Decentralized Systems

Update: This post is a partial review of John Holland’s Hidden Order: How Adaptation Builds Complexity and Mitchel Resnick’s Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds.

Both John Holland (Hidden Order, 1995) and Mitchel Resnick (Turtles, Termites, and Traffic Jams, 1994) argue that it is difficult to discern the behavior of a system from the behavior of its parts. Through the use of computer modeling—cellular automata in Holland’s case, the StarLogo programming environment in Resnick’s—both attempt to begin to understand the nature of these complex systems.

Holland defines complex systems as a product of “the interactions” between their relatively simple parts (3). The result of these interactions, which are often relatively simple themselves, is that the “aggregate behavior of a diverse array of agents”, or the “parts” of the system, “is much more than the sum of the individual actions” of those parts (31). That is, the ordered behavior comes as a result of the particular way in which objects interact, rather than from any kind of centralized oversight, which is presumed to be the source of most ordered behavior. Holland gives the example of a city as a complex system that “retain[s]” its “coherence despite continual disruptions and a lack of central planning” (1). Now most cities obviously have central planning architectures in the form of governments, but those centralized authorities often find it their job to combat or enforce city behavior that does not originate directly (at least in appearance) from their decisions. Where do city-level features like traffic jams, ethnically- or economically-segregated neighborhoods, and homelessness come from? Rarely can they be directly attributed to central planning. Rather, the interactions between residents—which are often dictated by central planning organizations—and other structures in the environment help to form and maintain the city and its “personality.” This aggregate behavior results in “an emergent identity” that, though continuously changing, is remarkably stable (3).

Similarly, Resnick explicitly focuses on these “decentralized interactions” and the systems that result from them (13). He provides five “Guiding Heuristics for Decentralized Thinking”: 1) “Positive Feedback Isn’t Always Negative”, that is some kinds of positive feedback, in the economy for instance, can lead to increases, rather than decreases, in order; 2) “Randomness Can Help Create Order”; 3) “A Flock Isn’t a Big Bird”—systems do not behave like a larger version of their components; 4) “A Traffic Jam Isn’t Just a Collection of Cars”, or decentralized systems are more than the sum of their parts; and finally 5) “The Hills Are Alive”—the environment and context of a decentralized system are key components of its behavior (134).

Resnick sees the decentered view of the world as necessary to changing deeply-entrenched centralized ways of thinking. According to him, this decentered view became apparent in the work of Freud and his description of the unconscious, and other decentered metaphors have been slowly gaining ground in other fields since then.

This view of the world as being primarily the product of decentered behavior has interesting applications for rhetoric, for persuasive situations are as decentered and interaction-dependent as the systems studied by Resnick and Holland. Resnick notes that as decentered thinking—in the form of chaos and complexity theory—has gained traction, “scientists have shifted metaphors, viewing things less as clocklike mechanisms and more as complex ecosystems” (13). Rhetoricians since the sophists, however, have known the complex, dependent nature of communication, where “decentralized interactions” like the complex interactions of rhetorical appeals and “feedback loops” of self- and community-reinforcement (13) are well known.

These connections imply that rhetoric is well-suited for application of decentralized thinking. Certainly there is a tendency even in rhetoric to over-emphasize centralized behaviors to the detriment of decentralized ones—see the work of Peter Ramus. As rhetoric continues to move closer to a sophistic understanding of the power of persuasion, the models of Resnick and Holland have the possibility of shedding light on rhetorical situations, providing a language to explain behaviors that, though recognized, might have been previously unexplainable.

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Update: This post is a partial review of Stuart Kauffman’s At Home in the Universe: The Search for Laws of Self-Organization and Complexity

In At Home in the Universe: The Search for Laws of Self-Organization and Complexity (1995) Stuart Kauffman argues that traditional notions of how order arises are at best incomplete. Traditionally, it is assumed that Darwinian forces—“Random variation, selection-sifting”—were responsible for all the order we see in the universe (8). However, Kauffman demonstrates that random variation alone isn’t enough to explain the origin of order. By themselves, variation and selection are susceptible to two problems which counteract their organizing properties: if it proceeds towards an evolutionary dead end it can become “trapped” there and, even when it doesn’t run into this problem it is prone to “error catastrophes” (184). Kauffman derives this information from statistical models called fitness landscapes that map all the possible ways a particular environment can evolve. When the landscape is too rough the environment becomes “trapped or frozen into local regions” preventing further development. This problem is not solved by finding solutions with less peaks and troughs, for “on smooth landscapes” selection “suffers the error catastrophe and melts off peaks,” a process which would leave the genotype “less fit” (184-85). When an error catastrophe occurs, “the useful genetic information built up in the population is lost as the population diffuses away from the peak” (184); that is, whatever fitness the population may have demonstrated would be lost because the mechanism of selection is not capable of surveying a fitness landscape to find the best possible niches for the organism. This leads to Kauffman’s realization that “there appears to be a limit on the complexity of a genome that can be assembled by mutation and natural selection”, and, in turn, that there is not just a “singular source” of order in the universe—natural selection—but rather there must be another source as well, one that limits selection into useful areas of fitness (185, 71).

Kauffman calls this other source of order “self-organization”, and he argues that because of it, “vast veins of spontaneous order lie at hand” (8). Self-organization is a product of “extremely complex webs of interacting elements [that] are sparsely coupled” (84). When enough of these interacting elements are brought together and their ability to communicate is limited—Kauffman has shown that one optimum number is two connections each—they can organize themselves into regular patterns. These patterns are analogous to the attractors in complex mathematical systems, and systems that exhibit the behavior of strange attractors provide the complex qualities that Kauffman describes.

Additionally, Kauffman notes that these attractors often occur on the border between stable and chaotic behavior or “poised between order and chaos” (26). It is during the phase transition between the stable and the unstable that systems display organized behavior, what Kauffman calls “order for free” (106). This order is what makes natural selection possible, for it limits the action of complex systems—which often display more states than could be cycled through in the life of the universe—from all possible states to a few attractors, making ordered behavior not improbable but expected.

Notes on chaos

This post is a pretty random group of reactions to the ideas of chaos theory presented in James Gleick’s Chaos: Making a New Science (1987).

According to previous understandings of nature, “Simple systems behave in simple ways. . . . Complex behavior implies complex causes. . . . Different systems behave differently” (303). However, chaos theory argues that “Simple systems give rise to complex behavior. Complex systems give rise to simple behavior. And most important, the laws of complexity hold universally, caring not at all for the details of a system’s constituent atoms” (304).

This theory suggests that commonly held assumptions about the behavior of the world are flawed. Natural systems do not exhibit simple, linear behavior. They do, however, exhibit patterns, but these patterns are often fractal, that is, they exhibit constant change and transformation at all scales and cannot be boiled down to simple geometric shapes. An example would be the contrast between a triangle, which only gives information at one scale, and a fractal image, which exhibits more information no matter what scale you look at.

“Libchaber believed that biological systems used their nonlinearity as a defense against noise. The transfer of energy by proteins, the wave motion of the heart's electricity, the nervous system—all these kept their versatility in a noisy world.” (194).

This is an interesting observation, since most communication deals at some level with the problem of overcoming noise, that is, barriers that prevent a clear understanding of the message. This phenomenon presents itself in nature as well as in language. Proposing non-linearity, the ability of seemingly chaotic systems to generate order, as a means of overcoming noise deserves more attention in studies of communication.

“the spontaneous emergence of self-organization ought to be part of physics” (252).

And everything else, I would say. The question of order pops up in a lot of scientific literature; attempting to find the answer to it is one of the things that attracts me to chaos and complexity theory. Most analytic effort is spent trying to explain the nature of order, but only recently has the origin of order taken the forefront in scientific questioning.

Metaphor and reality

Update: This post is a partial review of Kenneth Boulding’s Ecodynamics and James Gleick’s Chaos: Making a New Science

Lately I’ve been thinking a lot about the metaphors that Kenneth Boulding uses to describe the natural world in his Ecodynamics (1978). One such metaphor is evident in his statement that knowledge, or know-how, is embedded in the structure of natural objects. The way in which Boulding expresses this idea is that “in a certain sense, helium ‘knows how’ to have two electrons and hydrogen knows only how to have one” (14). This is a case where ‘structure’ has the ‘ability to “instruct”’ (13). One benefit of this particular way of looking at knowledge is that it limits what is determined about a subject to what can be known. A fact of an atom of helium is that it is an atom of helium, and that fact can be stated in terms of know-how. (Boulding uses this method to show how unhelpful the idea of the survival of the fittest is, for it really is just a statement about the survival of the survivors.) This metaphor is particularly powerful because it allows for our understanding of communication to explain natural phenomena like the replication of DNA. Know-how is communicated from the existing structure through other materials that lend themselves to communicating that structure as well. By accepting this metaphor, statements about language, the realization that “communication . . . becomes a process of complex mutuality and feedback among numbers of individuals that leads to the development of organizations, institutions, and other social structures which affect” the outside world (16). The spread of know-how through communication—the “multiplication of information structures” (101)—leads to complex behavior and organization, in persons as well as in nature.

This phenomenon, communication through the propagation of order and know-how, can be seen in other natural structures. In Chaos: Making a New Science (1987), James Gleick identifies several of these phenomena like entrainment or modelocking, an example of which being when several pendulums, connected by a medium like a wooden stand that can communicate relevant information like rhythms, all swing at the same rate (293). Similarly, in the phenomena of turbulence, “each particle does not move independently”; in their interdependent interaction, the motion of each “depends very much on the motion of its neighbors” (124). I don’t think that it is much of a stretch to say that know-how is propagated through the constraints of strange attractors (the image to the left is of the Lorenz attractor) and similar phenomena. Chaotic phenomena behaves in a particular way because that is what it knows how to do.

Supposing we can accept this metaphor for behavior in nature, that it is a kind of communication where what is being communicated is knowledge, then it seems like it would be completely reasonable to use the language of rhetoric to describe natural behavior. The sensitive interdependence of the parts of a system, recognized 1) by Boulding in animal development where “the history of a cell in an embryo depends on its position relative to others rather than its past history, because its position determines the messages”—or information—“that it gets” (107), and 2) by the physicist Doyne Farmer, who, in describing mathematical equations notes “the evolution of [a variable] must be influenced by whatever other variables it’s interacting with,” for “their values must somehow be contained in the history of that” variable (266) suggests a rhetorical way of looking at nature. As Boulding acknowledges, everything depends on everything else, a point that rhetoricians have been making about the persuasive situation since the discipline was formed. This connection opens up the exciting possibility of rhetorical analysis of natural systems, where the tools of monitoring persuasion in language could be used to track the movement of know-how through nature.