Three-Toed Sloth

Books to Read While the Algae Grow in Your Fur, December 2022

Attention conservation notice: I have no taste, and no qualifications to opine on anti-discrimination law, early 20th century shock art movements, early 20th century science fiction, or the Renaissance reception of classical mythology. Also, most of my reading this month was done at odd hours and/or while bottle-feeding a baby, so I'm less reliable and more cranky than usual.

Marie Mercat-Bruns, Discrimination at Work: Comparing European, French, and American Law (trans. Elaine Holt)

A French legal academic interviewing distinguished American legal academics about anti-discrimination law and related topics, with her commentary. (The interviews close off around 2011, so Ricci vs. DeStefano is a big subject, and the idea of a Supreme Court case instituting gay marriage nationally is definitely beyond everyone's horizon...) In between the interviews, Mercat-Bruns provides her own analysis, including a lot of discussion of French and EU legislation, regulations and case law. Her accuracy on those topics is (obviously?) not something I can evaluate, but I found it notable that she's usually asking why European law can't be more like American law. (Thus our soft-power conquest of the Old World continues.)

I read this for the inequality class, because I was unhappy repeating "I know nothing about anti-discrimination policy in other countries, sorry" in response to very reasonable questions from students. I now feel entitled to reply "I know hardly anything about how anti-discrimination law works in other countries, but...", which is progress. §

Caroline Tisdall and Angelo Bozzolla, Futurism (1977)

This is older, but it's still a really good book about the Italian Futurists. Indeed I can't think of a better one for a general audience with some background knowledge of modern art. The chapters on Futurist painting and sculpture, on music and performance, on women, and on politics are especially good.

I fell in love with Futurist painting as an undergrad, so like a freak I've read far too much about them; this book is surviving the on-going purge of my library. §

Olaf Stapledon, Star Maker (1937)

I read Last and First Men as a boy, and it warpped my mind forever, but I never attempted any other Stapledon (aside from being left cold by A Last Man in London, both as a child and as a grown-up). This was a mistake I am glad I finally fixed.

Star Maker is a very conscious attempt at creating a truly cosmic modern myth, so the whole two-billion-year saga of humanities in Last and First Men is a passing incident mentioned in a handful of paragraphs. Rather this attempts to embrace the whole life of our universe, and of the other universes which are all the work of the titular Star Maker.

A few stray notes (avoiding spoilers):

• Some philosophical influences are very obvious: Hegel, Spinoza, Leibniz's Monadology. The Hegelianism is pervasive throughout; it leads me to wonder what a Deweyan equivalent work of science-fictional myth would be like. The Spinoza who comes through here is that of the Ethics, in particular (but not just) the "intellectual love of God", the life of the stars (and the way the order and connection of their material bodies is the order and connection of their mental lives, seen under a different aspect), and some of the presentation of eternity in the climactic myth-within-a-myth. That last is also where Leibniz is felt.
• I will be surprised if Stapledon wasn't familiar with Attar's The Conference of the Birds, in which a group of travelers of various species move through a visionary landscape which is also a series of spiritual developments in search of a transcendent being, only to have revealed to them that they collectively are that being. (The true Simurgh is the friends they made along the way, as it were.) Just so here, with the growth of the collective group of seekers. Indeed I'd not be surprised if Attar's seven valleys map, in order, on to the stages of Stapledon's future history. (But see Allen below...)

Reading this now, with half a lifetime of consuming mind candy behind me, I can see just how much it shaped subsequent science fiction, even when that has contented itself with less ambitious and visionary, more all-too-human, projects. There are places where Star Maker is dated (the sequence of stellar evolution, the origin of planets, etc.), but it's still a magnificent venture, and I recommend it highly. §

Don Cameron Allen, Mysteriously Meant: The Rediscovery of Pagan Symbolism and Allegorical Interpretation in the Renaissance (1971, 2020) [Open Access]

For several centuries following the revival of classical learning, the received theory among European scholars and intellectuals was that the classical myths, especially as recounted in great poets like Homer, Virgil, and Ovid, were actually elaborate moral allegories and/or symbolic depictions of physical theories. These ranged from the you-can-kind-of-see-it (Circe turning Odyssesus's men, but not Odysseus himself, into swine \( \simeq \) something about reason resisting temptation to which the appetites succumb) to the excruciatingly flimsy. (I will not attempt to do justice to the elaborate encouragements to fussy virtue which were supposedly encoded in, of all books, Ovid's Metamorphoses.) Of course, the interpreters showed little agreement about exactly what a given myth was allegorizing --- except when one interpreter borrowed from his predecessors. None of the interpreters, moreover, seem to have really faced the question of why great poets would go to such pains to create elaborate allegories for rather trite morals.

Just to add to the confusion, all this went along with also seeing classical mythology as ripped off from, or a literally-demonic parody of, the Biblical Genesis story, and/or distorted memories of various historical events among the pagans (so Zeus was a king of Crete, etc.). As Allen explains, these ideas all had their roots in antiquity --- in writings of later pagans looking back at the myths (with more or less embarrassment), and in writings of the Church Fathers trying to make their own kind of sense of those stories. Medieval Christian practices of interpreting Biblical passages in multiple ways fed into the mix.

All of this was taken extremely seriously, and when Renaissance Europeans learned about classical myths, they learned them with these interpretations. Moreover, this complex of ideas helped shape how Europeans understood literary interpretation in the first place, and how they composed their own literary works. (Allen is especially good on Ariosto, Tasso and Milton.) This persisted, as Allen documents in great detail, for centuries, down through the 1700s where he calls a halt *.

From the modern perspective that began to appear in the 1700s, the idea that the classical myths were composed as elaborate moral or cosmological allegories is, of course, loony tunes. But the sheer distance between the surface story of (say) Aphrodite and Ares getting caught in adultery by Hephaestus and the ways that story was read allegorically over the centuries tells us something about how good people are at extracting meanings from anything **, about how unconstrained those meanings are by the object being interpreted, about how much, and how little, tradition and intellectual communities do to channel interpretation, and about how much of the history of ideas is a history of freaks. (Allen is more polite.) §

*: Stopping around 1750 is actually a bit disappointing to me, because the Romantic era seriously revived the idea that the ancient myths were full of hidden meanings, an idea which has persisted to this day. The Romantic mutation, however, seems to lie in implying that the meaning is personally transformative while being (strategically?) vague about just what it is. (The Renaissance mythographers, by contrast, were ploddingly explicit, and the morals were always very conventional.) It'd be very interesting to know what (say) Novalis had read in earlier mythographers. ^

**: OK, maybe not anything. I have speculated that one reason some stories last for so long is that they have a quality of suggestive ambiguity: they seem like they should mean something important, but it's not obvious what. Our surviving corpus of myths, and of renditions of myths, may have been under selection for this quality. ^

Books to Read While the Algae Grow in Your Fur; Writing for Antiquity; Scientifiction and Fantastica; Teaching: Statistics of Inequality and Discrimination; The Beloved Republic; The Commonwealth of Letters

Your Favorite DSGE Sucks

Attention conservation notice: 1800+ words of academic self-promotion, boosting a paper in which statisticians say mean things about some economists' favored toys. They're not even peer-reviewed mean things (yet). Contains abundant unexplained jargon, and cringe-worthy humor on the level of using a decades-old reference for a title.
Entirely seriously: Daniel is in no way responsible for this post.
Update, December 2022: Irritatingly, there are some small but real bugs, glitching all our numerical results. This is an even stronger reason for you to direct your attention elsewhere. (Details at the end.)

I am very happy that after many years, this preprint is loosed upon the world:

Daniel J. McDonald and CRS, "Empirical Macroeconomics and DSGE Modeling in Statistical Perspective", arxiv:2210.16224

Abstract: Dynamic stochastic general equilibrium (DSGE) models have been an ubiquitous, and controversial, part of macroeconomics for decades. In this paper, we approach DSGEs purely as statstical models. We do this by applying two common model validation checks to the canonical Smets and Wouters 2007 DSGE: (1) we simulate the model and see how well it can be estimated from its own simulation output, and (2) we see how well it can seem to fit nonsense data. We find that (1) even with centuries' worth of data, the model remains poorly estimated, and (2) when we swap series at random, so that (e.g.) what the model gets as the inflation rate is really hours worked, what it gets as hours worked is really investment, etc., the fit is often only slightly impaired, and in a large percentage of cases actually improves (even out of sample). Taken together, these findings cast serious doubt on the meaningfulness of parameter estimates for this DSGE, and on whether this specification represents anything structural about the economy. Constructively, our approaches can be used for model validation by anyone working with macroeconomic time series.

To expand a little: DSGE models are models of macroeconomic aggregate quantities, like levels of unemployment and production in a national economy. As economic models, they're a sort of origin story for where the data comes from. Some people find DSGE-style origin stories completely compelling, others think they reach truly mythic levels of absurdity, with very little in between. While settling that is something I will leave to the professional economists (cough obviously they're absurd myths cough), we can also view them as statistical models, specifically multivariate time series models, and ask about their properties as such.

Now, long enough ago that blogging was still a thing and Daniel was doing his dissertation on statistical learning for time series with Mark Schervish and myself, he convinced us that DSGEs were an interesting and important target for the theory we were working on. One important question within that was trying to figure out just how flexible these models really were. The standard learning-theoretic principle is that the more flexible model classes learn slower than less flexible ones. (If you are willing and able to reproduce really complicated patterns, it's hard for you to distinguish between signal and noise in limited data. There are important qualifications to this idea, but it's a good start.) We thus began by thinking about trying to get the DSGEs to fit random binary noise, because that'd tell us about their Rademacher complexity, but that seemed unlikely to go well. That led to thinking about trying to get the models to fit the original time series, but with the series randomly scrambled, a sort of permutation test of just how flexible the models were.

At some point, one of us had the idea of leaving the internal order of each time series alone, but swapping the labels on the series. If you have a merely-statistical multivariate model, like a vector autoregression, the different variables are so to speak exchangeable --- if you swap series 1 and series 2, you'll get a different coefficient matrix out, but it'll be a permutation of the original. (The parameters will be "covariant" with the permutations.) It'll fit as well as the original order of the variables. But if you have a properly scientific, structural model, each variable will have its own meaning and its own role in the model, and swapping variables around should lead to nonsense, and grossly degraded fits. (Good luck telling the Lotka-Volterra model that hares are predators and lynxes are prey.) There might be a few weird symmetries of some models which leave the fit alone (*), but for the most part, randomly swapping variables around should lead to drastically worse fits, if your models really are structural.

Daniel did some initial trials with the classic "real business cycle" DSGE of Kydland and Prescott (1982), and found, rather astonishingly, that the model fit the swapped data better a large fraction of the time. Exactly how often, and how much better, depended on the details of measuring the fit, but the general result was clear.

The reason we'd gotten in to all this was wanting to apply statistical learning theory to macroeconomic forecasting, to put bounds on how bad the forecasts would be. Inverting those bounds would tell us how much data would be needed to achieve a given level of accuracy. Our results were pretty pessimistic, suggesting that thousands of years of stationary data might be needed. But those bounds were "distribution-free", using just the capacity or flexibility of the model class, and the rate at which new points in the time series become independent of its past. This could be pessimistic about how well this very particular model class can learn to predict this very particular data source.

We therefore turned to another exercise: estimate the model on real data (or take published estimates); simulate increasingly long series from the model; and re-estimate the model on the simulation. That is, bend over backwards to be fair to the model: if it's entirely right about the data-generating process, how well can it predict? how well can it learn the parameters? how much data would it need for accurate prediction? With, again, the Kydland-Prescott model, the answer was... hundreds if not thousands of years worth of data.

Of course, even in the far-off days of 2012, the Kydland-Prescott model was obsolete, so we knew that if we wanted anyone to take this seriously, we'd need to use a more up-to-date model. Also, since this was all numerical, we didn't know if this was a general problem with DSGEs, or just (more) evidence that Prescott and data analysis were a bad combination. So we knew we should look at a more recent, and more widely-endorsed, DSGE model...

Daniel graduated; the workhorse Smets and Wouters (2007) DSGE is a more complicated creature, and needed both a lot of programming time and a lot of computing time to churn through thousands of variable swaps and tens of thousands of fits to simulations. We both got busy with other things. Grants came and (regrettably) went. But what we can tell you now, with great assurance, is that:

1. Even if the Smets-Wouters model was completely correct about the structure of the economy, and it was given access to centuries of stationary data, it would predict very badly, and many "deep" parameters would remain very poorly estimated;
2. Swapping the series around randomly improves the fit a lot of the time, even when the results are substantive nonsense.

The bad news is that even if this model was right, we couldn't hope to actually estimate it; the good news is that the model can't be right, because it fits better when we tell it that consumption is really wages, inflation is really consumption, and output is really inflation.

Series swapping is something we dreamed up, so I'm not surprised we couldn't find anyone doing it. But "let's try out the estimator on simulation output" is, or ought to be, an utterly standard diagnostic, and it too seems to be lacking, despite the immense controversial literature about DSGEs. (Of course, it is an immense literature --- if we've missed precedents for either, please let me know.) We have some thoughts about what might be leading to both forms of bad behavior, which I'll let you read about in the paper, but the main thing to take away, I think, is the fact that this widely-used DSGE works so badly, and the methods. Those methods are, to repeat, "simulate the model to see how well it could be estimated / how well it would predict if it was totally right about how the economy works" and "see whether the model fits better when you swap variables around so you're feeding it nonsense". If you want to say those are too simple to rise to the dignity of "methods", I won't fight you, but I will insist all the more on their importance.

It might be that we just so happened to have tried the only two DSGEs with these pathologies. (It'd be a weird coincidence, but it's possible.) We also don't look at any non-DSGE models, which might be as bad on these scores or even worse. (Maybe time series macroeconometrics is inherently doomed.) But anyone who is curious about how whether their favorite macroeconomic model meets these very basic criteria can check, ideally before they publish and rack up thousands of citations lead the community of inquirers down false trails. Doing so is conceptually simple, if perhaps labor-intensive and painstaking, but that's science.

Update, December 2022: Bugs

After posting the preprint, people helpfully found some bugs in our code. These glitch up all our numerical results. Since this is primarily a paper about our numerical results, this is obviously bad. The preprint needs to be revised after we've fixed our code and re-run everything. I am pretty confident, however, about the general shape of the numbers, because as I said we got the same kind of behavior from the Kydland-Prescott model and (importantly, in this context) off-the-shelf code. Of course, you being less confident in my confidence after this would be entirely sensible. In any event, I'll update this again when we're done with re-running the code and have updated the preprint.

*: E.g., in Hamiltonian mechanics, with generalized positions \( q_1, \ldots q_k \) and corresponding momenta \( p_1, \ldots p_k \) going into the Hamiltonian \( H \), we have \( \frac{dq_i}{dt} = \frac{\partial H}{\partial p_i} \) and \( \frac{dp_i}{dt} = -\frac{\partial H}{\partial q_i} \). A little work shows then that we can exchange the roles of \( q_i \) and \( -p_i \) with the same Hamiltonian. But you can't (in general) swap position variables for each other, or momenta for each other, or \( q_1 \) for \( -p_2 \), or even \( q_i \) for \( p_i \), etc.

The Dismal Science; Enigmas of Chance; Self-Centered

Books to Read While the Algae Grow in Your Fur, October 2022

Attention conservation notice: I have no taste, and no qualifications to opine on public administration, political philosophy, social epistemology, or the aims and methods of sociology. Also, most of my reading this month was done at odd hours and/or while bottle-feeding a baby, so I'm less reliable and more cranky than usual.

T. Kingfisher, What Moves the Dead

Mind candy: a re-telling of Poe's "The Fall of the House of Usher" as (is this really a spoiler?) parasite-porn horror. Amusing, and pleasingly creepy. §

Jeffrey L. Pressman and Aaron Wildavsky, Implementation: How Great Expectations in Washington Are Dashed in Oakland: Or, Why It's Amazing That Federal Programs Work at All, This Being a Saga of the Economic Development Administration as Told by Two Sympathetic Observers Who Seek to Build Morals on a Foundation of Ruined Hopes

I realize this is some sort of classic of the public policy / administration literature, so I am very late to this party, but it's really good. One way to expound this --- not Pressman and Wildavsky's, except once in passing early on --- is by an analogy with computer programming. When legislators (or dictators or executives, whatever) proclaim a policy, they state objectives and resources, and provide a sort of sketch of how they think the resources should be used to achieve the objectives. This is like getting requirements for a program and maybe some vague pseudo-code. The job of the programmer is then to implement, to actually come up with a program that runs. In the course of doing so one may discover all sorts of things about the original specification which will often call for it to be revised. If multiple programmers need to implement different parts of the specification, they will have to coordinate somehow, and may find this hard. If the program has to rely on other programs, let alone on other systems, well, good luck coordinating. §

(Link is to the 3rd edition of 1984, which is in print, though I read the 2nd of 1979, and haven't had a chance to compare the two.)

Nathan Ballingrud, Wounds: Six Stories from the Border of Hell

Horror mind candy; all six stories (the last two are really novellas) share a common mythology. Usually-reliable sources had praised Ballingrud's work, so when I ran across a cheap copy I picked this up. I understand the praise, because these are skillfully written (with an exception I will get to below), but I didn't love it, for some mostly-me reasons:

1. While many of the props are Lovecraftian (ghouls, sanity-destroying artifacts, subterranean English cannibal cults), the underlying metaphysics is much more Christian-heretical --- "Hell" is meant very literally, and human laws and interests and emotions have great significance (if not necessarily validity) in Ballingrud's cosmos-at-large. As I have said before, I have standards for my cosmic horror, and the merely Satanic does not cut it.
2. I think it's fair to say that basically every human emotion is depicted as a snare of Hell, love very much included. In some moods I could go along for such a ride, especially if it were presented with a lot more satirical humor, but as this went on I merely found it unpleasant.
3. Ballingrud's endings here are generally abrupt and weak. ("Skullpocket" is a notable exception.)

So: some real merits, but I will not be seeking out more. §

Olúfémi O. Táíwò, Elite Capture: How the Powerful Took Over Identity Politics (And Everything Else)

(Note: The e in "Olúfémi" should also have a dot accent underneath, but every way I've tried to generate this makes my antiquated blogging software produce gibberish...)

I picked this up because I'd liked one of the essays it was was based on, but wished Táíwò would elaborate on the argument. (I also had hopes of using it in the inequality class.) I was, however, disappointed. The book is no clearer than the essays about key concepts, such as "elite", "elite capture", "rooms", and what non-elite-captured institutions would look like. It's a short book, but there are many historical anecdotes, which are all overly-intricate. (Some of them are inspiring, but the details simply aren't relevant.) Abstruse philosophy-of-language ideas about conversational "common ground" are invoked to explain phenomena which a few pages later are also explained as mere fear-of-the-consequences, without any recognition of the tension. (There is a big difference between actually creating false consciousness, and merely intimidating people into saying things they don't believe.) It was a mistake to expand the essay to this length, at least in this way.

Now, there is a core idea here which I find persuasive, namely that those with existing advantages will tend to use those advantages to play a disproportionate, even dominating role in any situation, undertaking or movement and to steer it to their advantage, unless pretty severely checked by strong, and enforced, institutional constraints. That's Jo Freeman's "tyranny of structurelessness" (cited by Táíwò), as well as Robert Michel's "iron law of oligarchy" (not cited). So far, so convincing.

But let me push a little. Unless one imagines that everyone in a movement is equally influential, it's mathematically necessary that the most influential members, the elite, are disproportionately influential. (Just build the Lorenz curve of influence.) I admit this pretends that "influence" is a one-dimensional numerical variable, but that'll be true of all sorts of proxies for influence, like time other members of the movement spend attending to you. At what point does this disproportionate influence tip over into "elite capture"? If this is a matter of degrees rather than thresholds, how ought one trade off the bad of elite capture against other desiderata, like actually getting anything done? (Imagine every member of a movement of even 1,000 people speaking for just a minute on a decision, and being listened to.)

These are, of course, very old questions of democratic theory. Liberalism has at least evolved some answers, by now boringly familiar: leadership through formal representation, accountability of representatives to members through regular elections, competition between rival factions of would-be leaders, etc. --- in short, the threat of members throwing the bums out will keep the would-be bums in line. These have their own issues (throwing the bums out can be a collective action problem, which must be preceded by collective cognition), but, at least here, Táíwò doesn't seem to even dismiss the liberal-democratic stand-bys as inadequate, not suited to progressive movements, or what-have-you.

I realize this all amounts to wishing Táíwò had written a different book, but I do. §

(On the question of "identity politics", which actually gets comparatively little space in the book, I can't help boggling at a line Táíwò quotes from Barbara Smith, one of the founders of the Combahee River Collective, explaining why they needed to introduce a new kind of politics in the late 1970s: "We, as black women, we actually had a right to create political priorities and agendas and actions and solutions based in our experiences". The reason I boggle is that was a well-developed political theory in the 1970s which stood solidly behind groups organizing politically to articulate and advance agendas based on their common interests, values and ascriptive identities, including allying with other groups likewise pursuing their agendas. That theory was good old fashioned American interest-group pluralism. If the leading advocates of pluralism lacked the imagination to apply it to black women (or black lesbians, or...), that wasn't a fault in the theory. To be fair, leftist political theory at the time was coming from a place where the only legitimate group to advocate for itself was the organized working class...)

T. Kingfisher, The Twisted Ones

Arthur Machen, The House of Souls

Mind candy, seasonal. The Kingfisher novel begins with a middle-aged person traveling from Pittsburgh to North Carolina to clear out a relative's house and storage unit, a scenario I instantly identified with, and from there builds the strangeness and tension very satisfyingly. It's the first Kingfisher I've read, but it certainly won't be the last.

The Twisted Ones is avowedly based on Machen's short story "The White People", collected in House of Souls, so I finally read Machen. (I previously knew of him just as one of Lovecraft's influences, but, well, there were many, of varying quality.) There's a lot of genuinely good creepy stuff in here, but it's also often hard to tell whether, when Machen mentions nameless abominations, he's talking about genuinely indescribable cosmic horrors, or just being prudish about sex.

Spoiler-y inter-textual commentary for The Twisted Ones: I strongly suspect that some aspects of the visit of our hero to the city of the white people are homages to Lovecraft's At the Mountains of Madness: both feature series of murals depicting the history of the city as its population dwindles over the ages, and the city is ultimately taken over by servitors of the original inhabitants, shoggoths for Lovecraft, and von Neumann-esque self-reproducing magical automata for Kingfisher. §

Cailin O'Connor and James Owen Weatherall, The Misinformation Age: How False Beliefs Spread

Popular social science. The hook here is explaining what the hell has gone wrong with our politics / culture / thoughts in general over the last decade or so. What O'Connor and Weatherall actually do is explain, clearly but carefully, a range of models of social learning and social influence, intended to model how the social organization of a scientific community helps, or hinders, that community's pursuit of truth. (They tend to be Bayesians, and so presume that the truth is always an available option, rather than something that needs to be actually discovered; but I have a thing about this.) In later chapters, they consider how these social processes can be manipulated or subverted by interested parties, especially industrial propagandists. (The last part draws on Oreskes and Conway's great Merchants of Doubt, which I will review Any Year Now). Because of the authors' institutional affiliations, this counts as philosophy of science, but you could equally well see it as theoretical sociology (*). This is all skillfully done.

The last chapter gestures at applying the models to explain why our contemporary information environment is so awful, especially online. I say "gestures" because they don't really try to establish any very serious results here. I don't think they ever even try to document that, in aggregate, people are more mis-informed now than in, say, 1980 or 1960. As I've said before, I have a strong suspicion that the difference isn't the quantity of craziness, but its condensation into blobs of shared insanity. (The proverbial "tin-foil hat brigade" has indeed become a brigade.) If that's true, models of network learning would be a natural candidate to explain the development...

While I have gone on at some length about the last chapter, I am inclined to cut it a lot of slack as mere marketing. Two philosophers writing a non-technical account of social learning in networks, even a very clear and engaging account, might lead to a few course adoptions. (I myself would be very happy to use those chapters in a class on social learning or collective cognition, following their verbal explanations with the technicalities.) Claiming to explain "the misinformation age" will move a lot more copies, which I can't begrudge them. And the phenomena they describe are probably part of the story... §

*: I'd say "sociological theory", but that name is pre-empted by a sort of hazing ritual, in which newcomers are initiated into the tribe by means of textual ancestor worship, and the relative strength of different tribal segments is reflected in exactly which ancestors get worshiped.

Daniel Rigney, The Matthew Effect: How Advantage Begets Further Advantage

This is mostly a rather pedestrian review of literature on sources of cumulative advantage in science, the economy, aspects of democratic politics, and education. There are places where the book is clearly trying to be popular social science, but it just doesn't have the spark, or the clear lines of argument. The one exception is actually the first chapter, on how Robert Merton introduced the term "Matthew Effect", and how it fitted into his larger programs in the sociology of science and general sociology.

I'll keep this around to mine for references, but even those will be increasingly antiquated... §

John H. Goldthorpe, Sociology as a Population Science

On the advice of readers, I have spun off my remarks into a separate review (and expanded them to 800-odd words). §

Books to Read While the Algae Grow in Your Fur; The Progressive Forces; Teaching: Statistics of Inequality and Discrimination; Scientifiction and Fantastica; Commit a Social Science; The Collective Use and Evolution of Concepts; Networks; Philosophy; Cthulhiana; Actually, "Dr. Internet" Is the Name of the Monsters' Creator

Books to Read While the Algae Grow in Your Fur, August 2022

Attention conservation notice: I have no qualifications to opine on early 20th century Russian and Mongolian history, or even on crackpots.

Craig Alanson, Columbus Day and SpecOps

Mind-cotton-candy science fiction. I use the phrase "cotton candy" deliberately: it's pure diverting fluff of no substance whatsoever. I appreciated the diversion, but feel no compulsion to read any further in what is evidently a long series. It did, however, inspire me to re-read William Tenn's magnificent "The Liberation of Earth", which deserves to be retained as a precious part of our common cultural heritage. §

Richard Stark, Nobody Runs Forever

Mind candy crime fiction. This is a Parker novel, which is to say coolly detached competence porn set among professional criminals --- with emotional amateurs providing contrast and heaps of Plot. I found it refreshing. §

James Palmer, The Bloody White Baron: The Extraordinary Story of the Russian Nobleman Who Became the Last Khan of Mongolia

The life and times of an orientalist crackpot who rode the Russian Civil War to enacting a reign of terror in Inner Asia checks so many of my boxes that I have avoided reading this for years, lest it disappoint. Far from doing so, it was a treat. The subtitle is a bit inaccurate (as Palmer explains clearly, there was a khan, and he was a Mongol). But the book itself is clear, amused (when appropriate), humane, learned (when appropriate) and lively. §

Constant readers (if I have any left) will notice that this was not a lot of books. This is because I am now engaged in a very time- and attention- consuming project which will occupy me for the foreseeable future. My collaborator in this endeavor requests that I not blog about it, but I am allowed to describe it by linking to an emblematic image. I like to imagine that the satyr is playing the pipes because he and the nymph have learned that it is, paradoxically, actually the only way to get their baby to sleep.

Books to Read While the Algae Grow in Your Fur; Scientifiction and Fantastica; Pleasures of Detection, Portraits of Crime; Afghanistan and Central Asia; Psychoceramica; Writing for Antiquity; The Running-Dogs of Reaction

Books to Read While the Algae Grow in Your Fur, July 2022

Attention conservation notice:: I have no taste, and no qualifications to opine on the Italian Renaissance, political philosophy, intellectual history, or even game theory.

Niccolò Machiavelli, Selected Political Writings: The Prince, Selections from The Discourses, Letter to Vettori (edited and translated by David Wootton)

I have, of course, no qualifications to opine on translations of Machiavelli, but having worked my way through a fair number of versions of The Prince over the years, this is easily the most-readable one I've run across. (Wootton's introduction, in particular, is a remarkable production in its own right --- I'd say more but I don't want to spoil the effect!) It would be easy to treat these works as mere documents, artifacts illustrating a dead past, of merely-historical relevance. This translation makes them feel remarkably like a part of arguments we could be having right now, maybe are having right now.

Admittedly there is a cost to this --- when Wootton has Machiavelli use contemporary expressions like "political mechanism" or "social structure", I for one am curious about what the actual phrasing was. (If it really was "political mechanism", that'd be very interesting for the history of mechanism, so I suspect it wasn't.) But if I truly cared about that, I could consult other translations, or for that matter the original text. And the difficulties of trying to be more word-for-word literal are well-illustrated by Wootton's practice of parenthetically marking every place where Machiavelli used virtù (or one of its derivatives --- on p. 191 alone this has to be translated as, variously, "skill", "effect" and "will-power".

One thing reading this leaves me pondering is how to interpret The Prince: when (if ever?) was he speaking sincerely; when was he being ironic; when was he unmasking hypocrisy by plainly describing what his contemporaries were doing* (in a spirit I might characterize as somewhere between "I learned it from you" and "you say you want results, I'll tell you how to get results"); when was he using coded, "Aesopian" language to talk safely about dangerous matters; and when was he trying to make himself appear useful to dangerous gangsters and blasphemous grifters in the hopes they'd give him a desperately-needed job? (These are not mutually exclusive and I can well imagine him being especially pleased with himself when passages worked in multiple ways at once.)

The Discourses, by contrast, seem much more straightforwardly sincere. (Unless: maybe that's just what he wanted us to think!) But I will just mention two things which intrigued me. (1) I presume it's well-known to scholars, but new to me, that the famous opening to Gibbon's Decline and Fall about the age of the Antonines is clearly ripped off from elaborating on book I, chapter 10 of the Discourses. (Except for the bits in Gibbon about religion, which are from Machiavelli's book I, chapter 11.) (2) Has anyone written a good comparison between Machiavelli and ibn Khaldun, especially their ideas about institutions, personal character, and cycles of political founding, decay and re-formation? It's very interesting to see two inheritors of ancient political philosophy trying to found a generalizing science of politics based on historical examples, and I'm equally intrigued by the similarities and the differences. (Virtù is not how you say 'asabiyya in Italian, and neither is arete, but...)

This concludes this episode of my nattering about books I am not entitled to judge. §

ObLinkage: Previously on Wootton on Machiavelli.

*: Thus on Ferdinand of Aragon, ch. 21 begins "if you think about his deeds, you will find them all noble", but by the end of the paragraph, "exploiting religion, he practiced a pious cruelty, expropriating and expelling from his kingdom the Marranos: an act without parallel and truly despicable" (pp. 67--68).

Alain Bensoussan, Jens Frehse and Phillip Yam, Mean Field Games and Mean Field Type Control Theory

Mean field games are ones where each player's payoff depends on the distribution of states (or actions) across the other players, not on what any particular individual does. There are some interesting mathematical questions which arise when we consider the limit of an infinitely large population. (Each finite-dimensional individual then confronts the results of their joint actions as an alien and infinite-dimensional force.) In particular, the way large-but-finite-population games converge on infinite-population limits is related to some convergence issues in a long-simmering project, so I have been trying to educate myself about this topic. As part of that self-education, I have tried to explain my current understanding of mean field games more fully in another place.

This short book from 2013 is intended as a sort of crash course in mean field games (and the related mean field control problems). It presumes a lot of familiarity with mathematical control theory, partial differential equations and stochastic differential equations, but less with (e.g.) convergence of stochastic processes or even conventional game theory. In common with, it appears, most of the literature, it limits itself to settings where agents' internal states and exterior actions are all continuous, but it does consider both a single homogeneous population of agents, and the setting where agents are separated into a fixed number of discrete types (with the population of each type going to infinity together). It was useful for my purposes, which was giving me some orientation to the literature, but I imagine there must be better introductions now available.

If you are the sort of person who finds this intriguing, the odds are very good that you have access to the electronic version from the publisher, which is honestly probably all you need. §

Don S. Lemons, An Introduction to Stochastic Processes in Physics

This is very much intended as a first book on probability and stochastic processes for physics undergrads, and as such I imagine it'd work pretty successfully. I stopped being a physics undergrad 29 years ago, and will review the book for teachers of this material, not learners. (I.e., I won't explain common jargon.)

Lemons starts with very basic discrete and continuous distributions, spends a lot of time on Gaussians and moment generating functions, including a sketch of using moment generating functions to derive the central limit theorem. He then tries to describe continuous-time Gaussian processes, specifically the Wiener process and the Ornstein-Uhlenbeck processes. The viewpoint is essentially: take a deterministic ordinary differential equation, of the kind we know and love from physics courses, and throw a random-variable term into the right-hand side, i.e., more or less the way Langevin proceeded Back in the Day. (Langevin's key paper is included in translation.)

Lemons does a remarkable job of "solving" such stochastic differential equations by assuming that the solution is a Gaussian process, so all that's needed are the first and second moments as functions of time; getting ODEs for those moments; and solving those ODEs. It is, in short, a heroic attempt to act as though the theory of stochastic processes stopped with Chandrasekhar 1943. (The name "Ito" does not appear anywhere in the text.) Now, in deriving his solutions, Lemons pulls off some tricks which make me think that (unlike some physicists writing about stochastics) he does know Ito calculus, but doesn't mention it explicitly lest he be prosecuted by his less enlightened fellow tribesmen so as to not frighten off the children. I hesitate to say that this is unwise --- I presume that it's worked pedagogically for Lemons --- but what is unwise is not letting the reader know that there is a more advanced, i.e., both more flexible and more internally consistent, theory of SDEs, a theory which is certainly within the ability of physicists to master. (Cf.) In fact, I think that if Lemons had tried to teach Ito calculus to larval physicists, he'd have done a good job, which exaggerates my disappointment.

Over-all, if I had read this when I was in the intended audience, it would probably have done me a lot of good, but now I think my main use for this will be to mine it for examples to use as homework problems the next time I teach SDEs. §

Steven Cassedy, What Do We Mean When We Talk About Meaning?

I have struggled with the expression "meaning of life" for as long as I can remember, because I can't understand how "life" can be something like a message or a sign that means anything (outside of some very special circumstances). Cassedy is similarly puzzled: the way he puts it (I paraphrase a little) is that if someone could say "the meaning of life is X" (not that most people ever fill in X), one should be equally able to say "life means X", and, well, life is not a message or a sign. By a slight extension of this original sense, "meaning" also conveys "intention, purpose", and one could make sense of "the purpose of life is X" or "life is intended to do X", though it raises the question of whose intention or purpose.

What Cassedy does in this book is try to trace the history of how the phrases, and the ideas, of "the meaning of life" and "a meaningful life" became so ubiquitous in English and other languages. The starting point is Greco-Roman and Hebrew antiquity, where, he argues, there is simply no such concept. He then traces its pre-history, through the Christian fathers (especially Augustine) and the early modern period. "The meaning of life", he argues, first emerged in German, in the Romantic period, and spread from there, into English, French and Russian. (He has a convincing-to-me discussion of the German word involved, Sinn, but since my knowledge of German mostly relates to linear algebra and public transit, I am not competent to judge.) The phrase got further popularized in English through translations of the great 19th century Russian authors, especially Tolstoy and Dostoyevsky, who were of course influenced by the German usage. (Again, Cassedy goes over the history and usage of the Russian words translated as "meaning", but I know no Russian at all.)

Finally, he locates the real tipping point in post-war America, in the writings of the immigrant German theologian Paul Tillich, where "meaning" became a way of talking about God without having to affirm, or even explicitly mention, traditional supernatural dogmas --- but also without denying them, either. At the present, he concludes, it is the very slipperiness of "meaning" which makes it so ubiquitous: if people had to spell out exactly what they were trying to say, it would be less effective (and they might realize they don't know themselves what they're saying).

I found this fascinating and drily funny, but then I'm philistine anima-blind reconciled to living in a blind, purposeless universe, the fortuitous product of the concourse of atoms and void, where I get to be one of those safely on shore watching storms at sea lucky enough to not need this particular analgesic. §

Lauro Martines, Power and Imagination: City-States in Renaissance Italy (New York: Knopf, 1979)

This is a learned and gracefully written book which goes into a lot of the details of how Italian city states --- mostly but not exclusively north of Rome --- formed, struggled, were run, and eventually got absorbed (for the most part) into larger polities. I learned a lot about the internal political machinations, especially about institutional devices which, whatever their republican intentions, ended up helping to perpetuate oligarchy. Thus the "power" part.

The "imagination" is the high culture, especially art and humanism. Martines, for his part, sees this as ideology, and ideology in the service of upper class interests. While a lot of that is convincing, there do seem to be two gaps in his argument there. One is that he never grapples with why this art continues to be meaningful to people all over the world, centuries later, in ways which earlier and later art, equally in the service of related upper classes, just isn't. (Cf.) He does, to be fair, raise the parallel issue with humanistic scholarship, and says that the humanists made some "objective" discoveries of lasting value, but doesn't address how that was possible in a basically-ideological enterprise. The other defect, which I suspect is related, is that he doesn't really explain why serving upper class interests in this time and place should have required such an astonishingly large amount of innovation in technique. He's certainly aware of it: his first two pages of illustrations contrast a Florentine painting from the 1270s (basically still Byzantine) with one from 1426 (that might as well be from another world), and he has perceptive things to say about the development of artistic and literary styles. But these two issues --- why there was so much artistic and intellectual innovation, and why we still value the results --- are just not things he really tries to explain.

In the end, Martines gives the impression that he thinks of his subjects, the upper classes of the Italian city states, extraordinary but also horrible, and I can't help think that by the last chapters he was somewhat sick of them, and that in describing the Italian wars that began in 1494 he was (as the saying goes) "rooting for injuries". If so, it's hard not to sympathize. §

(I have not seen the paperback edition [Baltimore: Johns Hopkins University Press, 1988], but I can't find any indication of revisions.)

Fernand Braudel, Out of Italy: Two Centuries of World Domination and Demise (translated by Siân Reynolds from Le Modèle italien [Paris: Éditions Arthaud, 1989], but first published in Italian {Torino: Giulio Einaudi Editore, 1974])

I picked this up because I ran across a cheap copy and had been impressed by my earlier exposure to Braudel. This is wide-ranging and amiable, but I ended it with no clear idea of what Braudel was trying to argue, and very confused by what, exactly, he meant when he referred to something as a historical "problem" --- and he talks about problems incessantly. (And he's weirdly confident about what he knows are exceedingly tenuous estimates of economic conditions.) I half suspect the key to the book is a seemingly throw-away remark in the last chapter that "Everyone thinks for instance that 'France under the Sun King,' Louis XIV, was 'greater' than Francce under de Gaulle, but the 'inferior' France of the 1960s had a population two or three times greater and was many times richer". In conclusion: maybe worth reading if you are studying Braudel himself (or mid-20th-century historiography, etc.). Yes, I fully realize just how presumptuous it is of me to say such a thing. §

Books to Read While the Algae Grow in Your Fur; Writing for Antiquity; Philosophy; Enigmas of Chance; The Dismal Science; Physics

Book to Read While the Algae Grow in Your Fur, June 2022

Attention conservation notice: I have no taste, and no qualifications to opine on the (linked) decay of our infrastructure and our institutions, or to evaluate books on pregnancy (but then neither does that author).

Walter Jon Williams, Lord Quillifer

Mind-candy fantasy, competence-porn division. I very much enjoyed the latest installment in Quillifer's adventures and mis-adventures, but you really need to have read the previous books (1, 2) to get anything out of this. §

Emily Oster, Expecting Better: Why the Conventional Pregnancy Wisdom is Wrong --- and What You Really Need to Know

There are two hooks here. (Neither is that the "conventional pregnancy wisdom" is all wrong.) One is Oster bringing the clarity of decision theory to pregnancy: let the doctors tell us the probabilities of outcomes under various contingencies, then let pregnant women come up with their utilities for those outcomes and decide which risks are worth it. The other hook is that Oster actually understands study design, and pokes at the medical literature on pregnancy and child-bearing to see which bits of it can support any weight. I am much more persuaded by the second part than by the first, if only because I had independently read a bunch of the same studies Oster and came to similar evaluations. The medical literature isn't all on a level with the Journal of Evidence-Based Haruspicy, but a surprisingly large part of it comes shocking close. I'm sure there are real obstacles to doing better, but it wouldn't hurt the medical system to admit how little confidence they ought to have.

As for the decision theory, well, I just defy anyone to actually implement that ideal. To repeat a favorite anecdote from the great Persi Diaconis:

Some years ago I was trying to decide whether or not to move to Harvard from Stanford. I had bored my friends silly with endless discussions. Finally, one of them said, "You're one of our leading decision theorists. Maybe you should make a list of costs and benefits and try to roughly calculate your expected utility." Without thinking, I blurted out, "Come on, Sandy, this is serious."

That said, I did appreciate Oster's efforts at providing actual estimates of various probabilities, however imperfect. §

ObLinkage1: I am sure this will cause all kinds of awkwardness at the farmers' market. I find the criticisms of Oster in that essay unfair, despite agreeing that public policy is needlessly mean and has, in many ways, grown meaner over my lifetime. The flaws of public policy around parenting, pregnancy, etc. are not Oster's fault; they're not even the economists' fault collectively; it seems fine to not go into policy in a book of advice to prospective mothers, even if you think policy is very important.

ObLinkage2: This puts many of Oster's anecdotes about her own mother in a different (and more impressive) light.

NoLinkage: I am vaguely aware that Oster has made herself controversial with ideas about how to respond to the pandemic. I haven't followed that, I have no opinion on it, I don't see how it's relevant (one way or the other) to this book, and I don't intend to learn anything about this matter, if I can help it.

Chris Raschka, Charlie Parker Played Be Bop

I thank Dmitri Tymoczko for bringing this to my attention.

Chris Ferrie and Marco Tomamichel, Blockchain for Babies

I blame Dmitri Tymoczko for bringing this to my attention, and will not dignify it with a purchase link.

Thomas Thwaites, The Toaster Project: Or a Heroic Attempt to Build a Simple Electric Appliance from Scratch

What it says on the label: an art student tries to build a toaster, from raw materials sourced from Great Britain. Whether he succeeds is a matter of interpretation, but many valuable lessons about technology, knowledge, materials, the division of labor in society, and the nature of the built environment are learned along the way. Recommended if you can enjoy, or even just tolerate, wry, self-deprecating, Very British humor. §

Anna Clark, The Poisoned City: Flint's Water and the American Urban Tragedy

I think it's fair to say that this is the standard account of the Flint disaster, and it should be: it's well-written, impassioned, meticulous without being overwhelming, and provides a lot of important context. That said, there are a few points where I want to push back a little on some things Clark seems to imply.

1. In Flint, when ordinary people complained that their water was bad, blamed it for all sorts of mysterious medical complaints, and disbelieved official reassurances, the plain people of Flint were, in fact, right. But when ordinary people complain about MMR or Covid vaccines, blame them for all sorts of mysterious medical complaints, and disbelieve official reassurances, they are very, very wrong. (Anyone taking this as an occasion to send me anti-vax rubbish will be piped to /dev/null.) I don't expect Clark to give us the tools to differentiate between these two cases, in a principled way which could help readers going forward --- she's a journalist, not a prescriptive social epistemologist! But I do wish her writing showed some awareness of this pitfall of celebrating the wisdom of the common folk.
2. Relatedly, "hundreds of protesters bang[ing] on the locked doors of the ornate capitol building, shaking its wood panels" as the legislature tries to go about the ordinary business of democratic self-government (p. 167) --- well, that registers a little differently now, doesn't it?

Let me re-iterate that this is a really good book, which I strongly recommend. §

Books to Read While the Algae Grow in Your Fur; Natural Science of the Human Species; The Beloved Republic; The Continuing Crises; The Great Transformation; Scientifiction and Fantastica

Upcoming Talk: "Matching Random Features"

Attention conservation notice: You have better things to do with an hour of your precious, finite life than staring at a screen while an academic tries to give a hand-wavy summary and advertisement for technical work on abstruse problem you don't care about.

I will be talking on Random-Feature Matching to the One World Approximate Bayesian Computation Seminar at 8:30 am Eastern time (=1:30 pm UK time) on Thursday, 23 June. If you are interested in simulation-based inference but have not (oddly) read my paper, or if you just want to marvel at how bad someone can be at giving a Zoom talk, two years on, please join. (Details on getting access to the Zoom session can be had by following that last link.)

Let me take this opportunity to thank the organizer both for the invitation, and for not insisting on the usual seminar time of 9:30 am UK time.

Self-centered; Enigma of Chance

Course Announcement: "Statistics of Inequality and Discrimination" (36-313)

Attention conservation notice: Advertisement for a course you won't take, at a university you don't attend, in which very human and passionately contentious topics deliberately have all the life sucked from them, leaving only the husk of abstractions and the dry bones of methodology.

In the fall I will, again, be teaching my class on inequality

36-313, Statistics of Inequality and Discrimination
9 units
Time and place: Tuesdays and Thursdays, 1:25 -- 2:45 pm, in Wean Hall (WEH) 6403 (tentatively)
Description: Many social questions about inequality, injustice and unfairness are, in part, questions about evidence, data, and statistics. This class lays out the statistical methods which let us answer questions like Does this employer discriminate against members of that group?, Is this standardized test biased against that group?, Is this decision-making algorithm biased, and what does that even mean? and Did this policy which was supposed to reduce this inequality actually help? We will also look at inequality within groups, and at different ideas about how to explain inequalities between groups. The class will interweave discussion of concrete social issues with the relevant statistical concepts.
Prerequisites: 36-202 ("Methods for Statistics and Data Science") (and so also 36-200, "Reasoning with Data"), or similar with permission of the instructor

Last year was the first time I got to teach it, and it was a mixed experience. The students who stuck with it were, gratifyingly, uniformly very happy with it (and I am pretty sure they learned a lot!). But it also had the biggest "melt" of any class I've taught, with fully half of those who initially signed up for it eventually dropping it. The most consistent reason why --- at least, the one they felt comfortable telling me! --- was that they were expecting something with a lot more arguing about politics, and a lot less math and data analysis. I have taken this feedback to heart, and decided to do even more math and data analysis.

Tentative topic schedule

Slightly more than one week per. A more detailed listing, with related readings, can be found on the class homepage.

1. "Recall": Reminders about probability and statistics: populations, distribution within a population, distribution functions, joint and conditional probability; samples and inference from samples.
2. Income and wealth inequality: What does the distribution of income and wealth look like within a population? How do we describe population distributions, especially when there is an extreme range of values (a big difference between the rich and poor)? Where does the idea of "the 1%" wealthy elite come from? How has income inequality changed over recent decades?
   Statistical tools: measures of central tendency (median, mode, mean), of dispersion, and of skew; measures of dispersion (standard deviation etc.); measures of concentration and inequality (ratios between percentiles, the Lorenz curve, Gini coefficient); the concept of "heavy tails" (the largest values being orders of magnitude larger than typical values); log-normal and power law distributions; fitting distributions to existing data; positive feedback, multiplicative growth and "cumulative advantage" processes.
3. Speed-run through social and economic stratification: Reminders (?) about social concepts: ascriptive and attained social statuses, and qualitative/categorical vs. more-or-less dimensions of differentiation. Important forms of differentiation, including (but not necessarily limited to): sex, gender, income, wealth, consumption, caste, race, ethnicity, citizenship, class, order, education. The legal notion of "protected categories".
4. Income disparities: How does income (and wealth) differ across groups? How do we compare average or typical values? How do we compare entire distributions? How have income inequalities by race and sex changed over recent decades?
   Statistical tools: permutation tests for differences in mean (and other measures of the average); two-sample tests for differences in distribution; bootstrapping; inverting tests to find the range of differences compatible with the data; the "analysis of variance" method of comparing populations; the "relative distribution" method of comparing populations
5. Explaining, or explaining away, inequality: To what extent can differences in outcomes between groups be explained by differences in their attributes (e.g., explaining differences in incomes by differences in marketable skills)? How should we go about making such adjustments? Is it appropriate to treat discrimination as the "residual" left unexplained? When does adjusting or controlling for a variable contribute to an explanation, and when is it "explaining away" discrimination? What would it mean to control for race, sex or gender?
   Statistical tools: Observational causal inference; using regression to "control for" multiple variables at once, with both linear models and nonparametrically (by means of matching or nearest-neighbors); using graphical models to represent causal relations between variables; how to use graphical models to decide what should and what should not be controlled for; the causal model implicit in decisions about controls.
6. Detecting discrimination in hiring, admissions, etc.: Do employers discriminate in hiring (or schools in admission, etc.)? How can we tell? When are differences in hiring rates evidence for discrimination? How do statistical perspectives on this question line up with legal criteria for "disparate treatment" and "disparate impact"?
   Statistical tools: tests for differences in proportions or probabilities; adjusting for applicant characteristics (again)
7. Inequalities in health, disease and mortality: Quantifying differences in the incidence of diseases, in death rates, and in life expectancy. The "deaths of despair" controversy.
   Statistical tools: differences in proportions and probabilities again; survival analysis and survival curves; some of the elements of demography.
8. Mobility and Transmission of Inequality: What does it mean to talk about social mobility? Conversely, what doe it mean to say inequality can be transmitted from one generation to the next? What are the mechanisms this happens through? What are the large-scale patterns about mobility and transmission, over the last few decades?
   Statistical tools: correlations; conditional probability modeling; Markov models.
9. Measuring segregation: What do we mean by "segregation"? Segregation in law ("de jure") and segregration in fact ("de facto"). Different ways of measuring de facto segregation. Trends in de facto racial segregation since the end of de jure racial segregation. Why different measures of segregation give different results. Segregation by income. Segregation by political partisanship. Consequences of segregation. Inter-generational transmission again.
   Statistical tools: Standard measures of segregation; more recent measures of segregation based on information theory; spatial correlation; how do we make adjustments for changing distributions?
10. Algorithmic bias and/or fairness: Can predictive or decision-making algorithms be biased? What would that even mean? Do algorithms trained on existing data necessarily inherit the biases of the world? What notions of fairness or unbiased can we actually implement for algorithms? What trade-offs are involved in enforcing different notions of fairness? Are "risk-prediction instruments" fair?
    Statistical tools: Methods for evaluating the accuracy of predictions; differential error rates across groups; decision trees; optimization and multi-objective optimization.
11. Standardized tests: Are standardized tests for school admission biased against certain racial groups? What does it mean to measure qualifications, and how would we know whether tests really are measuring qualifications? What does it mean for a measurement to be biased? When do differences across groups indicate biases? (Disparate impact again.) Why correlating outcomes with test scores among admitted students may not make sense. The "compared to what?" question.
    Statistical tools: Predictive validity; differential prediction; "conditioning on a collider"
12. Intelligence tests: Are intelligence tests biased? How do we measure latent attributes? How do we know the latent attributes even exist? What would it mean for there to be such a thing as "general intelligence", that could be measured by tests? What, if anything, do intelligence tests measure? What rising intelligence test results (the Flynn Effect) tell us?
    Statistical tools: correlation between test scores; factor models as an explanation of correlations; estimating factor values from tests; measurement invariance; alternatives to factor models; item response theory
13. Measuring attitude and prejudice: How do we measure people's feelings about different groups? Why do different measures give different results? Do "implicit association tests" measure unconscious biases? What, if anything, do implicit association tests measure?
    Statistical tools: More on measurement; the distinction between reliability and validity; why it's much easier to quantify reliability than validity; approaches to "construct validity".
14. Evaluating inequality-reducing interventions: If we try to do something to reduce inequality, how do we know whether or not it worked? How do we design a good study of an intervention? How do we pool information from multiple studies? What can we do if only bad studies are available? Do implicit bias interventions change behavior? Does having a chief diversity officer increase faculty diversity? What does, in fact, seem to work?
    Statistical tools: Design and analysis of studies; experimental design: selecting measurements of outcomes, and the importance of randomized studies; meta-analytic methods for combining information
15. Policing and crime: When do differences in traffic stops, arrests, or police-caused deaths indicate discrimination? How do we know how many traffic stops, arrests and police-caused deaths there are to begin with? Does "profiling" or "statistical discrimination" make sense for the police, whether or not it's socially desirable? How can the same group be simultaneously over- and under- policed?
    Statistical tools: test for differences in proportions; signal detection theory; adjusting for systematically missing data; self-reinforcing equilibria
16. Self-organizing inequalities and "structural" or "systematic" inequalities: Models of how inequalities can perpetuate themselves even when nobody is biased. Models of how inequalities can appear even when nobody is biased. The Schelling model of spatial segregation as a "paradigm". How relevant are Schelling-type models to actual, present-day inequalities?
    Statistical tools: Agent-based models; models of social learning and game theory.
17. Statistics and its history: The development of statistics in the 19th and early 20th century was intimately tied to the eugenics movement, which was deeply racist and even more deeply classist (but also often anti-sexist). The last part of the course will cover this history, and explain how many of the intellectual tools we have gone over to document, and perhaps to help combat, inequality and discrimination were invented by people who wanted to use them for quite different purposes. The twin learning objectives for this section are for students to grasp something of this history, and to grasp why the "genetic fallacy", of judging ideas by where they come from (their "genesis") is, indeed, foolish and wrong.
    Statistical tools: N/A.
18. How do we know what we do about inequalities? Social data-collection systems and institutions. Measurement again, and measurement as a social process. Difficulties in reducing social reality to data; the case of race in the US census as an example. What systematic data collection leaves out.

Evaluation

There will be one problem set per week; each of these homeworks will involve some combination of (very basic) statistical theory, (possibly less basic) calculations using the theory we've gone over, and analysis of real data sets using the methods discussed in class. There will also be readings for each class session, and a short-answer quiz after each session will combine questions based on lecture content with questions based on the readings.

There will be no exams.

My usual policy is to drop a certain number of homeworks, and a certain number of lecture/reading questions, no questions asked. The number of automatic drops isn't something I'll commit to here and now (similarly, I won't make any promises here about the relative weight of homework vs. lecture-related questions).

Textbook, Lecture Notes

There is, unfortunately, no one textbook which covers the material we'll go over at the required level. You will, instead, get very detailed lecture notes after each lecture. There will also be a lot of readings from various books and articles. (I will not agree with every reading I assign.)

Teaching: Statistics of Inequality and Discrimination; Corrupting the Young; Enigmas of Chance; Commit a Social Science

Books to Read While the Algae Grow in Your Fur, May 2022

Attention conservation notice: I have no taste, and no qualifications to opine on the archaeology of the Southwest, the pre-history of diversity training, or trends in American economic inequality.

Walter Jon Williams, Metropolitan and City on Fire

These are two novels Williams wrote in the '90s about intrigue and machinations in a world-spanning city, where the geomantic forces generated by covering the planet in concrete, metal and plastic are carefully harvested and metered, and our heroine longs to smash it all. They're some of the best stuff Williams has ever done, which is saying a lot. Strictly speaking, they are fantasy, even "urban fantasy", but very much in the manner of well-thought-through science fiction.

As a character, Aiah has something in common with Williams's Caroline Sula and even (when it comes to learning to lie and manipulate) Dagmar Shaw, but she is her own, vivid and plausible, person.

I last read these in 1999; I re-read them because Williams recently said that the long, long delayed third volume will finally happen. I am very eager. §

John Kantner, Ancient Puebloan Southwest

This is a well-written, semi-popular account of the archaeology of the American Southwest, focusing on the period from the rise of Chaco Canyon to the early years of Spanish rule. The writing is mostly smooth and expository (*), and I learned a lot of fascinating-to-me details from it. Kantner does do the usual archaeologist thing of making very confident-sounding assertions about social organization which he must know are far more conjectural than he makes them sound. (**) But this is par for the archaeological course. If you have a non-expert interest in the subject, and can handle the lack of a definite article in the title, this is a worthwhile book. I would read a second edition. §

*: Though inconsistently so; he explains "inference", but not "dendrochronology" or "palynological". --- On a different plane, Kantner persistently writes "inequity" (an evaluative, qualitative judgment) when he should write "inequality" (a descriptive and quantitative comparison). Unless, that is, he regards every inequality as inequitable, which is his right but not something to be just assumed... ^

**: To paraphrase, he does things like assert that a division of such-and-such a community into "moieties" can be inferred from the construction of a wall dividing a building in two. Or, again, there are assertions that a one community couldn't have politically dominated another because the latter kept making pots in its old way. This sort of thing just shows a failure of imagination. (I used to part-own a house that had been built for one large family around 1900, and later split with a wall down the middle. While Pittsburgh has some peculiarities it does not divide duplex residents into two endogamous groups, so that I am expected to regard all North-Halfers as some kind of kin.) It also, I think, betrays a failure to check this sort of inference against cases where much more is known about society and politics from written records. ^

Elisabeth Lasch-Quinn, Race Experts: How Racial Etiquette, Sensitivity Training, and New Age Therapy Hijacked the Civil Rights Revolution (2001)

This is, obviously (?), a work of cultural criticism, but it's done with the tools of a serious historian who is trying to excavate where things like diversity training came from, and why they both emerged when and where they did, and how they survived that initial context. To oversimplify and exaggerate: the late 1960s/early 1970s were a weird time, when plenty of people on the fringes of psychology felt entitled to make stuff up because it sounded good and vibed with their politics, with very little reality-testing. Add the "triumph of the therapeutic" and of self-esteem, plus corporate concerns to ward off liability by claiming to do something (however ineffective), plus the continuing attraction of racialist thinking under another guise (*), and we get a mess.

There are, equally obviously, some political and ethical commitments animating this book, but they are transparent, and honestly ones I have a lot of sympathy for, even if I suspect she and I would often disagree on concrete policies. I would pay very good money to read Lasch-Quinn writing seriously about 2020; unfortunately this is not the kind of work which can be done that quickly, and anyway she seems to have moved on to other topics. §

*: Lasch-Quinn does not use phrases like "reinscribing an essentialized racial binary", but they would actually fit her argument.

Elizabeth Kolbert, Under a White Sky: The Nature of the Future

A collection of journalistic essays. The formula each time is Kolbert visiting some place --- an electrified anti-invasive fish barrier on the reverse-flowing Chicago river, the mouth of the Mississippi, a cave in the Nevada desert where a unique native fish species is being quixotically maintained, the Great Barrier Reef, a carbon-sequestration site in Iceland --- where she can see (as the saying went) "the Earth as transformed by human action", and talk to the workers. Often enough, the reason these efforts are necessary are dealing with side-effects of earlier efforts at control, which Kolbert presents as ironic but unavoidable; we've gone too far down this path to turn back now. (Though she doesn't say so, we'd gone too far when Gilgamesh was king in Uruk.) Stewart Brand is quoted, aptly; so is John McPhee's classic The Control of Nature.

Speaking of McPhee: this is one of the most New Yorker-y books I've ever read. It has all the characteristic virtues: easy prose, lively (but not startling) intelligence, an eye for detail expressed through original (but not outlandish) metaphors, judiciously-chosen historical anecedotes, sympathetic if amused pen-portraits of interesting characters; you come away feeling like you've understood something, without having been taxed. I realize my description may sound a bit barbed, because it is. On the one hand, I want to acknowledge how hard such writing is to pull off --- being scholarly and exhaustive actually takes much less effort and skill --- and record my admiration, indeed my envy. But on the other hand, the reader puts the book down feeling like they've understood something, without necessarily having done so. On the topics where I know enough to think I could judge (mostly having to do with climatology), Kolbert seems accurate, which increases my confidence in the rest of her work. But somehow I was more conscious of the art, and more suspicious of its effects, than I normally am.

This was the first book by Kolbert I've read; I will certainly read more. §

Gino C. Segrè and John D. Stack, Unearthing Fermi's Geophysics

This is a perfectly nice little introduction to geophysics, suitable for third- or fourth- year physics majors. (That is, you are expected to have forgotten undergraduate classical mechanics, thermo, and E& M; fluid and continuum mechanics are introduced here as needed.) The hook here is that this is based on the notes for such a course which Fermi taught, and which Segrè discovered in the archives. Of course it has been vastly fleshed out (the authors reproduce selected pages from Fermi's notes, and "telegraphic" hardly does it justice), and there are a few places where it's been brought up to date, primarily by comparing Fermi's numerical figures with modern measurements. There is thus no discussion of continental drift or of climate change, to name just two important topics. Still, I enjoyed the gimmick, and it's a nice introduction to interesting and important topics in physics. I would imagine that it would suffer, in terms of classroom use or even serious self-study, from lacking exercises. (It would be very interesting to see Fermi's idea of good homework problems!) §

Rebecca M. Blank, Changing Inequality

This is essentially a huge exercise in comparing the American Community Survey's economic statistics in 1979 with those in 2007. The headline is that households at (almost) every level had substantially higher incomes in 2007 than in 1979, even after making all kinds of allowances for changes in the cost of living (*). There was also vastly more inequality, particularly but not only towards the top.

The thing which makes this book more interesting than that sounds is the way Blank does very careful comparisons --- she calls them "simulations" --- why try to tease out the factors which have contributed to these shifts (**). Thus she tries to work out how much of the changes in typical incomes and in measures of inequality can be explained by changes in family structure, by changes in labor-force participation, by changes in income by education level, etc., leaving other factors at their 1979 values. Thus she can give answers to questions like "How much richer-but-unequal would we be just from our being more educated, if salaries and marriage patterns still looks like 1979?" Or, rather, she can give reasonable but still conjectural answers to such questions; any sort of counterfactual assertion rests on untestable hypotheses.

To summarize, much of the increase in typical household incomes comes from increased female labor-force participation. Some of the increase inequality is related; it comes from the increased tendency of highly educated men to be married to highly educated women who also work in well-paid jobs. But lots of the increasing inequality, which takes the form of higher household incomes increasing much faster than those at the median (or even the 80th percentile...) can't be explained in these ways. These findings in turn let Blank say some sensible things about how different policies might reduce inequality. (One finding, at first startling, is that bringing every poor household up to the poverty line would actually do very little to reduce inequality by any of the usual metrics.)

This isn't a scintillating read, but it's serious, sober and (as we used to say) reality-based. I read it in part as fodder for my inequality class, and I am seriously considering having The Kids do (simplified) versions of Blank's comparisons. If you have a serious concern with economic inequality, or social change in America since the 1970s, this is very worth reading. §

*: One important limitation to this conclusion, which Blank duly acknowledges, comes with this data. Because the ACS doesn't track households from one year to another, it doesn't let us saying anything about the stability or security of income. In particular, it doesn't let us say whether a household at the median in 1979 could be more confident of staying at the median than their counterparts in 2007. There is evidence that incomes fluctuate more now than they used to, which, if you believe standard economic theory, would reduce the value of any given level of income. ^

**: Mathematically, I think what she does amounts to a piece-wise constant approximation of Handcock and Morris's "relative distribution" method, which was also invented for studying shifts in inequality. But I haven't ground through the algebra and there might be subtle differences. ^

A. M. Stuart, Singapore Sapphire, Revenge in Rubies, Evil in Emerald

Mind-candy historical mysteries, set in Singapore, mostly among just-barely-genteel Britishers, in the years immediately before World War I. Enjoyable period color, though family tradition requires me to make dark aside about British imperialism as I read. §

Books to Read While the Algae Grow in Your Fur; Scientifiction and Inequality; The Dismal Science; Writing for Antiquity; Commit a Social Science; The Progressive Forces; Teaching: Statistics of Inequality and Discrimination; Pleasures of Detection, Portraits of Crime; Tales of Our Ancestors; Physics; The Great Transformation; Biology

Don't @ Me

Attention conservation notice: Rationalizing my gut-level dislike of a social medium as Objectively Correct. First drafted in mid-2017, left to rest in my drafts folder because, while sincere, it feels a bit mean. Posted now because I found myself re-writing the next-to-last paragraph.

---

If, as Leibniz has prophesied, libraries one day become cities, there will still be dark and dismal streets and alleyways as there are now. --- Lichtenberg

I mentioned, some years ago, that in response to reader requests I have a Twitter account. I use this only for announcing new posts here. Messages sent to it will go unread; attempts to communicate through it will be fruitless.

I have, nonetheless, put some time over the years into observing Twitter; I wish I had it back again. There are, so far I can see, only four good uses for Twitter:

1. Announcements of actual, substantive posts, resources or discussions elsewhere. (But we have e-mail and RSS already.)
2. Announcing off-line events, details given elsewhere.
3. Snapshots of cute animals, pretty landscapes, children's birthday parties, and the like.
4. Jokes.

For everything else, well, if someone had deliberately tried to combine the worst features of comments sections and Usenet, they could hardly have done better --- except by first imposing silly length restrictions, followed by kludged-on threads that make Usenet seem a model of clear organization, plus of course an interface that channels people towards the outrage (or main character) of the moment.

I don't know whether it makes people unhappy and angry, or whether only unhappy, angry people persist in using it, but I am not joking when I say that we would all be better off if it disappeared immediately.

--- One of my long-held semi-crank notions is this: all online communication, being through writing, reproduces the social dynamics of literary communities, especially print-literary communities. This law holds independent of the educational level or even intellectual seriousness of the participants. Thus flame-wars, sock-puppets, selective quotation, trawling through the archive for discreditable episodes, "the lurkers support me in e-mail", creating isolated fora to incubate increasingly weird ideas, recycling from supposedly-authoritative source texts long after they're debunked (if they were ever bunked in the first place), spastic attention cascades in which "all fandom was plunged into war", etc., escape from the pages of the little magazines (such as the Philosophical Transactions of the Royal Society), to become part of everyone's life. Twitter has raised this to a new level of awfulness, by making it very hard to actually contribute anything of value, or, having done so, for others to find it and build on it, while still preserving the affordances for weirdness, meanness, and spasm-proneness.

That is my opinion; and it is further my opinion that you people should get off my lawn.

Update, 28 May 2022, further to the theme, in no particular order:

• C. Thi Nguyen, "Twitter, the Intimacy Machine"
• A. A. Forst, "The Poisoned Chalice of Hashtag", Catalyst 4:2 (Summer 2020) (*)
• Chris Hayes, "On the Internet, We're Always Famous", The New Yorker 24 September 2021
• Fonda Lee, "Twitter Is the Worst Reader"
• Momin Malik, Bias and Beyond in Digital Trace Data, Ph.D. Thesis, Carnegie Mellon University, 2018

*: Some comments on Frost's review, without having read the book being reviewed. (1) I am, unsurprisingly, extremely sympathetic to the position that hashtag activism is basically futile. (If the authors really neglect Tufekci's empirical and theoretical work as much as Frost says they do, it's pretty damning.) (2) Not examining right-wing hashtag activism seems like an obvious analytical flaw. (Even if your primary interest is in left-wing movements, the comparisons are essential.) (3) It's true that Twitter isn't accountable to its users, or to the people-as-represented-by-government, but Frost for her part never makes clear which of the flaws she identifies would be remedied by such accountability. (4) Doing something about the opioid epidemic by tinkering with drug policy seems a hell of a lot more practical to me that doing something about it by overthrowing American capitalism, or even reversing the trends in inequality over the last half-century. (I would like to see those trends reversed.) ^

Actually, "Dr. Internet" Is the Name of the Monsters' Creator; Linkage; Modest Proposals; The Collective Use and Evolution of Concepts

Books to Read While the Algae Grow in Your Fur, April 2022

Attention conservation notice: I have no taste, and no qualifications to opine on U.S. politics, or the lives and works of 20th century Marxist intellectuals.

Charles Willeford, Miami Blues and New Hope for the Dead

Mind candy mystery: First two "Hoke Moseley" mystery novels, written and set in Miaimi c. 1980. They're still funny and satisfying crime fiction, but very much artifacts of a vanished age. (The cover of the in-print edition of Miami Blues is more than usually misleading.) The community-college bits in Miami Blues, and particularly the pontificating English professor, are made more amusing by learning that Willeford's day job was, precisely, being an English professor at a Miami community college. §

Elizabeth Hand, Available Dark

Sequel to Generation Loss, which I re-read. This time around, Cass gets mixed up with the confluence of Nordic death metal, neo-paganism, bizarre art photography, and Iceland's role in the financial crisis of 2008. Stirring this together with drugs, booze, toxic nostalgia and her convincingly awful combination of bad decisions and sudden insight produces truly absorbing Plot.

Something which registered on the re-read of Generation Loss, but which eluded me the first time around: Cass isn't from just any podunk town in upstate New York, but from the literally haunted town in Hand's Black Light, whose inhabitants have made a deal with, if not the Devil, then at least a nasty avatar of Dionysus. I now believe that it is legitimate to take Cass's visions not as [just] drug-induced hallucinations, but factual descriptions of supernatural experiences. In particular, I think Cass is, if not exactly a valkyrie or banshee, then something in that line, a walking, talking, bourbon-and-meth-swilling, shutter-happy harbinger of doom, and the birds know it. All of which said, these books are eminently enjoyable on a "straight", non-fantastic level, which is a neat trick.

I eagerly look forward to her further mis-adventures. §

Graydon Saunders, A Succession of Bad Days

Mind candy fantasy, the sorcerors' apprenticeship division: 900-or-so pages of the education of wizards, in the same world as Saunder's The March North, with detailed thermodynamics. (It's not called thermodynamics but I dare say anyone who will enjoy this will recognize what is going on.) I did not enjoy this as much as I did The March North, at least in part because all of the characters tend to sound too much the same, i.e., like Saunders. But I enjoyed it enough to keep reading all the way to the end. §

Jennifer Nicoll Victor, Understanding the U.S. Government

The fact that I listened to a course of Poli. Sci. 1 lectures, and learned from them, shows I am not qualified to actually review them. But I enjoyed this. §

Disclaimer: Prof. Victor and I actually collaborated once, in supervising a student project which tried to use social network analysis to get at the question of whether campaign donations affect Congressional outcomes. It was never published because we got null results (and the student moved on to other things). In retrospect, my guess is that resources (including funds) do matter, but that it's rare for disputed issues to have lots of resources on only one side of the dispute (if they did, the dispute wouldn't stay on the agenda for long), and the study wasn't well-positioned to get at the counter-factuals. But, like I said, I learned stuff about how my government works from these lectures, so you probably shouldn't listen to me!

Stanley Pierson, Leaving Marxism: Studies in the Dissolution of an Ideology

Mostly, this is three biographies of three very different intellectuals who all ended up ex-Marxists: Henri de Man, the Belgian advocate of planning and WWWII-collaborator; Max Horkheimer of the Frankfurt School; and Leszek Kolakowski. Pierson emphasizes that, like many Marxist intellectuals, they came from bourgeois backgrounds, were drawn to socialism and to Marxism by its resonance with their bourgeois values, and ultimately left Marxism because of those same values. (He does not inquire into how they differed from intellectuals of bourgeois origins who remained Marxists, or the rare 20th-century Marxist intellectuals from humbler backgrounds like Gramsci.) There are no great revelations here, but they're well-written and well-researched biographical studies. Recommended if you care about intellectuals in politics, or the Marxist tradition. §

Books to Read While the Algae Grow in Your Fur; Scientifiction and Fantastica; Pleasures of Detection, Portraits of Crime; The Progressive Forces; Writing for Antiquity; Commit a Social Science; The Beloved Republic

Intermittent Finds in Complex Systems and Stuff, No. 2

Attention conservation notice: Links to forbiddingly-technical scientific papers and lecture notes, about obscure corners of academia you don't care about, and whose only connecting logic is having come to the attention of someone with all the discernment and taste of a magpie (who's been taught elementary probability theory).

Or whatever the heck it is I study these days. (I did promise that this series would be intermittent.) In no particular order.

Modibo K. Camara, "Computationally Tractable Choice" [PDF]

I'll quote the abstract in full:

I incorporate computational constraints into decision theory in order to capture how cognitive limitations affect behavior. I impose an axiom of computational tractability that only rules out behaviors that are thought to be fundamentally hard. I use this framework to better understand common behavioral heuristics: if choices are tractable and consistent with the expected utility axioms, then they are observationally equivalent to forms of choice bracketing. Then I show that a computationally-constrained decisionmaker can be objectively better off if she is willing to use heuristics that would not appear rational to an outside observer.

If you like seeing SATISFIABILITY reduced to decision-theoretic optimization problems, this is the paper for you. I enjoyed this partly out of technical interest, and partly to see Simon and Lindblom's heuristic arguments from the 1950s rigorously validated.

One last remark: the slippage of "rationality" in the last sentence of the abstract is fascinating. We started by wanting to define "rational behavior" as being about effectively adapting means to ends; we had an intuition, inherited from 18th century philosophy, that calculating the expectation values in terms of rat orgasm equivalents would be a good way to adapt means to ends; we re-defined "rational behavior" as "acting as though one were calculating and then maximizing an expected number of rat orgasm equivalents"; now it turns out that that is provably an inferior way of adapting means to ends, and we have to worry about what it says about rationality. There's something very wrong with this picture! §

(Thanks to Suresh Naidu for sharing this paper with me.)

Carlos Fernández-Loría and Foster Provost, "Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters", arxiv:2104.04103

To make an (admirably simple) argument even simpler: Think of decision-making as a classification problem, rather than estimation. If your classifier mis-estimates \( \mathbb{P}\left( Y|X=x \right) \), but you're nonetheless on the correct side of 1/2 (or whatever your optimal boundary might be), it doesn't matter for classification accuracy! So if you over-estimate the benefits of treatment for those you decide to treat, well, you're still treating them...

Ira Globus-Harris, Michael Kearns, Aaron Roth, "Beyond the Frontier: Fairness Without Privacy Loss", arxiv:2201.10408

My comments got long enough to go elsewhere.

Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan, "Information-theoretic generalization bounds for black-box learning algorithms", arxiv:2110.01584

I was very excited to read this --- look at the authors! --- and it did not disappoint. It's a lovely paper which both makes a lot of sense at the conceptual level and gives decent, calculable bounds for realistic situations. I'd love to teach this in my learning-theory class, even though I'd have to cut other stuff to make room for the information-theoretic background.

Adityanarayanan Radhakrishnan, Karren Yang, Mikhail Belkin, Caroline Uhler, "Memorization in Overparameterized Autoencoders", arxiv:1810.10333

I was blown away when Uhler demonstrated some of the results in a talk here, and the paper did not disappoint.

Mikhail Belkin, "Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation", arxiv:2105.14368

Further to the theme.

Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, Colin Raffel, "Extracting Training Data from Large Language Models", arxiv:2012.07805

Demonstrates that from GPT-2 they can extract "(public) personally identifiable information (names, phone numbers, and email addresses), IRC conversations, code, and 128-bit UUIDs", even though "each of the above sequences are included in just one document in the training data".

Some miscellaneous, largely mean, comments:

• I don't understand why they compare zlib entropy to language-model perplexity, when entropy density is basically log(perplexity). This probably wouldn't make a big difference to any results but it bugged me.
• This has to be connected to Radhakrishnan et al., right?
• I'd really like to see someone throw this many parameters, and this much data, at something like Pereira, Singer and Tishby 1996 and see how it does in comparison, both in terms of the usual performance metrics and memorizing random (and inappropriate) bits of the training data. (Pereira may be in a position to do the experiment!)
• Some people will, of course, interpret this as evidence that GPT-2 knows who you are, and so is that much closer to judging the quick and the dead basilisk-dom being amenable to bargaining under the canons of timeless decision theory.

Gabriel Rossman and Jacob C. Fisher, "Network hubs cease to be influential in the presence of low levels of advertising", Proceedings of the National Academy of Sciences 118 (2021): e2013391118

In a pure social-contagion/diffusion-of-innovations process, the contagion/innovation will spread farther, and spread faster, if it begins at one of the the most central nodes in the network, than if it begins at a randomly chosen node, let alone a deliberately-peripheral one. This motivates a lot of effort in applications to search for influential figures and target them. What Rossman and Fisher do is extend the model very modestly, to model "advertising", i.e., a probability for nodes to contract the contagion / adopt the innovation spontaneously, without direct contact with an infected / adopter node. What they show is that even a very small amount of advertising massively reduces the advantage of beginning at a central node. It's a very convincing, lovely, and potentially-applicable result. I also strongly suspect there's a genuine phase transition here, with the transition point moving towards zero external field as the size of the network goes to infinity, but I haven't been able to show that (yet). --- Many thanks to Prof. Rossman for presenting this paper to CMU's Networkshop.

Yuan Zhang, Dong Xia, "Edgeworth expansions for network moments", arxiv:2004.06615

This is technical, but valuable for all of us interested in being able to quantify uncertainty in network data analysis, especially in those of us working graph-limits/graphons/conditionally-independent-dyads framework. --- Thanks to Prof. Zhang for a very enjoyable conversation about this paper during a "visit" to Ohio State via Zoom.

David Childers, "Forecasting for Economics and Business"

Great materials for an undergraduate economics course (73-423) at CMU. Thanks to David for the pointer.

Vera Melinda Galfi, Valerio Lucarini, Francesco Ragone, Jeroen Wouters, "Applications of large deviation theory in geophysical fluid dynamics and climate science", La Rivista del Nuovo Cimento 44 (2021): 291--363, arxiv:2106.13546

The laws of large numbers say that, on large enough scales, random systems converge on their expected values. ("Large scales" here might indeed be number of samples, or length of time series, or something similar.) In symbols which you should not take too literally here, as \( n \rightarrow \infty \), \( \mathbb{P} \left( |A_n - a_{\infty}| > \epsilon \right) \rightarrow 0 \) for every \( \epsilon > 0 \), where \( a_{\infty} \) is the limiting behavior of the process. Large deviations theory is about fluctuations away from the expected behavior, and specifically about finding rate functions \( r \) such that \( \mathbb{P} \left( |A_n - a_{\infty}| \geq \epsilon \right) \sim \exp{\left( -n r(\epsilon)\right) } \). This is a "large" deviation because the size \( \epsilon \) is staying the same as \( n \) grows. We'd anticipate seeing this kind of behavior if \( A_n \) was the result of some number \( \propto n \) of independent random variables, all of which had to cooperate in order to produce that \( \epsilon \)-sized fluctuation. More specifically, a good point-wise rate function will let us say that \[ \frac{1}{n}\log{\mathbb{P}\left( A_n \in B \right) } \rightarrow - \inf_{x \in B}{I(x)} \] so that, as the saying goes, an unlikely large deviation is overwhelmingly (exponentially) likely to happen in the least unlikely possible way. Large deviations theory gives us lots of tools for calculating rate functions, and so saying how unlikely various large deviations are (at least to within asymptotic log factors), and for characterizing those least-unlikely paths to improbable events. (I am glossing over all kinds of lovely mathematical details, but follow some links.)

Now climate systems contain a lot random variables, which are mostly tightly dependent on each other but not completely so. And a lot of what we should worry about with climate comes from large fluctuations away from typical behavior. (E.g., transitions from one meta-stable state of the climate, where, say, there is a Gulf Stream in the North Atlantic keeping western Europe warmer than Labrador or Kamchatka, to another meta-stable state where there is not.) So climate modeling is actually a very natural application for large deviations theory. This is a well-written review paper surveying those applications, with a minimum of mathematical apparatus. (The implied reader does, however, remember fluid mechanics and thermodynamics.) It makes me want to learn more about rare-event simulation techniques. §

Complexity; Enigmas of Chance; Networks; Physics; The Dismal Science; Constant Conjunction Necessary Connexion; Automata and Calculating Machines

Positive-Definite Tab Closure

Attention conservation notice: A link-dump piece, where some of the links were first opened in 2015.

Tabs I have closed recently, which are of a positive and/or constructive and/or cheerful nature:

• The Glorious Stupidity of Fomenko's New Chronology. (Part of what makes this so outrageous is that Fomenko is actually rightly celebrated for his mathematical work.)
• "Always bet on text".
• Teaching nearest neighbor methods led me to talking about Voronoi tesselations in class, which led to me finally asked "who was Voronoi, anyway?", and so discovering Halyna Sen and Rien van de Weygaert's "Life and Times of Georgy Voronoi" (arxiv:0912.3269).
• Similarly, there is an entirely logical path from re-assuring a certain historian that she did indeed understand the equations in Bacaër's chapter on Song Jian and China's one-child policy to listening to International Federation for Automatic Control's Orchestra performing "Smoke on the Water", but I decline to explain.
• Further musical entertainment: the Brooklyn Funk Essential's "The Revolution Was Postponed Because of Rain"
• Speaking of the Revolution that wasn't: "The First Privilege Walk" is an amazing article full of wonders; also of places where I had to roll my eyes at Parenti's unreconstructed Marxist-true-believer-dom.
• Speaking of the Revolution yet to come: "What Do Addison Rae's Critics Have in Common With the Taiping Rebels?"
• "Life in a carbon dioxide world" (via Paul McAuley)
• For living in a carbon dioxide world in a different sense, we might consider building commercially-competitive "junkyard datacenters" from decade-old smartphones. This is very cute, and even hopeful, but the calculations in the paper make me long for carbon pricing with a special fervor. (Also, I hate the way this paper uses the word "compute" with a peevish passion.)
• The eminent probabilist Michel Talagrand tries to make sense of quantum field theory. (I've just begun reading the book, but the handful of people who'll find that sentence exciting need to hear the news.)
• Speaking of QFT: a remembrance and appreciation of Steven Weinberg.
• Speaking of shapers of 21st century thought: Arianna Wright Rosenbluth and the birth of Markov chain Monte Carlo
• Speaking of the shape of ancient thought: "A work of speculative friction"
• Speaking of speaking about your thoughts: "Phobos and Deimos and public speaking" [cf. On Academic Talks: Memory and Fear]
• Adam Elkus uses Norbert Wiener to re-invent the young Marx's theory of alienation (without mentioning "Marx" or "alienation"). Also from Adam Elkus: Vampire Hunting as a Vocation.
• Maciej Ceglowski's "Confronting New Madrid" (I, II) is the best travelogue about seismic hazards and the meaning of America ever. Not that one expects anything less from the chronicler of America's most grandiose, and tasty, imaginary infrastructure.
• David Auerbach's Books of the Year for 2020 and 2021. The fiction recommendations always make me feel provincial and Philistine (not his fault!), but when I'm familiar with the non-fiction works they're uniformly of high quality, and I mine these for my own purposes.

(I am sure that I am forgetting to credit sources for these links, and can only plead for forgiveness.)

Linkage; Psychoceramica; Physics; Natural Science of the Human Species; Mathematics; Automata and Calculating Machines; The Commonwealth of Letters; Biology

Books to Read While the Algae Grow in Your Fur, March 2022

Attention conservation notice: I have no taste, and no credentials to opine on the sociology of education, political and moral philosophy, medieval Islamic science, or even, strictly speaking, pure mathematics.

Dana Stabenow, A Cold Day for Murder, A Fatal Thaw, Dead in the Water, A Cold-Blooded Business, Play with Fire

Mind candy mysteries, where the Alaskan environment is as much a character as any human being, or husky. Stabenow was, I believe, originally a science fiction and fantasy writer, and I think some of that comes through in the way the very strange world of Alaska is unfolded before the reader. It also comes through in the character of Kate Shugak, a hero of basically-royal birth who lives on the border between civilization and the wilderness, and who roams the countryside defeating monsters and malefactors, especially those who have offended against the laws of kinship and hospitality. (There are a lot of explicit references to Greek myths and I do not believe any of this is coincidence or even unconscious.) The fact that I read five of these in a month, and have more in the queue, tells you how easily they go down. §

Douglas B. Downey, How Schools Really Matter: Why Our Assumption about Schools and Inequality Is Mostly Wrong

I am not sure what to make about this one.

Downey studies some nationally-representative longitudinal data sets, which measure student achievement in reading and math at multiple points in the school year, over multiple years. "Longitudinal" here means that each student is being measured multiple times, allowing one to draw inference about how much was learned when. The basic finding Downey extracts from this is that during the school year, richer and poorer students, and black and white students, learn at basically the same rate. But they arrive at school at very different average levels of achievement, and their gaps grow while out of school each year. Thus, on this evidence, schools for the disadvantaged are in fact doing about as well at teaching reading and math as other schools. The inequality in educational outcomes, then, isn't due to inequality in schooling, but to (as Downey puts it) the other 87% of students' lives.

This is remarkably contrary to received opinion, what Downey calls "The Assumption", that schools for the poor are poor schools which do not teach effectively. I get the impression that Downey started by wanting to be talked out of this position, but came to embrace it for lack of intelligent opposition:

I don't think that the people questioning the evidence are bad people, but they are reluctant to let go of the dominant narrative about schools. It would be one thing if the reason was because they had issues with whether the ECLS-K item-response theory scales of reading can be considered truly interval, or if they questioned whether nonschool investments in children are constant across seasons, or if they thought that the approach scholars use to model the overlap days between test dates and the beginnings and ends of school years was insufficient. ... But while many have resisted the empirical patterns in chapters 1--4 and remain committed to The Assumption, the quality of evidence doesn't seem to be the obstacle. [p. 97]

I join Downey's audiences in astonishment. I also join him in thinking that "we really need to reform the distribution of rewards in the broader society", but I just have a hard time swallowing the findings. (Among other things, if he's right, why are parents so convinced otherwise?) But I also don't have any clever explanations to make this pattern in the data into a mere artifact. As a statistician, I do wonder about whether these surveys really cover a nationally representative sample of students and schools. (Though it's hard to imagine what sort of sampling bias would produce this pattern!) There is also the issue (which Downey highlights in the quote above) of whether these reading and math scores are really "interval". Concepts like "median" make sense with merely ordinal variables, but something like "the change in the median poor kid's reading score from September to May is equal to the change in median scores for rich kids", \( X_p(2) - X_p(1) = X_r(2) - X_r(1) \), needs us to be able to compare differences at arbitrary points along the scale. So this is resting a lot on the ways the survey researchers translate students' answers into numerical values, and I'd have liked to see a lot more about that. In particular I'd want to make really sure that this sort of parallel trajectories isn't an artifact of the scaling procedure.

It is unlikely, but not I guess impossible, that I will actually investigate this properly. In the meanwhile, I am informed, but puzzled and unsettled. §

(Text lightly edited 3 June 2022, to resolve some ambiguous pronouns etc.)

Update, 3 July 2022: a favorable review in the American Journal of Sociology.

Jürgen Jost, Postmodern Analysis

I should begin by admitting that I took real analysis as a sophomore, scraped out a C through the kindness of the teacher, and became a physicist. (I did eventually learn measure-theoretic probability.) So the idea of anyone taking advice from me on pure math textbooks is preposterous.

I should also say that I met Jürgen through Santa Fe more than twenty years ago, admire his work on information geometry and complex systems, have given talks at the Max Planck Institut he directs, etc. If I read one of his books and didn't like it, I'd just say nothing publicly.

With my throat now hopefully adequately cleared: When we all went home in March 2020, I got the idea that this would be when I finally learned some important areas of math properly. This fantasy led to downloading a large number of books from the library, and discovering that I would never read most of them for good reason. But this one I stuck with. It's a really good survey of crucial topics in analysis, starting with the basics of differentiation and Riemann integration, visiting things like ordinary differential equations as dynamical systems, Lebesgue integration, and function approximation, and ending up with the calculus of variations and partial differential equations and their interconnections. It's "postmodern" only in the sense that it comes after the classical works on modern analysis of the mid- / late- 20th century, and tries to give a survey of what a bright young mathematician should know now. The exposition is great, consistently just rigorous enough that I needed to inhibit my lizard-brain physicist impulses ("it'd be nice if that equation had a square-integrable solution, therefore it does"), but always with an eye on applications, i.e., on reality. It's really quite enjoyable, and makes me want to read Jost's other textbooks. §

(The obvious question is whether I would have done any better, as an undergrad, if this had been the text in my real analysis course. Honesty compels me to say: "not on your life"; our textbook was forgettable but decent, the problem was teenage me.)

Final disclaimer: I read the second (2003) edition; the third (2005) edition seems to mostly correct mis-prints, and add some results on coverings in the chapter on \( L^p \) function spaces. But I cannot swear to its content the way I can to the 2nd edition.

Stuart Hampshire, Justice Is Conflict

This is a strange (and short) little book of philosophy. The starting point is Plato's analogy, in the Republic, between conflict within the soul and conflict within the city (= polity). Hampshire says that, pace Plato, the way we really resolve conflict in the city is to make sure that all (he says "both") sides know that they have been able to make their case and be heard, even if they cannot get what they want. What ultimately matters is that there was a fair procedure, rather than a substantively just outcome. In the analogy of inner conflict, individual people just have more-or-less incompatible values, and we should not expect to find some way of reconciling them or subordinating one to the most correct values. Nor, he says, should we even want such a reconciliation or ordering.

I am sympathetic --- in some sense he's getting at the core of liberalism --- but I found the argument lacking. The analogy is obviously a bit weak: I don't think he ever really addresses what would correspond to a fair procedure in the soul. (Adversarial or critical thinking is all very well to endorse, but being your own critic has obvious limits.) Also, I think he equivocates about whether unifying values is impossible, or merely undesirable. That's fine by me, because I am strongly in the "impossible" camp --- I encountered "A heterarchy of values determined by the topology of nervous nets" at an impressionable age, and still regard it as irrefutable --- but philosophically a bit unsatisfying.

More frustrating was that Hampshire is fully aware that there are often disputes about which procedures are fair, and this doesn't seem to help us figure that out at all. To use a (banausic and depraved) analogy of my own: if I am writing new code to perform some task, i.e., devising a procedure, I check whether it works right by seeing if it gives the correct answer on test cases, i.e., is substantively correct in particular circumstances. But of course, just to make things circular, in other cases I work out what the answer is by using my procedure. At a much more elevated plane than numerical software, something like this would seem to be at work here, and could use some philosophical illumination. That is, I wish Hampshire would absorb something like Laudan's Science and Values. §

George Malagaris, Biruni [doi:10.1093/oso/9780190124021.001.0001]

Brief historical study of Abu Rayhan Muhammad ibn Ahmad al-Biruni (973 -- 1050?), emphasizing the historical context of Central Asia and the eastern Islamic world in general, giving the main facts of Biruni's biography (including puncturing some picturesque stories), and surveying his major works. Pride of place in Malagris's treatment goes to Biruni's India, fairly enough, but he's pretty comprehensive, and seems to understand the math. (I was astonished to learn that Biruni translated/adapted the Yoga sutras of Patanjali, which must have made some heads explode.) There's also some treatment of his correspondence with ibn Sina; it is simultaneously reassuring and depressing to see that a millennium ago, great scholars were just as capable of mutual incomprehension, dismissal, and pettiness as their modern counterparts, or online posters (cf.) (Actually, I suspect there's the possibility for a very interesting study of different conceptions of "science" in this exchange, and I wonder if someone has done it.) The book concludes with a treatment of Biruni's place in later historical memory, including the way he is claimed by multiple modern nation-states as part of their illustrious past. §

John Scalzi, The Kaiju Preservation Society

Mind candy comic science fiction. It's Scalzi, which means it's funny and mostly but not entirely lightheartedly, and reads extremely smoothly. §

Jane Langton, The Dante Game

Mind candy mystery: the umpteenth book in Langton's series, in which Homer Kelly stumbles his way into an artistic or literary enthusiasm and a homicide investigation. This time it's Dante, and the city of Florence, and the new pope's anti-drug crusade, which is far too successful for some people's liking. It's an old favorite which holds up very well. (Previously.) §

Books to Read While the Algae Grow in Your Fur; Pleasures of Detection, Portraits of Crime; Philosophy; Commit a Social Science; Scientifiction and Fantastica; Islam and Islamic Civilization; Afghanistan and Central Asia; Writing for Antiquity; Mathematics; Teaching: Statistics of Inequality and Discrimination

Books to Read While the Algae Grow in Your Fur, February 2022

Attention conservation notice: I have no taste, and no qualifications to opine on the history of Central Asia, the philosophy of science, the anthropology of New Guinea and/or cultural creativity, archaeology, Antarctic exploration, or the philosophy of Spinoza.

Adeeb Khalid, Central Asia: A New History from the Imperial Conquests to the Present

By "central Asia", Khalid means "Turkestan", both the eastern parts conquered by the Qing in the 1700s and the western parts conquered by the Romanovs in the 1800s. (Thus Afghanistan, Tibet, Mongolia, etc., feature only incidentally.) He begins with those conquests, after a little scene-setting to make their events comprehensible, and then goes down to 2020 and the on-going police state and cultural genocide in Xinjiang. Khalid's great (and persuasive) theme is how ordinary this history is, in a global perspective --- imperial conquest, the arrival of modernity, the development of nationalism and the construction of national cultures (he doesn't use the phrase "peasants into Uzbeks", but he comes close), Communism as a vehicle for nationalism, ambitious-to-mad state projects to develop economies, to transform nature and/or transform society, widening entanglement with global culture and economic forces... This is what the 19th, 20th and 21st centuries were like, for much if not most of the world. It's extremely scholarly --- Khalid has clearly read and synthesized almost everything --- but still very readable. If you are at all interested in this part of the world, it's very much worth your time. §

Wesley C. Salmon, with Richard C. Jeffrey and Jeffrey G. Greeno, Statistical Explanation and Statistical Relevance (Pittsburgh: University of Pittsburgh Press, 1971)

1300 words of review: Distinctions That Make Differences to Chances.

Annalee Newitz, Four Lost Cities: A Secret History of the Urban Age

I have mixed feelings about this. On the one hand, it's pleasantly-written and engaging popular social science about four interesting and important cities that were, for one reason or another, abandoned and (largely) forgotten: Çatalhöyük, Pompeii, Angkor and Cahokia. I learned from it, and I mostly enjoyed reading it. On the other hand, I sometimes found myself irritated by the sensation that Newitz was pandering to the prejudices of people like me --- all the cities were full of diverse immigrants, etc., etc. (Looking around after writing that, I see James Palmer had a similar reaction to those bits.)

Beyond those matters of tone, though, I do want to quibble with the way Newitz presents these cities. Many archaeologists have a bad tendency to present speculative interpretations as though they were facts. (They are not, of course, alone in this, and I've complained about this before.) This tendency seems to be very much on display here in the chapters on Çatalhöyük and Cahokia, where we have no writings to fill us in on ideologies and structures of inequality (not to say oppression). I can't help but suspect that this makes those cities better screens for modern projections than Pompeii and Angkor. There's also some trash-talking of V. Gordon Childe that strikes me as unfair, and dismissal of the idea that there are developmental trajectories to more hierarchy, size and complexity as Eurocentric myths, rather than cross-cultural empirical regularities. (And of course a key part of the Enlightenment world-view was seeing Europe as a place which had regressed in these regards for a millennium of barbarism, "mired in the superstitions and brutal monarchies of the Middle Ages", as Newitz puts it on p. 210.)

On re-reading this, I see I've given more space to what irritated me, which is mostly incidental, than to what I enjoyed --- so I will just re-iterate that despite my quibbles, I did enjoy. §

(Thanks to Jan Johnson for my copy of the book.)

Fredrik Barth, Cosmologies in the Making: A Generative Approach to Cultural Variation in Inner New Guinea

750-plus words of review: Cosmology and Cosmologists --- The Modern Ok School.

(I forget what chain of references first put this on my radar --- probably something in the Dan Sperber / Pascal Boyer nexus, but that's honestly just me guessing.)

Edmund Stump, The Roof at the Bottom of the World: Discovering the Transantarctic Mountains

A scientist's winningly enthusiastic history of exploration in the Antarctic mountains, from the first visits to the continent, through the heroic era, to the early 1960s. (It's startling just how much more massive the US's post-1945 efforts were than everything that came before.) The stories are supplemented with Stump's own memories of decades of geologizing on the continent, and his very good photographs. §

Steven Nadler, A Book Forged in Hell: Spinoza's Scandalous Treatise and the Birth of the Secular Age

Partly exposition of the Theological-Political Treatise, partly a biography of Spinoza, partly intellectual, political and religious history to set the context. I enjoyed it, but since I've never actually read the Treatise, despite an interest in Spinoza, I'm in no position to judge it. §

Books to Read While the Algae Grow in Your Fur; Writing for Antiquity; Philosophy Enigmas of Chance; Afghanistan and Central Asia; The Great Transformation; Minds, Brains, and Neurons; Commit a Social Science; Psychoceramics