Is software a kind of mathematics? – From an ongoing discussion on the Humanist list [#DH #Digital Humanities]

I’ve appended two emails I recently sent to the Humanist list. They’re recent contributions to a long and winding discussion.

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There’s a somewhat diffuse set of conversations going on over the last few weeks or so. I believe it’s been precipitated by remarks Willard made in Humanist 34.27: punctuation in the assignment statement. Almost at the end Willard remarked:

So I translate "X is to be MADE equal to the sum of Y and Z". Or, we might say, "Warning: this is NOT an algebraic statement!" Or, better, I think: "Here we must stretch our understanding of mathematics to include something new.

It’s that very last sentence that interests me, about stretching our understanding of mathematics. In Humanist 34.39 Willard elaborates on the target and scope of his inquiry:

I'm looking for evidence concerning the realisation or conviction that software, though mathematical in some sense, is "mathematics with a difference", as Mahoney wrote in "Computer science: The search for a mathematical theory". To ask whether software is mathematics or is essentially mathematical, expecting a yes-or-no response even if with arguments, seems to me too crude an instrument. So, I am wanting to ask, what is software in relation to mathematics? I don't assume that either is a well-defined (or confined) thing.

So, is software to be considered a kind of mathematics, with that little bit of notation as evidence bearing on the matter?

I’ll leave notation aside. It’s the relation between mathematics and software that concerns me. It seems to me that there’s a bit of ambiguity in the word “mathematics”. My dictionary tells me that it’s “the abstract science of number, quantity, and space…” That is, it is an intellectual discipline, something that people do. My dictionary also offers: “the mathematical aspects of something: the mathematics of general relativity.” That seems a bit different. Here “mathematics” is functioning not so much a discipline as it is the objects which that discipline is about.

What of “software”? Software is not at all a discipline. Computer programming is the discipline concerned with software. Whatever computer programming is, it is not reasonably considered a branch of mathematics. Nor for that matter does it make much sense to think of computer programs directly as mathematical objects, though we may subject them to mathematics analysis, which is a different matter (in principle, we can subject anything to mathematical analysis).

So, notation aside, I agree with Willard that “whether software is mathematics or is essentially mathematical, expecting a yes-or-no response even if with arguments, seems to me too crude an instrument.” Software is something else. Though it has some kinship with mathematics, it also has some kinship with natural language. But it does seem to me that we must think of it as something in itself, different from both mathematics and natural language. It is something new that entered the world in the decades after World War II.

This thoughtful paper by László Lovász explores how current developments in mathematics might alter how and what we teach. Though written in 2008, it asks questions that are still fresh and necessary, but not yet widely appreciated. https://t.co/vkKu94eBwB [PDF] (ht Etienne Ghys)

— Steven Strogatz (@stevenstrogatz) June 10, 2020

I’ve just come across (via Steven Strogatz on Twitter) a March 2008 paper by László Lovász that’s relevant to our ongoing discussion of software, mathematics, and the machine: Trends in Mathematics: How they could Change Education? Here’s the link:

https://web.cs.elte.hu/~lovasz/lisbon.pdf

I’m copying some paragraphs from section 5.1, Algorithms and programming.

The traditional 2500 year old paradigm of mathematical research is defining notions, stating theorems and proving them. Perhaps less recognized, but almost this old, is algorithm design (think of the Euclidean Algorithm or Newton’s Method). While different, these two ways of doing mathematics are strongly interconnected (see [6]). It is also obvious that computers have increased the visibility and respectability of algorithm design substantially.

Algorithmic mathematics (put into focus by computers, but existent and important way before their development!) is not the antithesis of the “theorem–proof” type classical mathematics, which we call here structural. Rather, it enriches several classical branches of mathematics with new insight, new kinds of problems, and new approaches to solve these. So: not algorithmic or structural mathematics, but algorithmic and structural mathematics!

So, we’ve got a distinction between algorithmic and structural mathematics. [And remember, “algorithm” is derived from the name of a mathematician, al-Ḵwārizmī.]

Moving on:

The beginning of learning “algorithmics” is to learn to design, rather than execute, algorithms [8]. The euclidean algorithm, for example, is one that can be “discovered” by students in class. In time, a collection of “algorithm design problems” will arise (just as there are large collections of problems and exercises in algebraic identities, geometric constructions or elementary proofs in geometry). Along with these concrete algorithms, the students should get familiar with basic notions of the theory of algorithms: input-output, correctness and its proof, analysis of running time and space, etc.

Here, notice the distinction between designing algorithms and executing them.

And now we come to the computer (programming language running on hardware):

One should distinguish between an algorithm and its implementation as a computer program. The algorithm itself is a mathematical object; the program depends on the machine and/or on the programming language. It is of course necessary that the students see how an algorithm leads to a program that runs on a computer; but it is not necessary that every algorithm they learn about or they design be implemented. The situation is analogous to that of geometric constructions with ruler and compass: some constructions have to be carried out on paper, but for some more, it may be enough to give the mathematical solution (since the point is not to learn to draw but to provide afield of applications for a variety of geometric notions and results).

So, an algorithm is to be distinguished from its implementation*. A given algorithm could be implemented using a variety of programming languages, with some being more hospitable than others. And the same is true for the underlying hardware. In particular, some algorithms may benefit from a parallel architecture, while others may not.

I present three more paragraphs without comment:

Let me insert a warning about the shortcomings of algorithmic language. There is no generally accepted form of presenting an algorithm, even in the research literature (and as far as I see, computer science text books for secondary schools are even less standardized and often even more extravagant in handling this problem.) The practice ranges from an entirely informal description to programs in specific programming languages. There are good arguments in favor of both solutions; I am leaning towards informality, since I feel that implementation details often cover up the mathematical essence. For example, an algorithm may contain a step “Select any element of set S”. In an implementation, we have to specify which element to choose, so this step necessarily becomes something like “Select the first element of set S”. But there may be another algorithm, where it is important the we select the first element; turning both algorithms into programs hides this important detail. Or it may turn out that there is some advantage in selecting the last element of S. Giving an informal description leaves this option open, while turning the algorithm into a program forbids it.

On the other hand, the main problem with the informal presentation of algorithms is that the “running time” or “number of steps” are difficult to define; this depends on the details of implementation, down to a level below the programming language; it depends on the data representation and data structures used.

The route from the mathematical idea of an algorithm to a computer program is long. It takes the careful design of the algorithm; analysis and improvements of running time and space requirements; selection of (sometimes mathematically very involved) data structures; and programming. In college, to follow this route is very instructive for the students. But even in secondary school mathematics, at least the mathematics and implementation of an algorithm should be distinguished.

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*In a series of discussions I had with David Hays a number of years ago he emphasized the fundamental importance of implementation. As I said almost a decade ago:

...we talked of the universe as being fecund (Hays proposed the term), with later Realms of Being implemented in ones that had evolved earlier. Our notion of implementation, which derives from computer science, seems kin to Latour’s notion of composition. Objects of one kind can be said to be implemented in, composed of, objects of some other kind. But the implemented/composed objects cannot be reduced to the objects of which they are composed/implemented.

The full discussion:  Fecundity and Implementation in a Complex Universe.

"Everything is subjective? – Really? Do we want to do down that rabbit hole?

And another from the ever-quotable @nowviskie https://t.co/0mGmGlSBbK

— Rebecca Sutton Koeser (@suttonkoeser) January 15, 2019

No, I don't think we do, though it's all to 'ready at hand' for many humanists.

One thing we should do is read John Searle on objectivity and subjectivity. Alas, that's likely to make things a bit complicated. But the issue is an important one, so we should be willing to shoulder the complexity. See, e.g., these posts:

• Meaning & texts: objectivity, subjectivity
• Subjectivity vs. Objectivity in the epistemic and ontological senses (John Searle)
• Another (strenuous) take on what went wrong with literary criticism, John Searle and Geoffrey Hartman edition
• Objectivity and Intersubjective Agreement

Is that it, do humanists (really) want to speak with the dead?

Steven Klein, The New Science Wars, The Chronicle Review, 12.16.16:

Because it dwells on these historically specific phenomena, humanistic inquiry is equipped to understand the contours of human experience and activity in a way the sciences cannot. The stance of humanistic inquiry is one of dialogue with its subjects — an imaginary one, of course, but one full of chastisement and support. In Stephen Greenblatt’s well-known phrase, humanistic scholarship springs from "the desire to speak with the dead." Scholars are interested in how people understood themselves, how they interacted with their cultural worlds, how they negotiated their everyday lives. At the core of the humanities is the attempt to enter into distant worlds and to see their connection to us.

[Alas] I think that's how it is, certain with literary criticism. After WWII the discipline decided to ground itself in the activity of interpretation and that, in turn, is grounded in conversation. That allows a certain kind of access, access necessary and valuable. But it also closes certain things off. Klein continues:

Because it rests on this act of imaginative judgment, humanistic scholarship can never suspend or escape the particular perspective of the researcher. To understand someone’s reasons for doing something, we are, to some extent, always imagining how we would act under similar circumstances. As a result, humanistic approaches can never fully embrace the ideal of pure scientific objectivity. Nor would they want to. The humanities cannot help but view humans as morally responsible agents. We are interested not just in why people do something, but in the reasons they themselves give. Interpreting our actions through the lens of the justifications we provide, a humanistic account passes some judgment on those justifications. We want to know the reasons people do things so we can reflect on whether our current reasons are good reasons.

I don't know about scientific objectivity – as you may know, "I don't give a crap about science" – but, objectivity, yes. That's what I'm after in the study of literary form, its analysis and description.

And if I had to speculate about how that kind of inquiry is grounded in a basic human activity, I'd pick tracking. At some point the skilled animal tracker may well try to imagine what the animal is thinking at every step of the way; in fact, I'm sure of it. But that's always in service of interpreting the signs, which are out there in space. Those signs are objects, as are the animals whose presence/absence they betray (in the act of tracking). Tracking is not an entirely visual activity – one attends to sounds and smells – but it is grounded in what you see before you.

And that, in my view, is what describing literary form is about. You are visualizing the text and using visual means to describe it, whether simple tables,, or diagrams of various kinds. These visual aids are not mere aids, they're the map itself.

Do literary critics really want to abandon the study of literary form? Though "abandon" is a bit of a stretch, at least from my point of view. As far as I'm concerned, the discipline has never really engaged with literary form. Nor, in a sense, with the text.  But those are larger discussions, one's I've engaged here and there.

Addendum, an hour or so later: What I dislike about this kind of argument is that there is little or no sense of loss, that this humanist stance has real costs, that it closes off avenues to worthwhile understanding. It's all well and good to argue for "dialog with the dead". But to do so without realizing that that is a limited view, that's unfortunate. That is enslavement to the past. I want to see humanists who choose the dialogic stance while at the same time recognizing its limitations, its cost.

H/t 3QD.

Objectivity

Here is the publisher's blurb for Lorraine J. Daston and Peter Galison, Objectivity, MIT Press, 2007:

Objectivity has a history, and it is full of surprises. In Objectivity, Lorraine Daston and Peter Galison chart the emergence of objectivity in the mid-nineteenth-century sciences—and show how the concept differs from its alternatives, truth-to-nature and trained judgment. This is a story of lofty epistemic ideals fused with workaday practices in the making of scientific images.

From the eighteenth through the early twenty-first centuries, the images that reveal the deepest commitments of the empirical sciences—from anatomy to crystallography—are those featured in scientific atlases, the compendia that teach practitioners what is worth looking at and how to look at it. Galison and Daston use atlas images to uncover a hidden history of scientific objectivity and its rivals. Whether an atlas maker idealizes an image to capture the essentials in the name of truth-to-nature or refuses to erase even the most incidental detail in the name of objectivity or highlights patterns in the name of trained judgment is a decision enforced by an ethos as well as by an epistemology.

As Daston and Galison argue, atlases shape the subjects as well as the objects of science. To pursue objectivity—or truth-to-nature or trained judgment—is simultaneously to cultivate a distinctive scientific self wherein knowing and knower converge. Moreover, the very point at which they visibly converge is in the very act of seeing not as a separate individual but as a member of a particular scientific community. Embedded in the atlas image, therefore, are the traces of consequential choices about knowledge, persona, and collective sight. Objectivity is a book addressed to anyone interested in the elusive and crucial notion of objectivity—and in what it means to peer into the world scientifically.

Interesting, very interesting.

Two questions about language: Why is it computationally privileged? Why is literary form objectively knowable, but meaning not?

What does that first question even mean? In what sense is language computationally privileged? They may well be an abstract answer to that question, but I don’t have to skills to articulate it. So I’ll have to do so indirectly.

In the decade or so following World War II electronic digital computation developed around a few key problems: 1) data processing, e.g. tabulating census statistics, 2) dynamics, e.g. simulating atomic explosions, artillery calculations, and 3) machine translation. Not artificial vision, or hearing, or motor kinematics, or any other human sensory or motor activity. The problem was to translate texts from one natural language to another.

THAT’s computational privilege, but I mean privilege in perhaps a peculiar sense. Machine translation, of course, was important for reasons of national defense. It wasn’t just any language we wanted to translate from; it was Russian. But that’s not what I mean by privilege. That’s why the work was funded, but by privilege I mean something like tractable. Language was deemed computationally tractable.

Language is computationally tractable in a way that those other activities – seeing, hearing, moving the body – are not. Why? Because digital computing is itself a linguistic activity, albeit that languages involved are highly restricted and limited in a way that natural languages aren’t. Still, natural language is more like computer languages than seeing, hearing, and jumping rope are. That’s what I mean by computationally tractable.

And this point it gets a little tricky. What is arithmetic? Ordinarily we think of it as a kind of math, which is very different from language. Ordinarily, that’s so. But it’s also superficial.

How do we do arithmetic calculations? One can use a simple mechanical device, like an abacus. But we’re taught to do it with numerical symbols, for values zero through nine, plus a decimal point, plus four operators (plus, minus, times, divided by) and the equals sign. That, plus a bunch of simple rules, makes arithmetic a kind of simple language. There is thus a deep connection between language and arithmetic, and hence between language and mathematics.

That’s computational privilege.

Now, our second question: Why is literary form objectively knowable, but meaning not? Caveat: This is going to be quick and impressionistic, more of a conceptual placeholder than anything else.

Literary form belongs to the computable aspect of language. Word meaning, and hence the meaning of texts of any kind, including literary texts, ultimately depends on the world and our access to the world through the senses and the motor system. That access is, in principle, open-ended and undefined. It is not computable.

Word meaning, I submit, is pretty much like those elusive qualia that philosophers talk about. Perhaps we can think of it as the qualia of the mind. We can build and test models of visual perception, for example, and so investigate the relationship between objective characteristics of the visual scene and color perception, that is, we can build models of how qualia arise, but those models are models, not qualia themselves. If the model is implemented in a computational system attached to visual sensors, then it may actually created pseudo qualia, but real qualia require a living system. We don’t know how to create those.

The same is true for language, for meaning vs. semantics. We can build a semantic model, which is about how words and texts have meaning. But it IS a model, not meaning. (For more on the distinction between meaning and semantics, see my recent post, 2 Comments on Moretti’s LitLab 15: Patterns and Interpretation [#DH].)

Meaning and qualia are both ontologically and epistemologically subjective in Searle’s sense. Semantics models and sensory models are both ontologically and epistemologically objective in Searle’s sense [1]. (Also see my recent post, Objectivity and Intersubjective Agreement.)

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[1] John R. Searle, The Construction of Social Reality, Penguin Books, 1995, pp. 5 ff.

Images and Objectivity

Ryan Cordell has an interesting post, Objectivity and Distant Reading, in which he comments on Objectivity (2010) by Lorraine Daston and Peter Galison:

Objectivity attempts to trace the emergence of scientific objectivity as a concept, ideal, and moral framework for researchers during the nineteenth century. In particular, the book focuses on shifting ideas about scientific images during the period. In the eighteenth and early nineteenth centuries, Daston and Galison argue, the scientific ideal was “truth-to-nature,” in which particular examples are primarily useful for the ways in which they reflect and help construct an ideal type: not this leaf, specifically, but this type of leaf. Under this regime scientific illustrations did not attempt to reconstruct individual, imperfect specimens, but instead to generalize from specimens and portray a perfect type.

Objectivity shows how, as the nineteenth century progressed and new image technologies such as photography shifted the possibilities for scientific imagery, truth-to-nature fell out of favor, while objectivity rose to prominence.

And that's what interests me, the focus on images, and the rise of photography:

In debates about the virtues of illustration versus photography, for instance, illustration was touted as superior to the relative primitivism of photography—technologies such as drawing and engraving simply allowed finer detail than blurry nineteenth century photography could. Nevertheless photography increasingly dominated scientific images because it was seen as less susceptible to manipulation, less dependent on the imagination of the artist (or, indeed, of the scientist).

Images, of course, are clearly distinct from the prose in which they are (often) set. Images are a form of objectification, though it takes more than objectification to yield objectivity.

Cordell then goes on to discuss computational criticism (aka distant reading), where "computation is invoked as a solution to problems of will that are quite familiar from decades of humanistic scholarship." Computational critics

might argue that methods such as distant reading or macroanalysis seek to bypass the human will that constructed such canons through a kind of mechanical objectivity. While human beings choose what to focus on for all kinds of reasons, many of them suspect, the computer will look for patterns unencumbered any of those reasons. The machine is less susceptible to the social, political, or identity manipulations of canon formation.

Interesting stuff. I've got two comments:

1) Consider one of my touchstone passages by Sydney Lamb, a linguist of Chomsky’s generation but of a very different intellectual temperament. Lamb cut his intellectual teeth on computer models of language processes and was concerned about the neural plausibility of such models. In his major systematic statement, Pathways of the Brain: The Neurocognitive Basis of Language (John Benjamins 1999) remarked on importance of visual notation (p. 274): “... it is precisely because we are talking about ordinary language that we need to adopt a notation as different from ordinary language as possible, to keep us from getting lost in confusion between the object of description and the means of description.” That is, we need the visual notation in order to objectify language mechanisms.

I note that, I think of objectification (in the sense immediately above) as a prerequisite for objectivity, but it is by no means a guarantee of it. That requires empirical evidence. A computer model will give us objectification, but no more.

2) Tyler Cowen has an interesting and wide-ranging interview with Jill Lepore in which she notes that Frederick Douglass was the most widely photographed man of 19th century America: "In the 1860s, he writes all these essays about photography in which he argues that photography is the most democratic art. And he means portrait photography. And that no white man will ever make a true likeness of a black man because he’s been represented in caricature — the kind of runaway slave ad with the guy, the little figure, silhouette of the black figure carrying a sack."

Humanistic Thought as Prose-Centric Thought

Back on March 17, 2010 I made a short post to The Valve: “Style Matters”. I reposted it to New Savanna in June of 2012 and them bumped it to the top of the queue on September 6 of this year. That history suggests that the idea is important to me, yet until quite recently ¬– the last few weeks – it was just an isolated observation. Finally, however, I’ve incorporated it into the ongoing line of investigation.

It’s a line of thinking about the nature of humanistic investigation that I’ve been pursuing off and on since, well, forever, or at any rate since my undergraduate years at Johns Hopkins during the Sturm und Drang of Structuralism and deconstruction when I was led to believe, willingly I might add, that Western metaphysics was in shambles and it was up to us to create a new world. For me that meant cognitive science. Yes, I know, cognitive science and deconstruction don’t have much in common, then or now. But, strange as it may seem in retrospect, we didn’t know that BACK THEN. No one told me not to do it. My humanist teachers certainly didn’t say “no”, even if they didn’t really understand this cognitive science stuff, and some of them even encouraged me.

Little did I know that, when I’d opted for cognitive science, for all intellectual purposes I’d left the profession – by which I mean academic literary criticism. On the contrary, as things worked out, I was able to study with a world-class cognitive scientist while pursuing my Ph.D. in the English Department at SUNY Buffalo in the mid-1970s. Hays was in the Linguistics Department – had been its founding chairman – and the English Department, which was very experimental, was perfectly happy for me to study with Hays. And so I wrote a dissertation – Cognitive Science and Literary Theory – which was as much a technical exercise in cognitive science as it was a contribution to literary theory.

I figured that THAT would be my contribution to the discipline, my calling card. It got me my first and only academic job, and that was it. Not even a handful of publications, some in first-class journals, could get me a second academic job. By the mid-1980s my career was over. I’d left the profession.

But, as I said, in another sense I’d left the profession by the time I’d decided to pursue cognitive science in the course getting of my doctorate in English literature. While it was obvious to everyone at the time that my work was quite unusual for a literary scholar, it wasn’t at all obvious that it was out of bounds. Because it wasn’t.

Not back then. As I’ve said already, that boundary hadn’t been drawn. Oddly enough, though cognitive science now has a following in literary criticism, and has had one for at least two decades, the work I did back then is still out of bounds. Even though it wasn’t out of bounds when I did it.

Boundaries are strange things.

Go figure.

Reading Macroanalysis 1: Framing: Hyperobjects, Objectification, and Evolution

Matthew L. Jockers. Macroanalysis: Digital Methods & Literary History. University of Illinois Press, 2013. x + 192 pp. ISBN 978-0252-07907-8

The book arrived midway last week, when I hadn’t even finished reading Tim Morton’s Hyperobjects, much less finished blogging about it. But that didn’t stop me from giving Macroanalysis a look-thru: contents, some of the figures, read a bit here and there. I ended up reading Chapter 9, “Influence”, first; I’d read Matt Wilkins’ review in the LA Review of Books:

It’s a nifty approach that produces a fascinatingly opaque result: Tristram Shandy, Laurence Sterne’s famously odd 18th-century bildungsroman, is judged to be the most influential member of the collection, followed by George Gissing’s unremarkable The Whirlpool (1897) and Benjamin Disraeli’s decidedly minor romance Venetia (1837). If you can make sense of this result, you’re ahead of Jockers himself, who more or less throws up his hands and ends both the chapter and the analytical portion of the book a paragraph later.

Would I be able to make sense of those results? thought I to myself as I read. Nope, I couldn’t. Better luck next time.

I then read though the first four chapters, gathered together as Part I: Foundation (“Influence” ended Part II: Analysis). OK, I’ll go along with most of that, but... I skipped over Chapter 5, “Metadata” and dug into Chapter 6, “Style”. Hmmm, thought I to myself, if you recast the analysis in terms of cultural evolution, you might be able to frame an argument for the autonomous aesthetic realm, though Jockers frames the discussion as constraints of the author. And when I went back to the “Metadata” chapter, wouldn’t you know it, I saw another opening for an evolutionary formulation.

And that’s about where I am now. I’ve read the short coda, “Orphans”, where Jockers expresses ambivalence about cultural evolution, and I’ve got two substantive chapters to go, “Nationality” (ch. 7) and “Theme” (ch. 8). But I really need to get blogging.

As the title suggests, this post is preliminary. I’m not going to say much about Jockers’ specific arguments. Rather, I want to do a bit of framing.

The Scope of the Humanities

One can hardly imagine two such different examples of contemporary humanistic thought as Macroanalysis and last week’s book, Tim Morton’s Hyperobjects. Morton is working within an Anglophone Continental discourse with roots in Hegel, Heidegger, and post-structuralist philosophy and Theory while Jockers’ methodology is grounded in humanistic computing, corpus linguistics, and social science. If you were to cross-match their bibliographies, you wouldn’t find many texts in common. Further, while Morton is trained as a literary critic, and has a lit crit job at Rice, Hyperobjects is not literary criticism. It’s philosophy and cultural criticism. Jockers is all literature.

Such is the contemporary scope of the humanities.

Reading Hyperobjects 7: Objectification and Objects

Note: I’m counting my 3QD piece as #5 and my introduction to that as #6.

Let’s begin obliquely, with something I’ve come to think of as Hartman’s Line. Hartman is Geoffry Hartman, one of the (in)famous Yale Mafia, the dons of deconstruction back in the last quarter of the previous century, and the line is the one he draws between reading and semiotics/structuralism in this passage, among others, from The Fate of Reading (p. 271):

I wonder, finally, whether the very concept of reading is not in jeopardy. Pedagogically, of course, we still respond to those who call for improved reading skills; but to describe most semiological or structural analyses of poetry as a ”reading” extends the term almost beyond recognition.

Jonathan Culler had the same line in Structuralist Poetics (1975) when he asserted that linguistics is not hermeneutic.

That line, I submit, was about objectification. Linguistics objectifies language; structuralism and semiotics, at least in their more technical incarnations, objectify poetry. Objectification gets in the way of reading, of interpretation.

But what’s this have to do with hyperbojects?

Hold on. I’m getting there. Have a little patience.

OK.

By the time Hartman and Culler had made those statements, I’d already passed over to the dark side. I’d pretty much decided that the objectification of literary texts was the way to go, a story I’ve told in this post about my early encounter with Lévi-Strauss and “Kubla Khan.” Culler himself didn’t return to structuralism nor did the profession show any further interest in linguistics and structuralism. The profession had, in effect, rejected objectification.

And that left me stranded, pursuing objective knowledge while my one-time colleagues critiqued everything in sight. When I first encountered object-oriented ontology I was hoping that perhaps I could use it as philosophical support, or at least as ideological camouflage, for my own work. It didn’t take me long to figure out that that wasn’t very likely, but by then I was hooked.

And that brings us back to Morton and hyperobjects. For Hartman’s Line is just a special case of the more general line that phenomenology and its offshoots and critiques has drawn between thought and the world. The object oriented ontologists don’t cross that line either.

Literature, Criticism, and Pluralism

When I set out to investigate Object-Oriented Ontology a year and a half ago I had several things vaguely in mind: 1) the possibility that Continental philosophy was waking up from its dogmatic dreams of dalliance and devastation, 2) some help in conceptualizing graffiti as a manifestation of the spirit of the place, and 3) some help in framing some questions about literature and literary criticism. Let’s set the first two aside and take up the third.

I take the following three propositions to be true:

1) The meaning of literary texts is indeterminate.

2) There is, however, something quite precise about texts; I take that to be form.

3) The form of texts can be effectively described in that, presupposing agreement on method, different can come to agreement about formal attributes of a text.

As a practical matter, literary criticism has taken the first as a truism. Every critic gets to “roll their own” meaning for a text; all that one has to do is provide a reasonable justification within some accepted interpretive scheme. Beyond this tacit and informal practice, some critics have explicitly argued for indeterminate meaning while others have argued for determinate meaning, often making the author the source of that meaning.

OOO as Interpretive Scheme

Object-oriented ontology can easily serve as an interpretive scheme, providing of course, that it can justify itself as a philosophical regime. That is, primary justification comes in basic philosophical terms, not in the application to literature. From my point of view, this is neither here nor there. The addition of one more interpretive engine to the critic’s tool kit is of relatively little consequence if, like me, you want to do something other than, beyond, reading texts.

Harman, however, has made some remarks that point toward the possibility of non-reductive readings, readings that don’t bypass the “surface” of the text in haste to find the “hidden” meanings, which I’ve discussed in Explicating Literature in Light of Object-Oriented Ontology. But those remarks are only pointers. It’s not clear to me how they might open up into full-blown explications. And, in any event, an explication is, in the end, an explication is a reading, and I’m chasing different unicorns.

Nor, it seems to me, does OOO have anything special to say about textual indeterminacy. Levi Bryant, to be sure, has declared that texts are factories, in a usage from Deleuze and Guattari. As far as I can tell, Bryant has nothing particularly interesting to say about how it is that texts do this, just that they obviously do so. All Bryant has to offer is old wine in new bottles or, as Terrence Blake puts is, tautological reformulation.

Distant Reading in Lévi-Strauss and Moretti

This is a somewhat revised revision of a post I made at The Valve in November of 2009 under the title, Lévi-Strauss 2: Subject and Object. As the number indicates, it was the second in what would become four posts on my encounter with Lévi-Strauss's structuralism early in my career. The purpose of this revision is to recast that post as an argument that what Lévi-Strauss was doing, in particular in his study of myth in the four volumes of Mythologies, was a species of distant reading.

The term, distant reading, of course, is Franco Moretti's and, I presume, the rationale of invoking distance is to stand it in contrast to the (forms of) close reading so-beloved of the New Critics and many of their successors. What Moretti did in Graphs, Maps, Trees is indeed quite different from close reading of any species. In one chapter large numbers of texts become reduced to two items each, a publication date and a genre label. In another Moretti draws maps of where characters are located and how they move in geographical space. In a third he traces how certain stylistic features move from one text to another without giving any attention to nuances within any given text.

That is quite different from what Lévi-Strauss did with his myths, each of which he recounts in summary form, thus giving each one of them specific detailed representation within his text. He had quite a bit to say about each one of those myths, situating them within kinship structures and their associated behaviors, and commenting upon local geography, flora, and fauna. He was thus, in a sense, quite "close" to those myths. Yet . . .

Consider what Moretti says about distant reading. It is “a specific form of knowledge: fewer elements, hence a sharper sense of their overall interconnection. Shapes, relations, structures. Forms. Models.” Those terms, relations, structures, forms, models, certainly characterize Lévi-Strauss's concerns. His work was a sustained meditation on relations and models.

As for distance, it has its varieties too. Lévi-Strauss was not examining texts from his own culture; he was examining texts from other cultures, cultures to which he was an outsider, distanced if you will, though he did make the odd claim that his accounts of the myths were, as such, yet other retellings of them. He was not reflecting on those texts as a subject within the social world in which those texts circulated. As for Moretti, he did not read, in any ordinary sense, most of the texts that figured as data points in his Graphs chapter. And his readings of (fragments of) other texts were not interpretive within any recognized school; he was not taking them up as a subject and, as a subject, offering reflections to those living in view of those texts. In both cases, then, we have a distance from subjectivity, albeit a distance structured through different means.

With these things in mind, let us move closer to the method Lévi-Strauss employs in The Raw and the Cooked. But let us first look at the method through the eyes of others.

Conference on Psycho-Ontology

There’s a conference on that topic at the Shalem Center in Jerusalem on 11-15 December of this year, with David Chalmers, Steven Pinker, Lera Bofoditsky and Jesse Prinz headlining. Here’s how the conference bills itself:

Do the operations of the human mind have something to teach us about the fundamental structure of reality? Philosophers such as Hume, Kant, James, Bergson, Husserl, Kuhn, and Goodman have, in different ways, seemed to believe this question should be answered in the affirmative. Yet as disciplines, cognitive science and metaphysics are usually conducted without reference to one another.

“Psycho-ontology” can be defined as the investigation of the relationship between human cognition and features of reality: We do psycho-ontology when we study the way perception, thought, and emotion play a role in helping constitute the world we inhabit. But psycho-ontology can also move in the opposite direction: It can involve studying the fundamental features of reality in order to gain insight into how human cognitive processes work.

It’s a subject of some interest to me, what with my long-standing interest in psychology of ontological cognition.

However, in looking over the program a bit, I suspect it may miss the point as far as object-oriented ontology (OOO) is concerned. The blurb for Chalmers gives it away: “What is the minimal vocabulary that Laplace's demon would need in order to know all truths about the world?” That’s not what OOO is about nor is it quite what I’m about. For my part, I fear that the notion of a fixed vocabulary is somehow adequate to all truths is somewhere between deeply problematic and hopeless one. But the broader point is simply that Chalmers seems concerned about enumerating the kinds of things in the world, which is what ontology seems to mean for this conference.

Look See: Photo Jazz

“The more you look, the more you see.” That phrase has been going through my mind recently, and in two contexts: 1) describing cartoons, 2) photography. The meaning is obvious enough, yet bears investigation. Since I’ve already addressed the first activity, I want to concentrate on the second in this post.

Get the Shot

Consider this photograph:

There’s a tree in the foreground. Or rather, there’s a part of a tree, since we can’t see the whole (above ground portion of the) tree. In the background we see the blurred outlines of parts of some buildings. One of which, the Empire State Building, is quite famous. I don’t know exactly where I was standing when I took that photo – though I could probably return to within, say, 50 yards of the spot. I took it sometime after 7AM last Saturday (9 April 2011), and the photograph itself is time-stamped, though the stamp is not very accurate (as I had to set the timer at some point, and I did so manually).

So, the photograph represents real objects as they appeared at a certain time and place. But that scene, as such, has no existence other than in the images I, or someone else, constructs from data taken out of the camera. In saying this I’m not setting the stage for some deep metaphysical argument about appearance and reality, though one could certainly go in that direction. I’m simply laying out the practical facts of photography.

I took a number of shots while I was standing more or less in the same location. I didn’t think about any one of them for more than a few seconds.

Let’s look at some numbers: Between 7:01:34 AM and 7:05:33 AM I took 16 shots. That’s 16239 shots in 239 seconds, or one shot every 15 seconds. Of course I didn’t take the shots at regular intervals, and I’m sure I was walking during that time. In fact, I was walking rapidly toward the bank of the Hudson so that I could get some shots of the cruise ship that had just passed in front of my location. Which meant that I was in conflict between the need to get to the river walk and the desire to take photos along the way. The time pressure was very much like that of jazz improvisation, and so with a similar mental process: lots of quick decisions made on an intuitive basis.

No time for deliberation, just get the shot. And the next one. And the next.