Silver’s Mining Playbook (data mining, that is)

Having left the New York Times last year, Nate Silver has now relaunched FiveThirtyEight. I love Silver’s work, and I think his contribution to the American political discourse is invaluable. His push for “data journalism” is timely and necessary. But then, I would advocate a data-driven approach to most areas of life.

When it comes to the philosophy of science, however, Silver could and should be more sophisticated. This bothered me about his intriguing but flawed book “The Signal and the Noise” (look out for a brief review on this blog in the future), and it became apparent again in this piece introducing the new FiveThirtyEight:

Suppose you did have a credible explanation of why the 2012 election, or the 2014 Super Bowl, or the War of 1812, unfolded as it did. How much does this tell you about how elections or football games or wars play out in general, under circumstances that are similar in some ways but different in other ways?

These are hard questions. No matter how well you understand a discrete event, it can be difficult to tell how much of it was unique to the circumstances, and how many of its lessons are generalizable into principles. But data journalism at least has some coherent methods of generalization. They are borrowed from the scientific method. Generalization is a fundamental concern of science, and it’s achieved by verifying hypotheses through predictions or repeated experiments.

The first of these hyperlinks is to the Stanford Encyclopedia of Philosophy’s entry on scientific progress, and the second is to the entry on Karl Popper. Both are problematic – let me explain.

Popper is famous for advocating the view that there is no such thing as verification. Contrary to what generations of scientists and philosophers of science thought, Popper argued, there is in fact no way to show that some generalization such as “all ravens are black” is true, or even likely to be true. On Popper’s account, all you can do is to refute false generalizations. A hundred black ravens do not show that all ravens are black. Nor do a thousand, or a million. However, a single white raven shows conclusively that “all ravens are black” is false. It is debatable whether this works as a general method of science: The philosophical consequences of such a view are severe, and it is pretty clear that scientists do not actually think like this. But one thing is certain: “verifying hypotheses through prediction or repeated experiments” is not a good characterization of Popper’s position.

Modern opinion on the role of generalizations in science is more divided. Certainly they play a role. But it is doubtful that science’s main goal is to search for generalizations, since these are often not too interesting. Take the above example of “all ravens are black”. This is known not to be true, but suppose it were: Would it be very interesting? We would still not know whether it just happens to be true, or whether there is something lawful about it. I would argue that the goal of science is different: it is to understand causal mechanisms. To return to the ravens, we want to know in detail how the causal mechanisms of raven pigmentation work. This will give us an understanding of why most ravens are black, and also of how the mechanisms of pigmentation can change to produce differently colored ravens. Knowledge of causal mechanisms is far more insightful and useful than knowledge about generalizations. But the transition from generalizations to causal mechanisms is one of the great challenges for data mining approaches such as Silver’s.

(Thanks to my friend Fabio Molo for drawing my attention to Silver’s piece, and to Tim Räz for paronomastic help with the title.)

Primates and Philosophers: Read monkeys for preexistence

Darwin opened his “M” notebook in the summer of 1838, when he had already formulated the thesis of common descent but not yet the mechanism of natural selection. The “M” notebook was dedicated to “metaphysical” considerations (note well: the modern usage of “metaphysical” differs). Speaking very broadly, it explored the evolutionary history of human psychological traits. On page 128, we find Darwin’s at his most quotable:

Plato says in Phaedo that our “necessary ideas” arise from the preexistence of the soul, are not derivable from experience — read monkeys for preexistence.

Ever since Darwin it has been evident that much of our emotional and cognitive furniture must be explained by our evolutionary history. This is one of the most philosophically significant aspects of evolutionary biology, but also one of the hardest to explore empirically.

Frans de Waal’s Primates and Philosophers: How Morality Evolved is a modern continuation of Darwin’s “M” project: a reflection by a leading primate researcher on the evolutionary origins of our moral sentiments. It is tremendously enjoyable. I can give it no higher recommendation than to note that I have already ordered more books by the author. De Waal argues that the “building blocks” of human morality can be found in our primate relatives and are the results of evolutionary processes such as reciprocal altruism, kin selection and perhaps (de Waal is skeptical) group selection. This is of course not unique: The value of the book lies in its copious and lucidly presented empirical material on the morally significant behavior of primates. De Waal argues forcefully and intelligently that moral behavior is not a thin “veneer” of good behavior plastered onto a brutish, selfish psychological core. Instead, acting morally is as human (or primately) as anything.

The book also includes comments by a number of philosophers. These are not as engaging as the science (although this may reflect only my interests), but they are useful. I particularly liked Philip Kitcher’s contribution. He argues that the “veneer theory” of human morality, which de Waal attacks at length, may be a straw man: Who really believes that morality is a purely cultural layer on top of a selfish underlying biology? I suspect that de Waal needed some sort of dialectic to get going with his argument, but “veneer theory” may not be a good choice, and I rather doubt that he is being fair to those he names as exponents of the view (such as Thomas Henry Huxley and Richard Dawkins). A more productive sparring partner might be one of the following positions: (1) The assumption (which may be prevalent among philosophers) that morality is a matter of reason and not of evolved emotions; or (2) the charge that de Waal’s primate research is merely “descriptive” and so has no bearing on morality, which is understood to be “normative”.

As a separate point, Kitcher argues that it is insufficient to speak of the foundation of morality in “altruism”: different dimensions of altruism must be distinguished in order for the “building blocks” notion to be made precise. I think this is a useful conceptual clarification. It lays groundwork for the continuation of the exciting and difficult empirical project.

The elephant in the room does not get much discussion, perhaps wisely: It is the conclusion (which I find nearly inescapable, and de Waal might agree) that an understanding of our moral sentiments is all there is, or almost all there is, to understanding the foundations of ethics and morality. For now, I leave it to Hume and Ruse and Wilson to argue for this – but read also Peter Singer’s contribution to the book in hand.

Against what method? (Or: Feyerabend in context)

In December 2012 the Blogosphere and Twitterverse became agitated about an opinion piece by Brian Cox and Robin Ince in the New Statesman: Politicians must not elevate mere opinion over science. Cox and Ince were immediately criticized by members of the science studies community such as Rebekkah Higgith and Jack Stilgoe. I don’t wish to get into the many issues of this debate, but I have a straightforward point to make about one aspect of it. It concerns the term “scientific method”.

Cox and Ince refer to the “scientific method” (without going into much detail) and are taken to task for this. For instance, Higgith writes:

[T]here are many scientific methods and many, when studied in detail, are not particularly methodological.

If Twitter is any indication, mentioning the “scientific method” is considered a sign of naiveté in the science studies scene. Jon Butterworth summarizes this nicely (also in the Guardian) when he says that talking about scientific method is “apparently not the done thing”. True! But where in the technical literature do we find the roots of the apparently deeply held belief that there is no such thing as scientific method, or that it is in any case “not particularly methodological”?

My best guess is that the denial of scientific method traces back in some way to Paul Feyerabend’s famous Against Method. Popularly associated with the slogan “anything goes”, Feyerabend’s book used historical cases to argue that a number of mid-20th-century philosophical beliefs about scientific method are wrong, or at least do not hold universally. These include: the belief that there is one single method that regulates all scientific epistemology; the belief that falsification plays a key role in the progress of science; the belief that ad hoc hypotheses are condemned and rarely occur in good science; the belief that replacing theories always have more empirical content than replaced theories.

Without a doubt Feyerabend’s book was an important milestone. It pointed out many serious problems with widespread mid-20th-century views of scientific epistemology. But it is important to understand that Feyerabend’s argument was more local than his title suggests. The book was not some ingenious, grand reductio argument that showed that no such thing as scientific method can possibly exist. It mostly showed that old proposals of scientific method — then dominant, but now largely abandoned — don’t hold water. Since these old conceptions were largely the product of non-naturalistic, ahistorical armchair philosophizing, this should not be too surprising.

So Feyerabend’s Against Method does not license sweeping claims against scientific method, and if seen in context it should not give comfort to social constructivists. The best explanation of the massive success of the empirical sciences remains the assumption that its theories have some special relationship with nature. In brief, scientists are great at epistemology! The hard problem, however, is to describe and understand the process. Peter Lipton summarized this nicely in his 2004 Medawar Lecture:

It is one thing to be expert at distinguishing grammatical from ungrammatical strings of words in one’s native tongue; it is something quite different to be able to specify the principles by which this discrimination is made. The same applies to science: it is one thing to be a good scientist; it is something quite different to be good at giving a general description of what scientists do. Scientists are not good at the descriptive task. This is no criticism, since their job is to do the science, not to talk about it.

Lipton’s next remark is so good as to deserve special emphasis:

Philosophers of science are not very good at describing science either, and this is more embarrassing, since this is their job.

But the difficulty of the task is no indication of its hopelessness. That would be a bit like denying that organisms grow on the grounds that the causes and mechanisms of developmental biology are incompletely understood.

95 theses on the church door

I’ve uploaded a version of my new talk “Towards a methodology for integrated history and philosophy of science” (with Tim Räz). If it seems rather programmatic, then that’s because it is intended that way.

The talk begins with a version of my “fundamental argument” for an integrated history and philosophy of science. It then proceeds to a discussion of how the methodological problems of the HPS project can be approached in practice.