and analyze these parts independently. Once we have reached an understanding of the parts in isolation, we can achieve a full understanding of the whole system by composing our understandings of the isolated parts. This is the key feature of linear systems: by studying the parts in isolation, we can learn everything we need to know about the complete system.
This is not possible for nonlinear systems, which do not obey the principle of superposition . Even if we can break such systems up into simpler constituent parts, and even if we can reach a complete understanding of the parts in isolation, we would not be able to compose our understandings of the individual parts into an understanding of the whole system. The key feature of nonlinear systems is that their primary behaviors of interest are properties of the interactions between parts, rather than being properties of the parts themselves, and these interaction-based properties necessarily disappear when the parts are studied independently.
The process that we call “life” is a fundamentally nonlinear process, emerging out of interactions between non-living parts. Life is a property of form, not matter, a result of the organization of matter rather than something that inheres in the matter itself. Neither nucleotides nor amino acids nor any other carbon-chain molecule is alive — yet put them together in the right way, and the dynamic behavior that emerges out of their interactions is what we call life. It is effects, not things, upon which life is based — life is a kind of behavior, not a kind of stuff — and as such, it is constituted of simpler behaviors, not simpler stuff.
Thus, analysis is most fruitfully applied to linear systems. Such systems can be taken apart in meaningful ways, the resulting pieces solved, and the solutions obtained from solving the pieces can be put back together in such a way that one has a solution for the whole system.
Analysis has not proved anywhere near as effective when applied to nonlinear systems: the nonlinear system must be treated as a whole.
A different approach to the study of nonlinear systems involves the inverse of analysis: synthesis. Rather than start with the behavior of interest and attempting to analyze it into its constituent parts, we start with constituent parts and put them together in the attempt to synthesize the behavior of interest. Analysis is most appropriate for the understanding of linear systems, synthesis is the most appropriate for the understanding of nonlinear systems.” (pages 12-13)
I will illustrate this idea here with an example. Seeking to understand MySpace is similar to understanding the genesis of life in an important sense. Studying one user or one page on MySpace (the approach appropriate if it were a linear system) could not have predicted that the site would gain 100 million users any more than studying one amino acid could have predicted that i would exist as a quasi-intelligent life form who writes posts for a professor with strongly idealistic hockey affinities. But once i realize that MySpace or quasi-intelligent life has a non-linear relationship with the behaviors of their requisite parts (millions of users and millions of profiles are what create the macro-level behavior of MySpace), i have made the first step toward being able to understand MySpace and, if i’m very clever in my “synthetic” approach, i might be able to guess at how i might create a competing social network for sub-intelligent life.
Another point made elsewhere in the paper is that the only way to understand non-linear systems (since their underlying mechanics can’t be predicted by looking at the final system) is to create models of the systems and see how they behave. I believe that this concept also supports a design methodology that puts at least prototypes, if not artifacts, out into the real world iteratively and often, since the effects, the meanings and the uses of artifacts in an Interaction Culture are probably too complex to predict ahead of time.