December 31, 2009

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

Duplicity
It'd be a spoiler to simply to count the number of layers of trickery here; and it's romantic; what more could you want? (Recommended by Kate Nepveu.)
Burrowers
Creepy, grim little western horror movie. The ecology almost makes sense even. (No purchase link just because Powell's doesn't seem to sell it.)
The story of Korolev, the Soviet space program, and of course the eponymous heroine, the first terrestrial creature in space. I kept muttering "The dog dies at the end", but by the end it mattered to me that the dog died.
Seamus Cooper, The Mall of Cthulhu
Yes, it's about an ancient alien squid-god trying to destroy Life As We Know It via a shopping mall in suburban New England, with all the usual indescribable horrors, and lots of joking references to previous works in the genre ("Ms. Harker"!). But also: a convincingly unidealized yet affecting friendship. — Apparently there will be a sequel; I will read it very eagerly.
John Layman and Rob Guillory, Chew: Taster's Choice
Unquestionably the finest, and grossest, detective story about food and black-market poultry ever, at least among those executed as comic books. (Subsequently: 2, 3--8.)
Susan Hough, Predicting the Unpredictable: The Tumultuous Science of Earthquake Prediction
Philip Kitcher, In Mendel's Mirror: Philosophical Reflections on Biology
Collection of Kitcher's papers about the philosophy of biology and related issues, mostly tied (as the subtitle suggests) to genetics. The most interesting paper for me was "Developmental Decomposition and the Future of Human Behavioral Ecology" [JSTOR], about what'd be involved in doing something like evolutionary psychology properly. (I should warn readers of that chapter — Kitcher doesn't do so properly — that he takes as his case study the explanation of incest avoidance, which leads him into a detailed examination of the situations where incest is not avoided. There are sound reasons for this, but it's not for the squeamish or, I'd imagine, the traumatized.) Those who like this sort of thing will find it to be just the sort of thing they like.
Sarah Graves, The Book of Old Houses
Astonishingly, I have yet to experience series fatigue after eleven books. The little bits of Lovecraftian atmosphere add here are, thankfully, debunked inside the story. (Previously: 1--4, 5, 6--10)
Rosemary Kirstein, The Language of Power
The continuation of what is at once an epic fantasy full of marvels and an inspiring depiction of the life of the mind. The scene with Rowan, Will, and the pair of invisible dragons will linger in my memory, and I found myself pleased and astonished at Kirstein's depiction of the sheer beyond-all-experience strangeness of magic. (There, I think I have avoided spoilers for once.)
My only complaint: where is the rest of the series?!?? I want them now!!!!
A. R. Luria, Cognitive Development: Its Social and Cultural Foundations
Re-read after a lapse of ten years. I still think it's a fascinating and profound, though also flawed, work; the successes now loom larger for me than the flaws, though the latter are very real. (To recapitulate what I've written elsewhere: Uzbekistani peasants in the 1930s had excellent reasons to play dumb when Soviet officials came around asking bizarre and leading questions, especially about foreign countries, or premised on obvious falsehoods.) Two things which now impress me more: first, the stuff about visual illusions and colors; and, second, the demonstration that the subjects could solve more concrete problems which were formally identical to the ones they couldn't, or wouldn't, solve in abstract or contrary-to-fact form.
Hans Reichenbach, The Direction of Time
One of the greatest of the logical positivists takes a whirl at reconciling time-reversible microscopic physics with irreversible macroscopic processes in 1956. I began reading this a long time ago, then bogged down in the last chapter, on quantum statistical mechanics; I took the occasion of a long plane flight to re-read and finish it, and am very glad I did. The discussion of relativity, thermodynamics and ergodic theory is clear and sound, if not — at least now — ground-breaking. (It seems extremely odd that general relativity is so ignored; but perhaps just as well, since cosmology was about to be revolutionized.) One highlight for me was the idea of "branch systems", and using the consistency of arrows of time across nearly-isolated mixing processes (not called that) to construct a more global arrow of time. Even the chapter on quantum effects was more interesting than I though it would be, being mostly concerned with the identity (or lack thereof) of quantum particles through time, though I think the treatment in Teller is superior.
The most fascinating part of the book for me, however, is Reichenbach's efforts to build up a notion of time which has not just an order but a direction from causal relations. (If we pick an axis in space, as he says, it has two equal good orders, say left-to-right or right-to-left; time is not just ordered but directed, past-to-future.) He develops in considerable detail the theme that edges in the causal network of spatio-temporal events can be oriented based on the principle that dependent events become independent conditional on their common causes. This is incredibly close to modern ideas about inferring the structure of causal graphical models (see Spirtes, Glymour and Scheines below; Glymour studied under two of Reichenbach's students, Wesley Salmon and Cynthia Schuster). Sadly, I would almost say tragically, however, Reichenbach makes the crucial mistake of thinking that the same sort of independence can easily happen conditional on common effects, when actually it almost never does. (My marginal note at this point is, I see, "NOOOO!") Arguably, this delayed the development of causal inference for decades.
Reichenbach was drawing on many different areas of physics and mathematics which have all made a lot of progress in the last half-century, so I am a bit uneasy about recommending it unreservedly to non-specialists. (There is a new book I can recommend, unreservedly and even reversibly, to general readers.) But the core ideas are very much right, and it's still an imposing and inspiring piece of work.
ObLinkage 1: Emerson on Reichenbach on time.
ObLinkage 2: Speaking of Bérubé (as I was, parenthetically), Steven Gimbel's "If I Had A Hammer: Why Logical Positivism Better Accounts for the Need for Gender and Cultural Studies" tries to appropriate Reichenbach, and logical positivism more generally, for the forces of political correctness.
L. G. Godfrey, Misspecification Tests in Econometrics: The Lagrange Multiplier Principle and Other Approaches
Lots and lots about checking for whether you have the wrong terms in your parametric (especially linear-and-Gaussian) model. Less fundamental than the approaches of White or Hart, and also better adapted to the background and habits typical of econometricians. (This is no accident.)
(Oh, the Lagrange multiplier principle? Suppose your model imposes some restrictions on the parameters, as compared to some larger model you can embed it in. Imagine estimating your model by doing a constrained maximization of the likelihood in the larger model; how big does the Lagrange multiplier on your constraints have to be? How much are you paying in likelihood, in other words, to enforce the constraint? If your model is true, then for large samples the cost is very small and the Lagrange multiplier tends towards zero.)
Warren Ellis and Paul Duffield, Freakangels, vol. 3
In which we consider various forms of rebuilding.
Peter Spirtes, Clark Glymour and Richard Scheines, Causation, Prediction and Search
Re-read as part of preparing for my lecture on casual discovery. I spent much of the winter of 2000 working my way through the first edition, and wound up completely imprinted on its way of thinking about what causal relationships are, how we should reason about them, and how we can find them from empirical evidence. On causation and prediction it now has an equal in Pearl's book (and I admit the latter looks prettier), but on search, that is, on discovering causal structure, there is still no rival. Their key observation is that even though correlation does not imply causation, correlations must have causal explanations. (This idea goes back to Herbert Simon, and Hans Reichenbach [see above] at least.) So patterns of correlations, among more than just two variables, constrain what causal structures are possible. Sometimes they constrain the causal structure uniquely, in other cases it's only partially identified by the dependencies. And of course there is always the possibility of making a mistake with limited data. But none of this is any different for causal discovery than it is for any other form of statistical inference. The great contribution of this book is showing that causal discovery can be just another learning problem. They have transformed metaphysical misery into ordinary statistical unhappiness.
(I can't resist illustrating, though it's necessarily a bit involved. Take three variables, call then X, Y and Z. We find that there is a correlation between X and Y which we can't make go away, no matter what we control for, and likewise between Y and Z, but not between X and Z. There are four possibilities compatible with this: the causal chain $X \rightarrow Y \rightarrow Z$ ; the opposite causal chain from Z to X; a "fork" where Y the common cause, $X \leftarrow Y \rightarrow Z$ ; and a "collider" or "conjunction" where Y is the common effect, $X \rightarrow Y \leftarrow Z$ . In the first three cases, Y "screens off" X from Z — those variables are independent of each other, conditional on Y. So the absence of conditional independence definitely tells us which way the causal links point. In fact, conditional independence at a collider, while mathematically possible, requires no-margin-for-error adjustment of the parameters, so if we assume that such conspiracies are absent ["faithfulness"], we have conditional dependence if and only if there's a collider, which gives us the direction of causation from correlations. "Orienting" some correlations in this manner induces orientations in others, distinguishing forks and the two kinds of chain. For more, see the aforementioned lecture notes, or indeed this book.)
Disclaimers: All three authors have appointments in the CMU Machine Learning department which I'm also affiliated with, etc., etc. And the MIT Press sent me a free copy for review in 2001. (There is a reason my totem is a sloth, yes?)
Nunzio DeFilippis, Christina Weir, Brian Hurtt and Arthur Dela Cruz, Skinwalker
Starts off as a procedural psycho-killer-in-Indian-country mystery, and then gets... strange.

Posted at December 31, 2009 23:59 | permanent link