Theoretical Biology

I wanted to post on J. Von Uexkull's -Theoretical Biology- (1926) as it is important work for Heidegger, Deleuze, Agamben, and others. Agamben's chapter on "The Tick" in -The Open- owes a great deal to Uexkull's work.

Here's a brief passage that approaches the sense of a butterfly wing flapping in Galveston, TX giving rise to events on St. Vincent Street in Montmarte.

"In principle, the step of a beetle's foot or the stroke of a dragonfly's wing must carry their effect as far as the dog-star. For, according to the causal conception, even the smallest component of natural phenomena is absolutely necessary, and cannot be thought away from the general system of action and reaction, wihtout making the whole impossible" (159).

Of course, all of these little wings are part of networks of emergence.

Another interesting passage is one that I believe either Heid or Husserl cite:

"It has been found that if, while a bee is feeding, its abdomen be carefully cut off, the insect will go on drinking with the honey flowing out of it again behind. In this case the action does not cease; the bee goes on drinking like Baron Munchhausen's horse" (169).

Uexkull talks about this in reference to "subjective annihiliation" which he links to the female praying mantis devouring the male after sex.

... It's also worth noting Uexkull's many bloc/ks that span from Giordano Bruno --who "rent open the roof of the heavens, and in its place put space, infinite and meaningless"-- to Kant.

Von Uexkull also speaks by way of "assemblages" and "community-machines" that resemble some of the language of Delueze.

Anyway, I want to suggest that along w/ Louis Agassiz and G. St. Hilaire, Uexkull is important historical figure for thoughts on emergence and complexity.

Strands of System

I'm currently reading Douglas R Anderson's -Strands of System: The Philosophy of C.S. Peirce- published by Purdue University Press (Go Boilers!:)

I'm interested in Peirce's sense of abductive or retroductive reasoning, particularly as Ulmer discusses it in relation to Conduction in Heuretics.

Here is the form of abduction:

The surprising fact, C, is observed;
But if A were true, C would be a matter of course,
Hence, there is reason to suspect A is true.

... the model for fidning possible answers has as illustration the work of S. Holmes, who is able to use the clues (topoi) of a given situation to construct a plausible narrative such that he can test it out deductively and inductively (empirically). Ulmer uses this notion to move on to conduction where he thinking of clues from particular to particular, often through their aesthetic images.

But, where Peirce is useful is that it allows for a provisional testing of ideas.

I might add that CSP is a forefunner to Pragmatism.

Shaviro & Asimov

I have been reading Steven Shaviro's Connected, or What it Means to Live in the Network Society. I would recommend it; in a series of short sections Shaviro makes connections between critical, complexity, and network theory and science fiction, popular culture, and current events (such as the battles regarding Napster and the rise and subversion of contemporary surveillance culture. Shaviro considers the book itself to be science fiction- a look at what the networked world could become. He concludes: "science fiction is about the shadow that the future casts upon the present. It shows us how profoundly we are haunted by the ghosts of what has not yet happened" (250). Which reminds me how difficult it is to include time in an understanding of the network. It is one thing to say that the network is never static--it is another thing to think through the implications of that statement. Its a really deep rabbit hole.

My emphasis while reading this summer has been on contextualization (not only in terms of composition)and Shaviro nicely contributes to the subject. Discussing Richard Dawkins's concept of the meme (the cultural equivalent of the gene--for instance... da-da, dud-dut-da- I'm lov...see you can finish it yourself), he writes:

If we take seriously the idea that memes, like genes, are always engaged in a Darwinian struggle to survive and reproduce, then we cannot assimilate them to the fashionable view that the world is nothing but patterns of information.... (16)

He grounds this position in a complex conceit between information and parasites (I quote liberally since I don't think either of you are planning on reading Shaviro):

A pattern of information is meaningless by itself. A virus remains inert unless it encounters a suitable living cell. A configuration of ones and zeroes is similarly no more than gibberish until it is processed by the right program. Genes and memes are helpless without their hosts. They need to be instantiated in flesh, or at least in matter. They can only replicate themselves by means of the effects they have on bodies. But these effects are multiple, contradictory, widespread, and often indirect. We cannot think of information as just a pattern imprinted indifferently in one or another physical medium. For information is also an event. It isn't just the content of a given message but all the things that happen when the message gets transmitted. As Morse Peckman puts it, "the meaning of a verbal event is any response to that event." In other words, meaning is not intrinsic, but always contingent and performative (final emphasis mine)(17)

This circles round what I have been attempting to articulate all summer: composition potentially presents itself as a way to help students come to terms with the networked nature of contemporary existence by emphasizing the need to read every sign in terms of its context. And our technologically driven culture further emphasizes the importance of this orientation away from "static" [modernist] epistemologies (ways of knowing that want to freeze, solidify, "permanize") to dynamic [postmodernist? complex? network? digital?] epistemologies (ways of knowing that emphasize fluidity, temporality, and motion).

Santos Out.

M. Mitchell Waldrop

I don't think either of you are delving into this one, so I'll try to give you a brief (ha!) synopsis (I'm a little over halfway through--this one moves pretty slowly).

Waldrop's Complexity: The Emerging Science at the Edge of Order and Chaos tracks the genesis and evolution of the Santa Fe Institute from its inception in the early seventies. The Institute was founded by a number of scientists (all with piles of Nobel prizes) interested in pursuing a truly interdisciplinary investigation on nonlinearity and complex adaptive systems.

Much of the book focuses on one of the institute's first major conferences. Funded by Citibank corporation, the conference brought together progressive physicists and economists try and come up with a more reliable system for predicting economic fluxuations. John Reed, CEO of Citibank, was frustrated with traditional economic models that didn't live up to the dynamic conditions of real world economies. Waldrop explains: "the existing neoclassical theory and the computer models based on it simply did not give him the kind of information he needed to make real-time decisions in the face of risk and uncertainty" (93).

Neoclassic economics relies on the concept of "perfect rationality" in order to predict the actions of an economic agent (be it a consumer, corporation, or country). Waldrop describes perfect rationality thusly:

Perfectly rational agents do have the virtue of being perfectly predictable. That is, they know everything that can be known about the choices they will face infinitely far into the future, and they use flawless reasoning to forsee all the possible implications of their actions. So you can safely say that they will always take the most advantageous action in any given situation, based on the available information. Of course, they may sometimes be caught short by oil shocks, technological revolutions, political decisions about interest rates, and other noneconomic surprises. But they are so smart and so fast in their adjustments that they will always keep the economy in a kind of rolling equilibrium, with supply precisely equal to demand. (142)

This generalization guiding the behavior of all economic agents becomes the postulate upon which all economic theory is grounded. It is the "c" required for all the math to work. If this sounds ridiculous to anyone, then, good, it should. It sounded especially ridiculous to a group of physists that have to test every theory with empirical evidence. To the scientists, "too much theory and you could end up gazing into your navel" (87).

Unlike traditional Newtonian or Einsteinian influenced physicists, many of the people associated with the Santa Fe institute were more concerned with pattern than with particle: instead of following the scientific tradition that focused on the composition and behavior of a single agent (be it atom, electron, or neutrino), these scientists concerned themselves with the collective behavior of agents. They were interested in noticing the dynamic relationships in particles that often yielded results unequal to the sum of individual parts. More than anything else, however, the scientists of the Santa Fe institute came to terms with being "messy." One of the institute's founders, George A. Cowan, explains:

"Almost by definition," he says, "the physical sciences are fields characterized by conceptual elegance and analytical simplicity. So you make a virtue of that and avoid the other stuff." Indeed, physicists are notorious for curling their lips at "soft" sciences like sociology or psychology, which try to grapple with real-world complexity. But then here came molecular biology, which described incredibly complicated living systems that were nonetheless governed by deep principles. “Once you’re in a partnership with biology,” says Cowan, “you give up that elegance, you give up that simplicity. You’re messy. And from there it’s so much easier to start diffusing into economics and social issues. Once you are partially immersed, you might as well start swimming.” (62-63)

What becomes key here is something that comes up in many of our (and I mean this locally and disciplinarily) discussions: simplicity vs. complexity. The (almost utopian, right Kristen?) allure of simplicity and the extent to which people are willing to go to secure it. Waldrop’s book celebrates (again and again and again) scientists who were able to take the plunge and dive in.

Two economists especially agreed with them: Brian Arthur and John H. Holland. As someone looking to make connections between this material and composition, I found Holland’s ideas on complex adaptive systems particularly cogent:

1. …regardless of how you define them, each agent finds itself in an environment produced by its interactions with the other agents in the system. It is constantly acting and reacting to what the other agents are doing. And because of that, essentially nothing in its environment is fixed.
2. …the control of a complex adaptive system tends to be highly dispersed… If there is to be any coherent behavior in the system, it has to arise from competition and cooperation among the agents themselves.
3. …complex adaptive systems are constantly revising and rearranging their building blocks as they gain experience.
4. …all complex adaptive systems anticipate the future… this business of anticipation and prediction goes far beyond issues of human foresight, or even consciousness…. Every complex adaptive system is constantly making predictions based on its various internal models of the world—its implicit or explicit assumptions about the way things are out there. Furthermore, these models are much more than passive blueprints. They are active…like any other building blocks, they can be tested, refined, and rearranged as the system gains experience. (145-146)

I know I should work out point by point how the above material relates to composition, but I am starting to burn out and will save that for a later day. Briefly, I will say that many of the process-based/textbook approaches to writing rely on a type of “perfect rationality” imbedded in economic theory. Writing is made to look clean and simple. Those of us in the field know that this is ridiculous. Also, I am recognizing how important contextualization is to my pedagogical approach to writing. Teaching students to recognize the social, cultural and political webs around them. Teaching them that everything is always (al----y) in flux. That its o.k. that everything is in flux. Framing writing as a series of choices based on awareness of the situation and their audience.

There’s a final point made by Holland, one that I believe is a bit more complex and thus I will quote it at length. In an educational discussion it seems a bit depressing in the sense of Bourdieu: it rings of the kind of cultural determinism that made me listen to Rage Against the Machine in the days of my youth. Now perhaps I have read too much Derrida to think I can ever change the system… or does the chaos theory discussed by Taylor and Waldrop (an initially small force can have dynamic, unpredictable, and incredibly influential impact upon a system) encourage me? Perhaps the point is to orient as many agents as possible? Hmph, I have been writing too long to work that one out. Here’s the paragraph:

Finally, said Holland, complex adaptive systems typically have many niches, each one of which can be exploited by an agent adapted to fill that niche. Thus, the economic world has a place for computer programmers, plumbers, steel mills, and pet stores, just as the rain forest has a place for tree sloths and butterflies. Moreover, the very act of filling one niche opens up more niches—for new parasites, for new predators and prey, for new symbiotic partners. So new opportunities are always being created by the system. And that, in turn, means that it’s essentially meaningless to talk about a complex adaptive system being in equilibrium: the system can never get there. It is always unfolding, always in transition. In fact, if the system ever does reach equilibrium, it isn’t just stable. It’s dead. And by the same token, said Holland, there’s no point in imagining that the agents in the system can ever “optimize” their fitness, or their utility, or whatever. The space of possibilities is too vast; they have no practical way of finding the optimum. The most they can ever do is to change and improve themselves relative to what the other agents are doing. In short, complex adaptive systems are characterized by perpetual novelty. (147)