## November 30, 2009

### Books to Read While the Algae Grow in Your Fur, November 2009

Jen Van Meter, Christine Norrie and Chynna Clugston-Major, Hopeless Savages
Incredibly sweet and charming; whether it's really punk rock I couldn't say. (I completely forget where I saw this recommended, but thanks to whoever it was.) — Sequels.
Mike Mignola and Christopher Golden, Baltimore, or, the Steadfast Tin Soldier and the Vampire
Stories within stories, framed by the Great War unleashing not the influenza pandemic of 1918, but a vampire-zombie apocalypse. Many, many nods to prior horror fiction (most obviously Dracula, but also "The Masque of the Red Death", etc.), and a lot of folkloric elements used to nicely creepy effect. (But isn't "Mircea" a masculine name?) Mignola's drawings are decorative and atmospheric, but not integral.
Cat Rambo and Jeff VanderMeer, The Surgeon's Tale, and Other Stories
The highlight is the title story, which occupies about half this little book, and breathes new life — you should forgive the expression — into the ancient trope of the Resurrection Gone Awry. Of the rest, Rambo's "The Dead Girl's Wedding March" and "A Key Decides Its Destiny" are the best, followed by VanderMeer's "The Farmer's Cat". About the last story, an extended joke about a Lovecraftian menu, the kindest thing I can say is that the authors must've had fun writing it.
F. T. Marinetti, The Untameables
Not actually recommended, unless you want a violent Futurist words-in-liberty fantasy full of orientalism, racism, and (most poisonously) formulaic decadence. On this evidence, Marinetti was much better at writing manifestoes (and cookbooks) than fiction. — I have had this on my shelf since, so help me, 1994, when I first started reading about Futurism; I should've gotten rid of it long ago.
Jason Aaron, R. M. Guéra, Davide Furnò and Francesco Francavilla, Scalped, vol. 5: High Lonesome
Noir blacker than coal-dust. Earlier installments: 1, 2--4.
Phil and Kaja Foglio, Agatha Heterodyne and the: Golden Trilobite; Voice of the Castle; Chapel of Bones
Vols. 6--8 of Girl Genius; in which the lost heir reclaims the ancestral castle, through the power of Science! (As well as perfecting the coffee-maker.)
Lev Vygotsky, Mind in Society: Development of Higher Psychological Processes
A fairly clear and cohesive statement of Vygotsky's key ideas, which were a species of pre-cognitive Marxist psychology. Here is his concerned with looking at what happens to children's cognitive development when they bring together their practical abilities to manipulate their bodies and tools, with their communicative abilities to use words (and other signs) — specifically their learning to use speech to guide behavior, especially their own behavior. (This is very much about the unity of theory and praxis; but Dewey said similar things, from a background of American pragmatism. [Then again, Marx himself was pretty pragmatist already in 1845.] Vygotsky mentions Dewey once here, without much understanding.) Specifically, Vygotsky claims that speech and discursive thought come to guide behavior through children learning to talk to themselves about what they need to do to solve practical tasks, which they come to from previously learning to talk to others about what to do, or trying to get others to do it for them.
This sets up three big themes of Vygotsky's. First, he thinks that all of the characteristically human ("higher") mental processes originate as social interactions, which we then learn to internalize and carry out independently. The Marxist themes (especially out of Engels) here are obvious; he does not, needless to say, demonstrate his contention, and in any case seems to overlook the point that an organism needs a lot of specialized structure and capacity to engage in those social interactions in the first place, let alone internalize them! But he deserves, I think, considerable credit for raising the problem, and the related one of how we use tools and signs in our environment to extend our own cognition. (Here some of the experiments he reports on what's needed for children to make effective use of memory aids are fascinating; but this approaches stigmergy rather than social labor.) Secondly, he emphasizes, when assessing children's development, that looking at what they can do on their own is just picking out (at best) what they have finished learning. If instead one assesses what they can do "with assistance, collaboratively or in groups", what he calls the "zone of proximal development", one gets a sense of what they are learning and could learn. One might argue, though he doesn't pursue this, that even in adults, activities like scientific investigation never really leave the zone of proximal development, especially not at the highest levels of accomplishment. (Skilled scientists can do their students' homework problems on their own, but not their research.) Thirdly, he is emphatic that if you want to investigate cognitive development, you need to probe the developmental process, and not just its end-products in over-learned habits or polished skills. He suggests that the ideal experiments would actually evoke cognitive development under laboratory conditions, and that his students carried out such experiments; he does not provide enough details to assess such a claim.
The book concludes with some chapters on play, imagination and make-believe, and on the "pre-history" of writing (i.e., things which come before writing in individual development but have some kinship to it).
Despite the way I've written, this isn't actually a book Vygotsky planned and wrote out. It was assembled by its translators out of distinct Russian manuscripts and preliminary translations provided by A. R. Luria, and then considerably revised by the translators. (I presume this is where anarchonisms like "World War I" came from.) How much of the result is really due Vygotsky, or to Vygotsky-and-Luria, and how much to the Americans, I can't say. The latter do however provide an introduction, a brief biographical note and an extensive afterword; all of these are probably most useful to readers previously unacquainted with Vygotsky or his school.
Michael Bérubé, The Left at War
The first half of this is Bérubé arguing with (as he says) the "Manichean Left" over Afghanistan, Iraq, the Balkan wars of the 1990s, and their general orientation and understanding of how the capitalist democracies work, or don't. I find myself in complete agreement with this, including Bérubé's positive vision, and unable to add anything of value. (Read it.)
The second half is an argument about the theory of ideology, the notion of hegemony, and the not-exactly-a-discipline of cultural studies. More specifically, it's a plea to get beyond the dualism of "everything with any shred of mass appeal is a tool of the System" on the one hand, and pretending that fans reading their own meaning into music videos (or whatever) has anything to do with smashing said system, on the other. His plea is for a more nuanced approach to ideology, which recognizes that the leading ideas of any society are never all of a block, that political power always comes from coalitions bringing together many divergent interests and ideas, etc., etc. He is particularly fond of a version of these ideas articulated by Stuart Hall, which seem, to judge by his quotations, quite reasonable but not at all special, unless one is starting out from the most benighted precincts of Marxism (e.g., that old fraud Althusser). Linked to this, Bérubé is quite strenuous about the importance of issues other than economic justice for any left that wants to be serious about making sure that everyone isn't just formally free, but can actually use and enjoy their freedom.
Again, I find it hard to disagree with most of this; I just fail to see what the two halves of the book have to do with each other. The best argument I can reconstruct on his behalf would be something like this: lots of people on the left have drifted, or been pushed, into Manichean positions because it seems to follow from the way they understand ideology. If a better account had been widely disseminated, fewer of them would have pursued that dead end. Some parts of cultural studies had articulated that better account, but they failed to make themselves heard; thus, if only more attention had been paid to debates about how best to make use of Gramsci to understand British politics in the early 1980s, the left would not have backed itself into such a corner in 2001--2003.
I find myself drifting into snark in that last sentence, which is unfair. I agree that the very crude counter-cultural thinking and functionalism which seem very common on the left are Not Helpful. I'm just skeptical that (1) giving more weight to cultural studies would have made this better, and that (2) the particular cultural studies sub-tradition Bérubé points to is really the best available way of thinking about these matters. I have my own favorite candidate, but mostly it's that what he takes from this sub-tradition just don't seem that distinctive, except for its Marxist background. (For instance, compare what Bérubé, following Hall, says about first asking what's right or true about an ideology with what Boudon said in his excellent book on The Analysis of Ideology [= L'origine des idées reçues]. And no, I'm not trying to play "My esoteric French theorist trumps your merely-obscure British theorist"; Boudon is a distinguished sociologist who just happens to have made this the center of his theory of ideology.) Which is not to say that we shouldn't work to develop and disseminate better ideas about these matters!
Bérubé tends to avoid advancing his case directly, but rather to get his point across by discussing some other writer's work, or in some cases some other writer's discussion of a third author, etc. (I suspect this is a professional deformation of literary critics.) This is a manner of writing which drives some people crazy, and one which I find very tiresome in other hands, but he pulls it off. Assuming this won't put you off, I recommend this very strongly if you have any interest at all in progressive politics.
Disclaimer: Bérubé blogs at Crooked Timber, where I've guest-blogged, etc., but we've never met or corresponded, and I have no stake in the success of his book.
Rick Geary, Trotsky: A Graphic Biography
Well-told and well-drawn — though nothing in the rest of the art matches to the level of the hero/monster panels of the opening pages (and cover!).
Sarah Graves, Unhinged; Mallets Aforethought; Tool and Die; Nail Biter; Trap Door
Honestly, I'm a bit surprised series fatigue hasn't set in yet; but I continue to enjoy these. (And Eastport continues to suffer levels of violent death comparable to post-invasion Iraq. The fact that this homicide rate has not yet attracted official attention suggests that the serial killer is Bob Arnold, the police chief, rather than Jake Tiptree or Ellie White.) Previous installments: 1--4, 5; sequel: 11.
Jonathan Israel, A Revolution of the Mind: Radical Enlightenment and the Intellectual Origins of Modern Democracy
The story, or part of the story, of how the outlandish and unprecedented ideology of a network of radical, subversive scribblers became what we all at least pay lip-service to. Really deserves a detailed discussion; I'll just say that there's a lot of fascinating material in here, but also many places where I felt he didn't really prove his point, even or especially when I was very sympathetic to what he was saying.
Stephen Budiansky, The Bloody Shirt: Terror After the Civil War
Once upon a time, the US Army attempted to bring democracy to a backward part of the world which had long been wracked by ethnic conflict. There were some promising beginnings, but the defeated, formerly dominant faction refused to accept that their relative demotion, and engaged in a vicious, well-organized campaign of terrorism, which ultimately proved to be entirely successful. Those who had trusted enough in the power and benevolence of the United States enough to participate in the governments ultimately overthrown by "violence and fraud" (in the words of one of the over-throwers) were lucky to escape with their lives (as many did not). Minimal democratic norms were not re-established for ninety years or more.
This is of course the story of the failed Reconstruction of the South after the civil war, which Budiansky tells by recounting the inter-cut, and occasionally overlapping, lives of a number of individuals on the Reconstruction side of the conflict. One of his more effective tactics is to quote extensively from their letters and journals, as well as from contemporary books and newspapers. Caveat lector: many of these — especially the newspapers — are full of vicious racist bile, as well as the astonishing lies elite white Southerners told to portray themselves as oppressed victims. (This begins with the story of the "bloody shirt" that opens the book.) This stuff was hard for me to stomach, and might be too much for some.
My biggest complaint with the book is that I wish Budiansky had done more to tell the stories of black Americans, the way he did with his white subjects — not that there are none, I hasten to add. I can guess at reasons why it would be harder to find materials (all of them ultimately having to do with the fact that Southern blacks were an oppressed people who emerged from slavery for a few years before being crushed back down to serfdom), but still... That said, Budiansky's story of crushed hopes, futile bravery and murderous hatred is wonderfully written and incredibly depressing. I hope that it fills many American with the sort of patriotic shame which helps us be better.
Luc Devroye and Gabor Lugosi, Combinatorial Methods in Density Estimation
The fundamental theorem of statistics, says Pitman, is the Glivenko-Cantelli theorem: the empirical distribution function $F_n$ of a large sample of independent, identically-distributed random variables comes arbitrarily close to their true distribution function $F$: as $n$ goes to infinity, $\sup_{x}{\left|F_n(x) - F(x)\right|} \rightarrow 0$ almost surely. This means that we can learn any underlying probability distribution to arbitrary accuracy just by collecting enough data.* Unfortunately the empirical distribution function is always discrete, so it doesn't have a density, even if the underlying distribution does. Or, if you like, it has a density, but it's a mixture of Dirac delta functions. (The convergence is in the sense of "weak convergence" or "convergence in distribution".) Density estimation is basically about taking the empirical distribution function and smoothing it so that it has a well-behaved density. The oldest way of doing this is to build a histogram, which gives constant densities to intervals; other methods include fitting function series (Fourier or wavelet expansions) to the data, or using kernels (replacing each of the delta function spikes with a smooth density, say a Gaussian bell-shaped curve). The art here is to pick the manner of smoothing, and the amount of smoothing, so that (1) the convergence promised by Glivenko-Cantelli for the unsmoothed distribution is not just maintained but is (2) strengthened to convergence of the estimated density on the true density, and ideally (3) the latter convergence happens rapidly.
Devroye and Lugosi's book is devoted to establishing conditions under which common density estimators have these three desirable properties (or, more rarely, when they do not). Throughout, they focus on the "total variation" or $L_1$ distance between densities: $d_{TV}(f,g) = \int{|f(x) - g(x)| dx}$. They mention, but generally avoid, other common distances or pseudo-distances such as $L_2$ ( $=\sqrt{\int{|f(x)-g(x)|^2 dx}}$ ), Hellinger distance ( $=\int{{\left(\sqrt{f(x)}-\sqrt{g(x)}\right)}^2 dx}$ ), or relative entropy (Kullback-Leibler divergence, expected log-likelihood ratio, $= \int{f(x)\log{\frac{f(x)}{g(x)}} dx}$ ). The total variation distance has a very natural probabilistic interpretation (the maximum amount by which the estimated probability of any event differs from its true probability), and they can get very nice finite-sample bounds by minimizing it over various classes of possible estimates, so this choice is eminently defensible; it does however cut them off from using a lot of existing theory. (For instance, the optimal coefficients in a Fourier series, from an $L_1$ point of view, are not just the empirical Fourier coefficients, since the latter are $L_2$-optimal.)
Their general goal is to prove finite-sample upper bounds on the $L_1$ error of their density estimates; if these go to zero as $n$, we get (1) and (2) above, and the rate of convergence tells us how close we are to obtaining (3). Their route to this goal is almost always through VC theory, and empirical process theory more generally. As always, this has two parts: one is deviation inequalities (e.g., Hoeffding's) which bound the probability that any one candidate density will look much better in sample than it will look out of sample. The other part is combinatorial arguments that the behavior of an entire space of functions can be approximated by that of a finite number of key functions. Meshed together by a union bound, these give uniform concentration bounds, with rates of convergence depending on the complexity of the combinatorial construction needed to achieve a given degree of approximation (i.e., the VC dimension). Devroye and Lugosi's key theorems bound the error of their density estimates in terms of the VC dimensions of the sets formed by comparing two densities in the class. (Specifically, they are interested in the sets where one estimate is higher than another by a given amount; this is, as they note, extremely similar to the threshold procedure used to apply VC theory to regression problems.) Finite VC dimension for such sets implies convergence to within a constant factor of the best available approximation to the true density. They extend such results to ones where the amount of smoothing is determined by data-set splitting, i.e., dividing the data into a training and a testing set, and picking the degree of smoothing which best generalizes from the training set to the testing set. (They do not consider any other form of cross-validation, which is a shame because they're a lot more common than simple data-splitting, but understandable because they're very ugly to analyze.) They give a lot of attention to kernel density estimates, including bounds for continuous kernels in terms of how hard it is to approximate them by simple step-functions, for which the combinatorics are easy.
Strictly speaking, the book presupposes measure-theoretic probability, but readers uncomfortable with sigma-fields and Radon-Nikodym derivatives could mostly get away with ignoring the former and reading "probability density functions" for the latter. Similarly, the actual combinatorics are either elementary, or can be taken on trust. This book is probably not the best way to first encounter density estimation — I suspect a less theoretical introduction would not only make the ideas clearer, but also make readers want theoretical guidance — but no experience on that score is, strictly, necessary. Neither, really, is prior knowledge of learning theory or VC theory, though again it would probably help. The ideal situation for the book is, I'd guess, a second-year graduate-level course on density estimation (there are many excellent problems), or self-study.
*: Well, we have to pretend the data are IID, but let that slide. Or: assume sufficiently rapid strong mixing and argue, as in Vidyasagar, that VC results then hold with tolerable corrections. Kernel density estimates for stochastic processes are treated at length in Bosq's Nonparametric Statistics for Stochastic Processes: Estimation and Prediction, but the starting point there is ergodic theory, not learning theory.
George Clark, Science and Social Welfare in the Age of Newton
Connections between the scientific revolution, economic development and economic policy (such as it was) in late-17th and early-18th century England, and to a lesser extent France and the Netherlands. Interesting stuff on the connections between the activities of scientists and technological development, including the shrewd observation, contra Marxists claiming that scientific progress was basically directed to solving the capitalists' problems, that there were plenty of lucrative problems where scientists got nowhere, or didn't even try to get anywhere, because it was just not scientifically feasible. Also some interesting material on the early history of statistics. The first edition was published in 1937, and shows both that it was written during the Depression, and that respectable economists had no idea what was going on. (This does not much harm the book.)

Posted at November 30, 2009 23:59 | permanent link