Underwood and Sellers 2015: Beyond narrative we have simulation

It is one thing to use computers to crunch data. It’s something else to use computers to simulate a phenomenon. Simulation is common in many disciplines, including physics, sociology, biology, engineering, and computer graphics (CGI special effects generally involve simulation of the underlying physical phenomena). Could we simulate large-scale literary processes?

In principal, of course. Why not? In practice, not yet. To be sure, I’ve seen the possibility mentioned here and there, and I’ve seen an example or two. But it’s not something many are thinking about, much less doing.

Nonetheless, as I was thinking about How Quickly Do Literary Standards Change? (Underwood and Sellers 2015) I found myself thinking about simulation. The object of such a simulation would be to demonstrate the principle result of that work, as illustrated in this figure:

19C Direction

Each dot, regardless of color or shape, represents the position of a volume of poetry in a one-dimensional abstraction over 3200 dimensional space – though that’s not how Underwood and Sellers explain it (for further remarks see “Drifting in Space” in my post, Underwood and Sellers 2015: Cosmic Background Radiation, an Aesthetic Realm, and the Direction of 19thC Poetic Diction). The trend line indicates that poetry is shifting in that space along a uniform direction over the course of the 19th century. Thus there seems to be a large-scale direction to that literary system. Could we create a simulation that achieves that result through ‘local’ means, without building a telos into the system?

The only way to find out would be to construct such a system. I’m not in a position to do that, but I can offer some remarks about how we might go about doing it.

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I note that this post began as something I figured I could knock out in two or three afternoons. We’ve got a bunch of texts, a bunch of people, and the people choose to read texts, cycle after cycle after cycle. How complicated could it be to make a sketch of that? Pretty complicated.

What follows is no more than a sketch. There’s a bunch of places where I could say more and more places where things need to be said, but I don’t know how to say them. Still, if I can get this far in the course of a week or so, others can certainly take it further. It’s by no means a proof of concept, but it’s enough to convince me that at some time in the future we will be running simulations of large scale literary processes.

I don’t know whether or not I would create such a simulation given a budget and appropriate collaborators. But I’m inclined to think that, if not now, then within the next ten years we’re going to have to attempt something like this, if for no other reason than to see whether or not it can tell us anything at all. The fact is, at some point, simulation is the only way we’re going to get a feel for the dynamics of literary process.

* * * * *

It’s a long way through this post, almost 5000 words. I begin with a quick look at an overall approach to simulating a literary system. Then I add some details, starting with stand-ins (simulations of) texts and people. Next we have processes involving those objects. That’s the basic simulation, but it’s not the end of my post. I have some discussion of things we might do with this system followed with suggestions about extending it. I conclude with a short discussion of the E-word. Continue reading “Underwood and Sellers 2015: Beyond narrative we have simulation”

Numerical vs. analytical modelling


Since its resurgence in the 90s Multi-agent models have been a close companion of evolutionary linguistics (which for me subsumes both the study of the evolution of Language with a capital L as well as language evolution, i.e. evolutionary approaches to language change). I’d probably go as far as saying that the early models, oozing with exciting emergent phenomena, actually helped in sparking this increased interest in the first place! But since multi-agent modelling is more of a ‘tool’ rather than a self-contained discipline, there don’t seem to be any guides on what makes a model ‘good’ or ‘bad’. Even more importantly, models are hardly ever reviewed or discussed on their own merits, but only in the context of specific papers and the specific claims that they are supposed to support.

This lack of discussion about models per se can make it difficult for non-specialist readers to evaluate whether a certain type of model is actually suitable to address the questions at hand, and whether the interpretation of the model’s results actually warrants the conclusions of the paper. At its worst this can render the modelling literature inaccessible to the non-modeller, which is clearly not the point. So I thought I’d share my 2 cents on the topic by scrutinising a few modelling papers and highlight some caveats, and hopefully also to serve as a guide to the aspiring modeller!

Continue reading “Numerical vs. analytical modelling”

Free Online Machine Learning Course


This is a quick post about a free online course on Machine Learning. The course is run by Andrew Ng at Stanford and I thought it would be of interest to those who read this blog as it covers learning algorithms which help us to understand how humans learn things as well as machines.

The course comes in structured chunks which are released a week at a time. It hasn’t started yet as it is in the pre-launch period but you can go on the site, sign up and watch the first week of videos and answer the review questions to get a head start.

It seems that this course is running as a beta version of what online courses could be in the future. If you’re even slightly interested in how machines, and indeed humans, learn I suggest you sign up and take part. You can set the difficulty as basic or advanced and it’s FREE!

Sign up and see the first week of videos here: http://www.ml-class.org/course/class/index

You can also see a lecture series by Andrew Ng on youtube here: http://www.youtube.com/watch?index=1&v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599

Cognitivism and the Critic 2: Symbol Processing

It has long been obvious to me that the so-called cognitive revolution is what happened when computation – both the idea and the digital technology – hit the human sciences. But I’ve seen little reflection of that in the literary cognitivism of the last decade and a half. And that, I fear, is a mistake.

Thus, when I set out to write a long programmatic essay, Literary Morphology: Nine Propositions in a Naturalist Theory of Form, I argued that we think of literary text as a computational form. I submitted the essay and found that both reviewers were puzzled about what I meant by computation. While publication was not conditioned on providing such satisfaction, I did make some efforts to satisfy them, though I’d be surprised if they were completely satisfied by those efforts.

That was a few years ago.

Ever since then I pondered the issue: how do I talk about computation to a literary audience? You see, some of my graduate training was in computational linguistics, so I find it natural to think about language processing as entailing computation. As literature is constituted by language it too must involve computation. But without some background in computational linguistics or artificial intelligence, I’m not sure the notion is much more than a buzzword that’s been trendy for the last few decades – and that’s an awful long time for being trendy.

I’ve already written one post specifically on this issue: Cognitivism for the Critic, in Four & a Parable, where I write abstracts of four texts which, taken together, give a good feel for the computational side of cognitive science. Here’s another crack at it, from a different angle: symbol processing.

Operations on Symbols

I take it that ordinary arithmetic is most people’s ‘default’ case for what computation is. Not only have we all learned it, it’s fundamental to our knowledge, like reading and writing. Whatever we know, think, or intuit about computation is built on our practical knowledge of arithmetic.

As far as I can tell, we think of arithmetic as being about numbers. Numbers are different from words. And they’re different from literary texts. And not merely different. Some of us – many of whom study literature professionally – have learned that numbers and literature are deeply and utterly different to the point of being fundamentally in opposition to one another. From that point of view the notion that literary texts be understood computationally is little short of blasphemy.

Not so. Not quite.

The question of just what numbers are – metaphysically, ontologically – is well beyond the scope of this post. But what they are in arithmetic, that’s simple; they’re symbols. Words too are symbols; and literary texts are constituted of words. In this sense, perhaps superficial, but nonetheless real, the reading of literary texts and making arithmetic calculations are the same thing, operations on symbols. Continue reading “Cognitivism and the Critic 2: Symbol Processing”

Python: Your one stop shop for social science research.

Python is easy to use and pretty solid across platforms.  Drew Conway has written an essential list of python tools for social science researchers.  From running experiments to analysis and modelling, Python can do practically anything you’d ever want, mostly in two or three lines.  Hooray!


Language Evolution and Language Acquisition

The way children learn language sets the adaptive landscape on which languages evolve.  This is acknowledged by many, but there are few connections between models of language acquisition and models of language Evolution (some exceptions include Yang (2002), Yu & Smith (2007) and Chater & Christiansen (2009)).

However, the chasm between the two fields may be getting smaller, as theories are defined as models which are both more interpretable to the more technically-minded Language Evolutionists and extendible into populations and generations.

Also, strangely, models of word learning have been getting simpler over time.  This may reflect a move from attributing language acquisition to specific mechanisms towards a more general cognitive explanation.  I review some older models here, and a recent publication by Fazly et al.

Continue reading “Language Evolution and Language Acquisition”