Escaping abstraction

Jacques Barzun, “History as Counter-Method and Anti-Abstraction,” quoted in Arthur Krystal’s 2007 profile in The New Yorker:

History, like a vast river, propels logs, vegetation, rafts, and debris; it is full of live and dead things, some destined for resurrection; it mingles many waters and holds in solution invisible substances stolen from distant soils. Anything may become part of it; that is why it can be an image of the continuity of mankind. And it is also why some of its freight turns up again in the social sciences: they were constructed out of the contents of history in the same way as houses in medieval Rome were made out of stones taken from the Coliseum. But the special sciences based on sorted facts cannot be mistaken for rivers flowing in time and full of persons and events. They are systems fashioned with concepts, numbers, and abstract relations. For history, the reward of eluding method is to escape abstraction.

The cultural Cold War

Jamie Cohen-Cole, The Open Mind: Cold War Politics and the Sciences of Human Nature (2014)

Greg Barnhisel, Cold War Modernists: Art, Literature, and American Cultural Diplomacy (2015)

Loren Graham, Science in Russia and the Soviet Union: A Short History (1993)

Sarah Miller Harris, The CIA and the Congress for Cultural Freedom in the Early Cold War (2016)

John Krige, American Hegemony and the Postwar Reconstruction of Science in Europe (2006)

Stuart W. Leslie, The Cold War and American Science: The Military-Industrial-Academic Complex at MIT and Stanford (1993)

Christopher J. Phillips, The New Math: A Political History (2014)

Carroll Pursell, Technology in Postwar America: A History (2007)

Gregory A. Reisch, How the Cold War Transformed Philosophy of Science: To the Icy Slopes of Logic (2005)

Giles Scott-Smith, The Politics of Apolitical Culture (2016)

Valery N. Soyfer, Lysenko and the Tragedy of Soviet Science (1994)

Frances Stonor Saunders, The Cultural Cold War: The CIA and the World of Arts and Letters (1999)

Jessica Wang, American Science in an Age of Anxiety: Scientists, Anticommunism, and the Cold War (1999)

Duncan White, Cold Warriors: Writers Who Waged the Literary Cold War (2019)

Stephen J. Whitfield, The Culture of the Cold War (1991)

Hugh Wilford, The Mighty Wurtlizter: How the CIA Played America (2008)

Audra J. Wolfe, Freedom’s Laboratory: The Cold War Struggle for the Soul of Science (2018)

Audra J. Wolfe, Competing with the Soviets: Science, Technology, and the State in Cold War America (2013)

Mechanism in biology

William Bechtel, Discovering Cell Mechanisms: The Creation of Modern Cell Biology (2006)

William Bechtel, “Mechanism and Biological Explanation,” Philosophy of Science (2011)

William Bechtel, “Biological Mechanisms: organized to maintain autonomy,” in Systems Biology: Philosophical Foundations (2007)

Carl Craver and James Tabery, “Mechanisms in Science,” Stanford Encyclopedia of Philosophy (2015)

Carl F. Craver and Lindley Darden, In Search of Mechanisms: Discoveries Across the Life Sciences (2013)

Margaret Gardel, “Moving beyond molecular mechanisms,” Journal of Cell Biology (2015)

Daniel J. Nicholson, “The concept of mechanism in biology,” Studies in History and Philosophy of Biological and Biomedical Sciences (2012)

Rob Phillips, “Musings on mechanism: quest for a quark theory of proteins?” Journal of the Federation of American Societies for Experimental Biology (2017)

James Tabery, Monika Piotrowska, and Lindley Daren, “Molecular Biology,” Stanford Encyclopedia of Philosophy (2015)

The two cultures of statistical modeling

Peter Norvig, “On Chomsky and the Two Cultures of Statistical Learning” (2011):

At the Brains, Minds, and Machines symposium held during MIT’s 150th birthday party, Technology Review reports that Prof. Noam Chomsky

derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don’t try to understand the meaning of that behavior.

The transcript is now available, so let’s quote Chomsky himself:

It’s true there’s been a lot of work on trying to apply statistical models to various linguistic problems. I think there have been some successes, but a lot of failures. There is a notion of success … which I think is novel in the history of science. It interprets success as approximating unanalyzed data.

This essay discusses what Chomsky said, speculates on what he might have meant, and tries to determine the truth and importance of his claims. Chomsky’s remarks were in response to Steven Pinker’s question about the success of probabilistic models trained with statistical methods.

  1. What did Chomsky mean, and is he right?
  2. What is a statistical model?
  3. How successful are statistical language models?
  4. Is there anything like their notion of success in the history of science?
  5. What doesn’t Chomsky like about statistical models?

The abstract of Leo Breiman, “Statistical Modeling: The Two Cultures” in Statistical Science (2001):

There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.

Some definite knowledge to be obtained

from the preface to Bertrand Russell’s Dictionary of Mind, Matter, and Morals (1952):

I feel considerably honoured that my philosophy should have been thought worthy to be alphabetically anatomized in this dictionary. I have been accused of a habit of changing my opinions in philosophy and, in so far as this is true, the dictionary will enable readers to find it out. I am not myself in any degree ashamed of having changed my opinions. What physicist who was already active in 1900 would dream of boasting that his opinions had not changed during the last half century? In science men change their opinions when new knowledge becomes available, but philosophy in the minds of many is assimilated rather to theology than to science. A theological proclaims eternal truths, the creeds remain unchanged since the Council of Nicaea. Where nobody knows anything, there is no point in changing your mind. But the kind of philosophy that I value and have endeavoured to pursue is scientific in the sense that there is some definite knowledge to be obtained and that new discoveries can make the admission of former error inevitable to any candid mind.

Quite an everyday occurrence

from Huygens and Barrow, Newton and Hooke, Vladimir Arnold, translated by Eric J. F. Primrose (1989):

Hooke was a poor man and began work as an assistant to Boyle (who is now well known thanks to the Boyle-Mariotte law discovered by Hooke). Subsequently Hooke began working in the recently established Royal Society (that is, the English Academy of Sciences) as Curator. The duties of the Curator of the Royal Society were very onerous. According to his contract, at every session of the Society (and they occurred every week except for the summer vacation) he had to demonstrate three or four experiments proving the new laws of nature.

Hooke held the post of Curator for forty years, and all that time he carried out his duties thoroughly. Of course, there was no condition in the contract that all the laws to be demonstrated had to be devised by him. He was allowed to read books, correspond with other scientists, and to be interested in their discoveries. He was only required to verify whether their statements were true and to convince the Royal Society that some law was reliably established. For this it was necessary to prove this law experimentally and demonstrate the appropriate experiment. This was Hooke’s official activity.

[…]

At that time it was easy to carry out fundamental discoveries, and large numbers of them were carried out. Huygens, for example, improved the telescope, looked at Saturn and discovered its ring, and Hooke discovered the red spot on Jupiter. At that time discoveries were not unusual events, they were not registered, not patented, as they are now, they were quite an everyday occurrence. (This was the case not only in the natural sciences. Mathematical discoveries at that time also poured forth as if from a horn of plenty.)

But Hooke never had enough time to dwell on any of his discoveries and develop it in detail, since in the following week he needed to demonstrate new laws. So in the whole manifold of Hooke’s achievements his discoveries appeared somewhat incomplete, and sometimes when he was in a hurry he made assertions that he could not justify accurately and with mathematical rigour.

[…]

Holding the chair at Cambridge, Newton earned considerably more (200 pounds a year), and the farm that he had inherited, which he leased out and where the famous apple tree grew, gave him roughly the same income. Despite the fact that Newton was quite well off, he did not want to spend any money on the publication of the book, so he sent the Principia to the Royal Society, which decided to publish the book at its own expense. But the Society had no money, so the manuscript lay there until Halley (who was the son of a rich soap manufacturer) published it on his own account. Halley took on himself all the trouble of publishing the book, and even read the proofs himself. Newton, in correspondence at this time, called it “Your book”…

The world is awash in bullshit

from the introduction to the course Calling Bullshit: Data Reasoning in a Digital World, taught by Carl T. Bergstrom and Jevin West at the University of Washington:

The world is awash in bullshit. Politicians are unconstrained by facts. Science is conducted by press release. Higher education rewards bullshit over analytic thought. Startup culture elevates bullshit to high art. Advertisers wink conspiratorially and invite us to join them in seeing through all the bullshit — and take advantage of our lowered guard to bombard us with bullshit of the second order. The majority of administrative activity, whether in private business or the public sphere, seems to be little more than a sophisticated exercise in the combinatorial reassembly of bullshit.

We’re sick of it. It’s time to do something, and as educators, one constructive thing we know how to do is to teach people. So, the aim of this course is to help students navigate the bullshit-rich modern environment by identifying bullshit, seeing through it, and combating it with effective analysis and argument.

What do we mean, exactly, by bullshit and calling bullshit? As a first approximation:

Bullshit involves language, statistical figures, data graphics, and other forms of presentation intended to persuade by impressing and overwhelming a reader or listener, with a blatant disregard for truth and logical coherence.

Calling bullshit is a performative utterance, a speech act in which one publicly repudiates something objectionable. The scope of targets is broader than bullshit alone. You can call bullshit on bullshit, but you can also call bullshit on lies, treachery, trickery, or injustice.

In this course we will teach you how to spot the former and effectively perform the latter.

An engine of discovery

from the preface to Cell Biology by the Numbers, Ron Milo and Rob Phillips:

One of the great traditions in biology’s more quantitative partner sciences such as chemistry and physics is the value placed on centralized, curated quantitative data. Whether thinking about the astronomical data that describes the motions of planets or the thermal and electrical conductivities of materials, the numbers themselves are a central part of the factual and conceptual backdrop for these fields.  Indeed, often the act of trying to explain why numbers have the values they do ends up being an engine of discovery.

A requirement for genuine expertise

David Foster Wallace in conversation with Dave Eggers, The Believer, 2003:

We live today in a world where most of the really important developments in everything from math and physics and astronomy to public policy and psychology and classical music are so extremely abstract and technically complex and context-dependent that it’s next to impossible for the ordinary citizen to feel that they (the developments) have much relevance to her actual life. Where even people in two closely related sub-sub-specialties have a hard time communicating with each other because their respective s-s-s’s require so much special training and knowledge. And so on. Which is one reason why pop-technical writing might have value (beyond just a regular book-market $-value), as part of the larger frontier of clear, lucid, unpatronizing technical communication. It might be that one of the really significant problems of today’s culture involves finding ways for educated people to talk meaningfully with one another across the divides of radical specialization. That sounds a bit gooey, but I think there’s some truth to it. And it’s not just the polymer chemist talking to the semiotician, but people with special expertise acquiring the ability to talk meaningfully to us, meaning ordinary schmoes. Practical examples: Think of the thrill of finding a smart, competent IT technician who can also explain what she’s doing in such a way that you feel like you understand what went wrong with your computer and how you might even fix the problem yourself if it comes up again. Or an oncologist who can communicate clearly and humanly with you and your wife about what the available treatments for her stage-two neoplasm are, and about how the different treatments actually work, and exactly what the plusses and minuses of each one are. If you’re like me, you practically drop and hug the ankles of technical specialists like this, when you find them. As of now, of course, they’re rare. What they have is a particular kind of genius that’s not really part of their specific area of expertise as such areas are usually defined and taught. There’s not really even a good univocal word for this kind of genius—which might be significant. Maybe there should be a word; maybe being able to communicate with people outside one’s area of expertise should be taught, and talked about, and considered as a requirement for genuine expertise.… Anyway, that’s the sort of stuff I think your question is nibbling at the edges of, and it’s interesting as hell.

Energies and perseverances

Thomas Jefferson to Dr. John P. Emmet, May 2, 1826, discovered in Nathaniel Grossman, The Sheer Joy of Celestial Mechanics:

[…] consider that we do not expect our schools to turn out their alumni already on the pinnacles of their respective sciences; but only so far advanced in each as to be able to pursue them by themselves, and to become Newtons and Laplaces by energies and perseverances to be continued throughout life.