Three-Toed Sloth

Books to Read While the Algae Grow in Your Fur, April 2015

Attention conservation notice: I have no taste.

Christian Caryl, Strange Rebels: 1979 and the Birth of the 21st Century

A very nicely written popular history of five movements that either began or reached a peak in 1979: the Iranian Revolution, the Soviet-Afghan War, Deng Xiaoping's economic reforms in China, Margaret Thatacher's government in Britain, and John Paul II's first visit as Pope to Poland, viewed as part of a moral campaign against the Soviet Union. Caryl, quite rightly, views these all as anticipations of trends that have come to shape our world, and look like to keep shaping the 21st century --- the economic rise of China, the collapse of Soviet Communism, neoliberalism, Islamism. (I don't believe he ever uses the word "neoliberal" or its derivatives.) Beyond the coincidence of dates, he also links them through their opposition to what, for most of the 20th century, could have been seen as its dominant trends of secularization and socialism, though he's very careful to note how, e.g., post-revolutionary Iran retained and even amplified many modernization initiatives of the Shah's regime, or how Thatcher left alone much of the British welfare state. (He has a nice passage, which irritatingly I cannot find again, about how it's much easier to be a rugged individualist when you know you can go to a hospital for free if you're sick, and will have a guaranteed pension when you're too old to work.) He is also, mercifully, restrained in claiming causal connections between these events &emdash; the biggest is how much the USSR's military commitments in Afghanistan limited its ability to use force in eastern Europe — or abstract, thematic parallels. I think he's too inclined to give credit to Thatcher's economic policies, but otherwise I have few complaints or quibbles. Recommended.

Sydney Padua, The Thrilling Adventures of Lovelace and Babbage: The (Mostly) True Story of the First Computer

If you enjoy this weblog, it is very likely that you are part of the target audience for an irregular comic, in which Lovelace and Babbage were hived off into a pocket universe in which they actually built the Analytic Engine, and used it to fight crime. This is precisely that comic, with footnotes and a bonus material about how the Engine would have looked and worked. I recommend it very highly.

Michael Ellman, Socialist Planning

A sort of soberer older cousin to Red Plenty; at least in the first edition of 1979, which is what I read. (I am curious to see what revisions Ellman made later, since 1979 was of course when things began to change radically...)

The book primarily attends to the USSR and its satellites in eastern Europe; the secondary focus is on China. (There are occasional discussions of Yugoslavia and Cuba, and mentions of the Communist governments of southeast Asia, but nothing about how they actually ran things; and I don't believe North Korea is ever named at all.) There was a lot of interesting and valuable detail about how the Soviets actually drew up plans and tried to implement them. About China Ellman had to be sketchier, because there was simply so little available information, and because the process was itself more chaotic. (While Ellman was evidently more skeptical about Maoism and the Cultural Revolution than many westerners were in the 1970s, this is one places where I hope he'd like to revise and extend his remarks.) Indeed, from reading Ellman, I find myself doubting that pre-Deng China really had economic plans, as opposed to mere orders...

Ellman's attempts to draw up some sort of assessment of the accomplishments and defects of state socialism tries to do this from both a liberal and a Marxist perspective. I think he wasn't harsh enough on the ways capitalist countries fail liberalism, or on the ways state socialist countries failed socialism. Whether what's come since in the former second world is an improvement from either point of view is a more complex question, which this book is necessarily silent on.

Gene Wolfe, Sword of the Lictor

In which Severian commits another crime of mercy, wanders in the wilderness (much of it consisting of relics of former civilizations), and is offered all the kingdoms of the world if he will but fall down and worship a resurrected two-headed tyrant. Also, a story is told which is somewhere between the myth of Romulus and Remus, and The Jungle Book.

I wish I understood how exactly Wolfe manages to convey the sense that he understands all the mysteries he is hinting at, without actually explaining much of anything. (Previously: 1, 2; subsequently: 4)

Milovan Djilas, The New Class: An Analysis of the Communist System

Djilas was probably the highest-ranking member of any ruling Communist party to become an anti-Communist. This was his 1957 attempt at an explanation of what Communist governments, like the one he helped found in Yugoslavia, were actually up to. This analysis was plainly very strongly influenced by Marxism, because it's all about class struggle and the over-riding historical imperative of enhancing production. Dividing through for some of that, Djilas's big idea is that Communism amounted to collective ownership, not by the whole people, but by a "new class" of economic managers and party functionaries. This new class pursued rapid industrialization partly so they would have more to exploit, but even more so as to not be utterly overwhelmed by the advanced capitalist countries. Having a superior position to other classes within the countries they ruled, they naturally used it in self-interested ways; thus class conflict, far from being eliminated, persists. Djilas explains Communist dictatorship, suppression of freedom of thought and other civil liberties, etc., as the new class's means of maintaining its firm position of collective ownership against possible threats, not so much from reactionaries as from other classes. (Cf. the much later joke about Brezhnev's mother.) I have to confess that I cannot follow his explanation of why the new class's relationship relations of production are incompatible with democracy within the Party, or even with "formal" democracy for the country as a whole. (After all, capitalist and even slave-owning societies have been compatible with republicanism and democracy for privileged classes.) While recognizing the diminution of the intensity of repression after Stalin, Djilas emphasized that this involved no change in principle or actual entrenchment of rights the state and party were bound to respect.

As I said, this strikes me as not just more-or-less Marxist but an amplification of the Trotskyist view of what went wrong in the USSR --- though without Trotsky's need to justify the 1917 Revolution and his own actions in helping build the Soviet state. That is, Djilas doesn't regard the new class as some sort of bureaucratic degeneration of a workers' state, still less a reactionary restoration of capitalism, but rather as the logical end-point of the Communist trajectory.

Wisely, Djilas offered no concrete forecasts as to what would happen to the Communist governments. I don't feel competent to say whether subsequent events were compatible with his theory.

There was a curious after-life to Djilas's theory of the "new class", since it was picked up by American neo-conservatives and used as a club in the culture wars. Their theory was (I am not making this up) that a tendency to social and cultural liberalism on the part of teachers, journalists and entertainers is just like the apparatchiks' complete control of the economic resources and repressive force of totalitarian states. There is thus a (thin, twisty, strained) line of intellectual descent from Djilas to clowns like Glenn Beck; it would be grimly amusing to read a full history of this some day.

Gareth Hinds, MacBeth

A graphic-novel adaptation, aimed at younger readers. The selections from the text, and minor modernizations to vocabulary, are all well-chosen. More important, the drawing is excellent, and actually really amplifies the text; I doubt I will ever see Lady MacBeth, the witches, or Banquo other than this way. (Hinds's MacBeth will however compete in my memory with Toshiro Mifune.)

Disclaimer: Hinds is married to a friend of my brother's. This is how I came to look at his book, but has no (conscious) bearing on my review.

Ian Tregillis, The Mechanical

Mind candy: a sort of alternate-historical fantasy, in which Huygens, by evidently stealing ideas from Newton invented alchemical-mechanical golems (called "clakkers"), leading to the Dutch taking over all of the world except for New France, home of the Papacy and French monarchy in exile. Of course, clakkers turn out to be conscious, to experience excruciating pain when they receive orders, and to have some mysterious means of liberation... (There is a lot about Spinoza, which will probably become clearer in the inevitable sequels.) It's catnip for those who have spent far too much time reading about 17th-century science and philosophy, especially in the United Provinces, while simultaneously liking fantasy.

Tregillis's self-presentation.

Marie Brennan, Voyage of the Basilisk

Mind candy. I enjoyed it, but I suspect it's only for those who have read previous installments in Lady Trent's adventures.

David Brion Davis, Inhuman Bondage: The Rise and Fall of Slavery in the New World

Popular history by a respected historian. (He says at the beginning that it began as notes for a short course for high school teachers, which strikes me as an excellent thing.) While slavery in the United States occupies most of the book, Davis is very good at setting that in the context of slavery throughout the Americas (as the subtitle indicates), and indeed the broader historical context of slavery in Europe, southwest Asia and Africa. What becomes depressingly clear from reading this (if it wasn't already) is just how utterly central slavery was to the formation of the modern world economy and to the European colonization of the Americas, and just how monstrous an institution it was. I can't decide if that makes the first word of the title well-chosen, or if it shouldn't rather force us to define down our notion of what acting like a human being means. (One must imagine Simon Legree saying "Am I not a man and a brother?") That abolitionism became a serious movement anywhere, let alone one which was able to succeed in some country through the force of mere persuasion, is almost as astonishingly hopeful as the history of slavery is depressing. Davis's narrative of the slave power within the US, culminating in enormously destructive treason in defense of slavery (*), is exemplary, as is the treatment of Lincoln and the failure of reconstruction.

*: I cannot now recall where I learned this apt phrase; Davis does not, I believe, use it.

Randal Douc, Eric Moulines and David S. Stoffer, Nonlinear Time Series: Theory, Methods, and Applications with R Examples [R code, errata, data]

This is a thorough, but introductory, treatment of the statistical theory of parametric nonlinear time series models, illustrated with many concrete mathematical and data-analytic examples. The emphasis is however on the theory, I think quite rightly, because the theory is crucial for understanding what methods work and why they do so.

The book consists of three parts, only loosely linked. Part I is on basic time series models and methods: it reduces all of linear-Gaussian model theory to two chapters, including ARMA, time- and frequency- domain methods, and the linear Gaussian state-space model. Chapters 3 and 4 tour various nonlinear models, mostly parametric ones, and illustrate some of the need for non-linearities.

Part II (chapters 5--8) is a short course on the ergodic theory of Markov processes. Like everyone else since the early 1990s, their treatment of this subject is heavily influenced by Meyn and Tweedie's Markov Chains and Stochastic Stability. Douc et al. particularly emphasize uniform and V-geometric ergodicity, that is, conditions under which a Markov process's state distribution converges exponentially fast to an invariant or equilibrium distribution. (The ugly phrase "V-geometric" combines the notion of an exponential or geometric convergence rate with a particular way of measuring distance between distributions, generalizing total variation or $L_1$ distance.) Given rapid convergence of distributions, especially with calculable rates, one can get limit theorems about time averages, both almost-sure ("Birkhoff") limit theorems which, like the law of large numbers, say that time averages converge on expectation values, and central limit theorems giving the Gaussian fluctuations around the limits. These in turn provide the foundations for maximum likelihood estimation of Markov models, and what Douc et al., following Cox (sec. 3.2), call "observation-driven" models, a.k.a. chains with complete connections or stochastic automata. Many of the specific models from Part I reappear in this part as examples, but I believe one could read Part II without ever looking at Part I.

Part III is about state-space or hidden Markov models, where there is a latent or hidden state that evolves according to a Markov process, and we observe a noisy, generally nonlinear, function of the state. The special case where the state evolves linearly, the observation is linear in the state, and all noise terms are Gaussian allows for an exact treatment, given in chapter 2. The other special case where the state and the observation are both discrete also allows for exact treatment, the EM algorithm (as originally invented by Baum and Welch). The key problems for state-space models is that of estimating the current state given all observations up to the present, called "filtering", and estimating the sequence of states over some time interval given observations over a (possibly longer) interval, called "smoothing". If the model parameters are known, then the solution to both problems is given formally by Bayes's rule (cf.), but of course it cannot actually be calculated, so one must approximate.

Chapters 10 and 11 explain the "particle" approach to filtering and smoothing. The particle filter is easier to explain than the particle smoother, so I'll stick with the former. Start by randomly drawing $N$ "particles" in the hidden state space according to the initial state distribution, call these $X_0^1, X_0^2, \ldots X_0^N$. (The subscript indicates time, the super-script indexes particles.) Then have each particle evolve its state independently, according to the Markov process for the states, so $p(\tilde{X}_1^i|X_0^i) = q(X_0^i,\tilde{X}_1^i)$ for the fixed Markov kernel $q$ describing the state evolution. (Remember, we're assuming the parameters are known for the moment.) Now take the first observation $Y_1$. It will have some conditional probability (or probability density) given each particle's state, $p(Y_1| \tilde{X}_1^i) = r(\tilde{X}_1^i, Y_1)$. Resample the particles with probabilities proportional to the $r(\tilde{X}_1^i, Y_1)$, so that we get $N$ particles $X_1^1, X_1^2, \ldots X_1^N$. It is not hard to convince oneself that (as $N\rightarrow\infty$) the distribution of $X_1$ particles converges on the actual distribution $p(X_1|Y_1)$. Now evolve the $X_1$ particles to get $\tilde{X}_2$ and repeat the cycle with the next observation, $Y_2$. Moreover, by throwing enough particles at the problem, we get a consistent approximation to $p(X_t|Y_1, \ldots Y_t)$, and this approximation can even be kept up over time.

Chapter 12 extends particle filtering and smoothing to likelihood-based inference, i.e., to approximating, $p(Y_1, \ldots Y_n; \theta)$, where the parameter $\theta$ now influences both the state evolution (above, $q$) and the observation mechanism (above, $r$). Given a decent approximation to the likelihood, one can (with some care) maximize it, or one can dilute its evidence about $\theta$ with a prior and do Bayesian inference. Chapter 13 provides a very nice information-theoretic treatment of the consistency and asymptotic efficiency of maximum likelihood, without presuming that the model is well-specified. The consistency of Bayesian inference is not touched on, which may be just as well because it's a much harder problem.

The mathematical exercises are good; I confess I didn't try the computational ones. I would be very happy to use this book for a graduate course in time series theory, emphasizing Parts II and III, and probably combined with Fan and Yao for non-parametric models, and/or Gourieroux and Monfort for simulation-based inference of scientific models.

Disclaimer: I know Prof. Stoffer slightly, which is, I presume, why the publisher sent me an unsolicited review-copy of this book last year.

Books to Read While the Algae Grow in Your Fur; Scientifiction and Fantastica; Enigmas of Chance; The Dismal Science; Writing for Antiquity; The Continuing Crises; The Great Transformation; The Commonwealth of Letters; Automata and Calculating Machines; Afghanistan and Central Asia; The Beloved Republic

"Sparse Graph Limits with Applications to Machine Learning" (Week after Next at the Statistics Seminar)

Attention conservation notice: Notice of an upcoming academic talk at Carnegie Mellon. Only of interest if you (1) care about how the mathematics of graph limits intersects with non-parametric network modeling, and (2) will be in Pittsburgh week after next.

Jennifer Chayes, "Sparse Graph Limits with Applications to Machine Learning"

Abstract: We introduce and develop a theory of limits for sequences of sparse graphs based on $L^p$ graphons, which generalizes existing theories of graph limits, and in particular includes graphs with power-law degree distributions. We then apply these results to nonparametric stochastic block models, which are used by statisticians to analyze large networks. We use our sparse graph limit results to derive strong results on estimation of functions characterizing these nonparametric stochastic block models. This talk assumes no prior knowledge of graphons or stochastic block models. The talk represents joint work with Christian Borgs, Henry Cohn, Shirshendu Ganguly, and Yufei Zhao.

Time and place: 4 pm on Monday, 20 April 2015, in Scaife Hall 125

As always, the talk is free and open to the public.

(I'd write something long here about why graph limits are so interesting, but why repeat myself?)

Networks; Enigmas of Chance