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.

Complexity and AI

As these two recent articles—“AI Reaches the Golden Years” and “Brainy Robots Start Stepping Into Daily Life”—suggest, there is currently quite a bit of interest in the development of artificial intelligence. How to implement true machine intelligence, though, is still an open question.

The Wired article points out a problem AI has had since its inception: how to deal with ‘common sense.’ Even though computers like Deep Blue rock at chess, because of the complexity involved in modeling this kind of fuzzy knowledge, programming machines to do what are considered to be normal, everyday tasks is extremely daunting.

Deep Blue: “Bring it on”

In his 1994 book Complexification: Explaining a Paradoxical World Through a Science of Surprise, John L. Casti points out this difficulty with what he calls “top-down” AI models; that is, models that attempt to program in all the environmental factors that will affect the AI. This is the method that has a hard time with mundane tasks. Another, more profitable, method appears to be modeling systems from the “bottom-up,” that is, mimicking the process of the brain and allowing complex behavior to originate from those interactions.

This method, too, has its difficulties. Casti notes, and plieb has pointed out, that there is good reason to believe that to actually produce brain-like activity, a device “must also share the size, connective structure and initial configuration of the brain” (160). Though such a thing may be possible, Gödel has suggested that if it were to evolve, it would be too complex for us to understand, much as our own brains’ functions remain a mystery to us (Casti 167).



Gödel’s solution is supported by attempts at creating A-life via cellular automata (CA) like Conway’s Game of Life (an example of a “glider gun” CA from the Game of Life is pictured above_. Following Steen Rasmussen’s rules for what A-life must look like:

Postulate 1: A universal Turing machine can simulate any physical process.

Postulate 2: Life is a physical process.

Postulate 3: There are criteria by which we can distinguish between living and nonliving systems.

Postulate 4: An artificial organism must perceive a reality R*, which for it is just as real as the “real” reality R is for us.

Postulate 5: The realities R* and R have the same ontological status.

Postulate 6: We can learn about the fundamental properties of our reality R by studying the details of different R*s. (Casti 168-69)

A Game of Life board that could execute such a CA would be roughly 3 square kilometers in size (Casti 228).

These results to not mean that artificial life is out of the question. Casti suggests that the correct response to Gödel’s statement would not be to build AI, but to “grow” it using a bottom-up approach. This would allow for simple processes to form an aggregate—the final form of which, as noted above, would be too complex for us to completely understand—that would exhibit life-like behavior. This system would exhibit the properties noted by Stuart Kauffman in cellular interactions; that the “unimaginably complicated network of interactions” occurring in the cell don’t “lead to utter chaos, but rather results in the cell organizing itself into stable patterns of activity appropriate for its particular function in the organism” (Casti 267). Taking advantage of this spontaneous order is what the NYT article calls “cognitive computing” and falls under the heading of complexity. If such life is ever achieved, perhaps it will be Gödel’s standard—that we can’t understand it—that will validate it as being “alive,” rather than any other arbitrary list of behaviors.