This is about computational thinking. But computational thinking is not one thing. It is many, some as yet undefined. What can it become for students of the humanities?
How, you might ask, are we to engage a computational understanding of literary process, if computation isn’t well-defined?
With care, I say, with care. We have to make it up.
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As Stephen Ramsay pointed out in a post, DH and CS (where DH = digital humanities and CS = computer science), computer scientists are mostly interested in abstract matters of computability and data structures while programmers are mostly concerned with the techniques of programming certain kinds of capabilities in this or that language. Those are different, though related, undertakings.
Further, the practical craft has two somewhat different aspects. One faces toward the end user and is concerned with capturing that user’s world in the overall design of the program. This design process is, in effect, applied cognitive anthropology. The other aspect faces toward the computer itself and is concerned with implementing that design through the means available in the appropriate programming language. This is writing, but in a very specialized dialect. But it’s all computational thinking in some meaningful sense.
Though I have written a computer program or three, that was long ago. I have, however, spent a fair amount of time working with programmers. At one period in my life I documented software; at a different time I participated in product design.
But I also spent several years in graduate school studying the computational semantics of natural language with the late David Hays. That’s an abstract and theoretical enterprise. Though he is one of the founders of computational linguistics, Hays did no programming until relatively late in his career, after he’d left academia. He was interested in how the mind works and computation was one of his conceptual strategies. I studied with Hays because I wanted to figure out how poetry worked. All the members of his research group were interested in the human mind in one way or another; some of them were also programmers of appreciable skill.
Out of all that I’ve developed a computational style of thought. It has two faces. One looks at specific literary texts, often in great detail. It is descriptive and analytic. The other attempts to look at the mechanisms of textuality, whether at the scale of individual texts or that of literary history. This mode of thinking is theoretical.
And we might say that those two styles of thinking are connected by a sense of reverse engineering. The theoretician is attempting to craft mechanisms which, when put in motion, would read texts with concentration and wonder. The practical analyst is looking for features in texts that betray the workings of those mechanisms.
It’s a delicate and crazy dance.
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The rest of this post glosses four books each of which embodies some aspect this computational style. In a second post I’ll presents a thought experiment from one of those books. In this thought experiment we trace the path of a single ant as it walks on a beach. Think of the ant’s path as a text.
The first book, Scott McCloud’s Understanding Comics, has nothing to do with computing; but it conveys a wonderful sense of how stories are designed and built. The second, Valentine Braitenberg’s Vehicles, imagines a series of toy vehicles, each more capable than the previous one; it conveys a sense of computing as a means for getting about in the world. The third book is The Sciences of the Artificial by Herbert Simon, one of the founders of artificial intelligence, and conveys Simon’s own sense of the computational style. The fourth and last book, The Computer and the Brain, is by John von Neumann, one of the seminal thinkers of the previous century, and is about how computing can be implemented in a material substrate.
(1) Scott McCloud (1993). Understanding Comics. New York: HarperPerennial.
In some odd, but wonderful, ways this may be the best single introduction to the computational (aka cognitive, compositionist) study of literature. It's not an academic book; there's no scholarly apparatus. But it yields a superb sense of what it is like to think about story-telling from a cognitive or compositionist point of view.
It takes the form of a comic book, words and images in panels cover every page - McCloud is a cartoonist. The pictorial form is what makes it so effective. So, McCloud has the reader thinking about visual objects and how they're constructed and how those constructions are organized into stories.
It conveys a sense of design, engineering, and construction which is very important and which, alas, is missing in the current literary cognition literature. It gives the reader a sense of mechanism without the pain involved in detailed understanding the computational models of the cognitive sciences.
(2) Valentine Braitenberg (1999). Vehicles: Experiments in Synthetic Psychology. Cambridge, MA: MIT Press.
This is a cumulative series of thought experiments, 14 of them in the first 83 pages. Braitenberg asks us to imagine a simple (artificial) creature in a simple environment. Here’s how he begins to describe the first one: “Vehicle 1 is equipped with one sensor and one motor. The connection is a very simple one. The more there is of the quality to which the sensor is tuned, the faster the motor goes” (p. 3). He then works out the consequences of this very simple creature, how it moves about.
In the second chapter he gives the vehicle two sensors and two motors and from that constructs primitive fear and aggression. And so it goes for the rest of these 14 chapters. In each chapter he adds a little bit to the vehicle from the previous chapter and explores the behavior consequences, e.g.: love (vehicle 3), concepts (#7), getting ideas (#10), egotism and optimism (#14). The last 50 pages contain biological notes on the vehicles, thus relating to the real nervous systems of real animals. Like the McCloud, it conveys a sense of design, engineering, and construction that will be important in developing a rich computational criticism.
(3) Herbert A. Simon (1981). The Sciences of the Artificial, Second Edition. Cambridge, MA: MIT Press.
Trained in political science, Simon became one of the founding fathers of artificial intelligence, computing, and cognitive science during the 1950s and 60s. This is a relatively informal collection of essays that has been widely, and justly, influential. From the preface (xi):
Engineering, medicine, business, architecture, and painting are concerned, not with how things are but with how they might be—in short, with design. . . . These essays then attempt to explain how a science of the artificial is possible and to illustrate its nature. I have taken as my main examples the fields of economics (chapter 2), the psychology of cognition (chapters 3 and 4), and planning and engineering design (chapters 5 and 6) [my emphasis, BB].
Chapter 7 is entitled “The Architecture of Complexity” (originally published in 1962) and takes up the problem of biological evolution. Chapter 3, “The Psychology of Thinking: Embedding Artifice in Nature,” contains a well-known passage about an ant walking on the beach.
(4) John von Neumann (1958). The Computer and the Brain. New Haven: Yale University Press.
Von Neumann was a mathematician who made contributions in many fields. But he is best known for his work in computing. This slender volume (82 pages) is the last project he worked on and is incomplete. Brain cancer took him before he could finish.
The book is about how a computing process can be embodied or implemented in physical matter, and discusses two modes, the analog and the digital, and, among other things, addresses the limitations these modes impose. Real computation is a material process. Therefore a computational approach to literature is materialist.
Though the book contains no math, it is quite abstract. Its details are at some remove from all the complex details about existing computers (then and now) or the messy wetware of the brain. That is to say, it is about the essential.
Forget about the fact that computers are now quite different from those von Neumann knew, and forget about the fact that most of what we know about the brain was discovered since von Neumann’s death. In this book a first-class mind grapples with deep questions in simple, if abstract, terms. Reading it is a good workout.
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This is somewhat revised from a previous version HERE.