Fists and irreducible complexity

Update: This post is a partial review of Malcolm Gladwell’s Blink: The Power of Thinking Without Thinking.

A key question in rhetoric and communication studies is how people are persuaded to act. Sometimes the act in question is overt in that it is the completion of some action; other times, the action could be implicit, in that it is the acceptance of some idea or line of reasoning as being true (or false). This latter group of actions are variously referred to as decisions or making up one’s mind. (I don’t consider these categories to be all that rigid. Consider them convenient shorthand for some temporary ideas—an argumentative place to hang your hat.) In Blink: The Power of Thinking Without Thinking (2005), Malcolm Gladwell argues that many of our decisions are made without our conscious input, that they are the result of unconscious processes that occur independently of our considered, conscious thought.

Many of Galdwell’s proofs for this idea come from the ideas of complexity theory and have interesting applications to rhetoric. In the first case, Gladwell references Paul Ekman and W. V. Friesen’s Facial Action Coding System (FACS) (here is a link to a brief explanation of FACS, and a FACS chart is pictured below). Ekman and Friesen documented over 10,000 configurations of the facial muscles, of which they found about 3,000 that were meaningful. This result comes from the layering of actions in the facial muscles, actions which grow exponentially as more muscles are worked together (or in sequence). The result seems very much like a strange-attractor type problem, where out of many countless meaningless results—what Gladwell calls ‘the kind of nonsense faces that children make’ (201)—a few stable meaningful results arise.


In Gladwell’s discussion of ‘thin-slicing’, his name for the ability to find emergent (my word, not his) properties of a system in very small samples of that system—say, ten seconds of a couple’s conversation is sufficient to make a highly-probably determination of that couple’s future, or a similarly small sample of Morse code is enough for a trained listener to be able to identify the operator transmitting that code. According to Galdwell, this code pattern from the second example, called a fist, ‘reveals itself in even the smallest sample of Morse code’ and ‘doesn’t change or disappear for stretches or show up only in certain words or phrases’ (29). This fact would seem to indicate that the fist is not irreducibly complex, that is, that the message is not the shortest possible way of describing the fist, for the fist shows up even in very small samples of the message.

In complexity theory, the irreducibly complex is equivalent to the random. Take the example of a random string of numbers. This string is the prototype of an irreducibly complex message because it cannot be expressed in a reduced form. The shortest method of reproducing a string of random numbers is the string itself. Language theorists like Jacques Derrida seem to argue that all symbol messages are irreducibly complex in this way, that they cannot be expressed in any shorter form than what they are, for to shorten or summarize them would be to make a different message by leaving out key information.

The fist example seems to indicate, however, that some significant portion of symbol messages, those parts that are roughly equivalent to style, are able to be reduced and maintain their identity. I’m not quite sure what the implication of this result is, but I find it interesting, especially in the context of analog and digital communication. Though Morse code is essentially a digital medium, the fist only appears as an analog aftereffect of the digital message. Similarly, Nicholson Baker’s advocation for the preservation of library card catalogs is an example of a digital message that is willing to discard analog aftereffects that are deemed unimportant.

Now, it is obvious that the digital portion of a message is also not irreducibly complex. New methods of compression might make it possible to transit the same message in a shorter form. The counterpart in communication theory, I suppose (and someone feel free to correct me if I’m getting all of these theories in a muddle), is that the iterable nature of symbol systems allows for shorthand communication of messages.

If messages are made up of both digital and analog components, neither of which are, by themselves, irreducibly complex, what then, in the Derridian sense, is the irreducible part of the message? I wonder if it is the interplay between these two elements, the connection between the analog and the digital, that is random and irreducible.

One of Gladwell’s arguments in Blink is that it is in our interest to discover where our unconscious decisions arise from as a means of determining whether or not they are to be trusted. On that note, a final thought: the analog portion of the message is much more difficult to counterfeit than the digital, though such imitation is not impossible. When all information is digitized, it is able to be copied—falsified—endlessly, much more easily than analog messages. This is because digital messages lack the global, emergent features of the reducibly complex, like the Morse code operator’s fist. As digital information is still carried via analog devices (analog telephone lines, for instance) it is possible that this portion of the signal can be analyzed for identifying features. One solution to our current concerns with digital security might be finding a way to reconnect the analog and the digital, making individual messages more difficult (perhaps impossible?) to counterfeit.

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.

Top-Down

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 Kauffman cites the Cambrian explosion—the appearance of an abundance of new life forms during the Cambrian period—as an example of a phenomenon that is difficult to explain using only the theory of natural selection. Natural selection suggests that gradual changes over time slowly accrue, allowing for the development of fitter organisms. This idea, however, is difficult to map onto the relatively rapid appearance of many different body plans during that period. A more selection-friendly period, Kauffman notes, is the rebound from the Permian extinction, when “96 percent of all species disappeared” (13). After the Permian the divergence in body plans, or phyla, basically ended. While there were “many new families, a few new orders, [and] one new class” that appeared at that time, there were no new phyla.

Kauffman refers to the Cambrian explosion as a top-down event—the rapid appearance of many wildly divergent kinds of organisms—while he calls the rebound from the Permian extinction—where there were many changes in the makeup of different organisms, but no new body plans—a bottom-up event (13). This movement from top-down to bottom-up events is typical, according to Kauffman, and is also seen in technological innovations, where an initial period of discovery is followed by an explosion of variations that later settle down into a few distinct, usually optimum, plans. The “branchings of life” that this particular view exhibits follows what Kauffman feels to be a lawful pattern—”dramatic at first, then dwindling to twiddling with details later”, what he calls a “complexity catastrophe” (14, 194). This catastrophe explains how “the more complex an organism, the more difficult it is to make and accumulate useful drastic changes through natural selection”, for “As the number of genes increases, long-jump adaptations becomes less and less fruitful” (194-95).

This process of organization comes from the tendency of “complex chemical systems” to become autocatalytic, that is exhibit a “self-maintaining and self-reproducing metabolism”, and it allows the process of ontogeny, where at division cells differentiate for different purposes (47, 50).

In the first case, Kauffman demonstrates with Boolean networks how autocatalysis occurs naturally in chemical systems. The image on the right shows “a Boolean network with two inputs per node” where “colors represent the state of a node” as being on or off. Using a sparsely-connected network like this to model simple chemical reactions, Kauffman shows that “when the number of different kinds of molecules in a chemical soup passes a certain threshold, a self-sustaining network of reactions”, or autocatalysis, “will suddenly appear” (47). Kauffman argues that his behavior on the part of these chemical systems is completely expected, and therefore not mysterious, as many attempts to explain it imply. Kauffman’s Boolean networks show that “when a large enough number of reactions are catalyzed in a chemical reaction of system, a vast web of catalyzed reactions will suddenly crystallize”, a property that is completely expected (50).

These complex behaviors also explain ontogeny. The tendency of complex chemical systems to organize themselves into auto-catalytic systems leads to those systems settling into a few attractors (see Order for Free for a discussion of attractors in biological systems), a result that allows the cells to differentiate but only in limited ways. As cells branch out to become particular kinds of cells in the organism, their tendency to stay in the basin of the attractor keeps them from becoming disordered, yet allows them to continue to propagate themselves. Order, then, from both the top-down and the bottom-up, is “vast and generative” and “arises naturally” out of common chemical interactions (25).

Order for free

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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.

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.

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.

Communication as relationship

Update: This post is a partial review of Gregory Bateson’s Steps to an Ecology of Mind

In his references to communication, Bateson provides an intriguing alternative to the prevailing model of the persuasive process. That process is largely considered to consist of an author/speaker presenting some superior content, and that content, through its effective presentation, persuading an audience of the author’s thesis. (This is certainly not the only model for communication, but I would think it is the one that most people—especially non-academics—would adhere to.) Rhetoricians would focus on the importance of the presentation, that is, all of the choices the author/speaker makes in presenting the content, but primacy would be given to the content. This formulation privileging content would not only be considered the best one, but possibly the most ethical one as well.

Bateson presents an alternative. Although he does not ignore the importance of content, his model of communication focuses on the presentation, what he refers to variously as “structure” or “patterns,” of that communication, rather than what is being presented, and these structures carry information about relationships. Referring to his own speech at an academic conference, Bateson remarks that his real topic “is a discussion of the patterns of [his] relationship” to the audience, even though, as he tells them, that discussion takes the form of his trying to “convince you, try to get you to see things my way, try to earn your respect, try to indicate my respect for you, challenge you” (372). In other words, the acceptance of the content is dependent upon how well he as a speaker is able to affirm that relationship. In another example, he points out that if one were to make a comment about the rain to another person, hearer would be inclined to look out the window to verify the statement. Though he notes that such behavior can seem confusing because it is redundant, this redundancy is not a question of the first speaker’s truthfulness, but rather an example of an attempt to “to test or verify the correctness of our view of our relationship to others” (132).

This patterning or structure, the need to comment on the relationship between speaker an audience by 1) conforming to arbitrary rules (another kind of structure) like those set up in an academic conference or 2) reminding ourselves that a person is a trustworthy source of information, is, to Bateson, representative of “the necessarily hierarchic structure of all communicational systems” which manifests itself in communication about communication (132). For Bateson, this realization has at least two outcomes. First, in learning it means that what is learned by the subject is often not the topic under study per se, but how “the subject is learning to orient himself to certain types of contexts, or is acquiring ‘insight’ into the contexts of problem solving,” that is, “acquir[ing] a habit of looking for contexts and sequences of one type rather than another, a habit of ‘punctuating’ the stream of events to give repetitions of a certain type a meaningful sequence” (166). Second, this means that these structures of “contexts and sequences” replicate themselves in learners, who then become audiences, and those audiences respond to recognition (perhaps the wrong word) of that structure. As Bateson says, “in all communication, there must be a relevance between the contextual structure of the message and some structuring of the recipient” for “people will respond most energetically when the context is structured to appeal to their habitual patterns of reaction” (154, 104). In these situations, what appeals to or persuades the audience is not the content, but the recognition of the familiar structure in the communication in question.

I think this focus on the structure of communication and the way in which that structure replicates itself across individuals highlights the technological dimension of speech—the way in which “meaning” is dependent upon the form of language—and has some interesting applications for the question of ethics and truthfulness in rhetoric, a topic I will comment on in my next post.

The Rhetoric of . . .

Update: This post is a partial review of Gregory Bateson’s Steps to an Ecology of Mind

Reading Gregory Bateson’s introduction to his Steps to an Ecology of Mind, two things jumped out at me. First was his notion that human processes had to that point (1971) been described in terms of energy, a tendency which Bateson points out is misleading, for it frames the discussion of human cognitive processes—“behavior”—in terms of “mass and length” (xxix). Instead, Bateson suggests that these processes should be looked at in regards to a completely different paradigm: that of ordering. Bateson calls the energy example “the wrong half of the ancient dichotomy between form and substance” (his point illustrated with two accounts of creation, both of which are focused on this division) (xxxii).

The form-substance dichotomy has interesting parallels with language theory, particularly in the flawed notion that substance is superior to form; in language, this distinction might be made between, speaking loosely, grammar and meaning. While substance/meaning is of great importance—in science as well as language—one cannot understand it apart from form. I was particularly interested in this point because this kind of investigation can take the form of a “rhetoric.”

I will explain with my second point of interest: this connection between form-substance and the practice of rhetoric (which I feel is significant, though I realize my statement of this significance is loose as of yet) seemed to be emphasized early in Bateson's introduction. He writes “But the definition of an ‘idea’ which the essays [in this book] combine to propose is much wider and more formal than is conventional. . . . let me state my belief that such matters as the bilateral symmetry of an animal, the patterned arrangement of leaves in a plant, the escalation of an armaments race, the processes of courtship, the nature of play, the grammar of a sentence, the mystery of biological evolution, and the contemporary crises in man's relationship to his environment, can only be understood in terms of such an ecology of ideas as I propose” (xxiii). Earlier he explains that the “ecology of ideas” he refers to is connected to what he calls minds. In the case of this long quote, I think there is an intimate relationship between many of the things listed (courtship, play, arms races, grammar) and what is known as rhetoric. Any one of the items I have just listed could be discussed in terms of “The Rhetoric of X” in the sense that Bateson is getting at. It is my feeling that he is attempting to ask fundamental questions about order that are related to the kinds of answers that rhetoric has tried to use to explain complex human interactions like persuasion. Certainly the part of “rhetoric of” that fails is when one focuses only on rhetoric textbooks, that is mere instructions of how to do something, but Bateson here opens the door (I think) to a conversation between rhetoric and his ecologies.

Bateson’s chapter “Experiments in Thinking About Observed Ethnological Material” in particular seems like nothing more than a rhetoric of scientific discovery, where he proposes that in science there should be alternate periods of “loose thinking and the building up of a structure on unsound foundations” followed by “the correction to stricter thinking and the substitution of a new underpinning beneath the already constructed mass” (86). One way in which this kind of see-sawing method can be achieved is through “train[ing] scientists to look among the older sciences for wild analogies to their own material” (87). Also interesting (I'll try and return to it later) is his worry that in some stages of his career he relied too heavily on words that were “too short and therefore appear more concrete than they are,” which seems to be a specific case of dissonance between form and substance (82).