I’ve been reading and writing about the philosophy of science a bunch in the last couple of years, so this post is a place to clip together a number of quotes and posts in one place.
Michael Strevens says the scientific method boils down to the “iron rule of explanation” that “only empirical evidence counts.”
This is a very stripped down idea. It allows for subjectivity, and it grants that there is no logically or philosophically satisfying way to decide how to interpret the results of observation or experimentation.
Here, then, in short, is the iron rule:
1. Strive to settle all arguments by empirical testing.
2. To conduct an empirical test to decide between a pair of hypotheses, perform an experiment or measurement, one of whose possible outcomes can be explained by one hypothesis (and accompanying cohort) but not the other…
How can a rule so scant in content and so limited in scope account for science’s powers of discovery? It may dictate what gets called evidence, but it makes no attempt to forge agreement among scientists as to what the evidence says. It simply lays down the rule that all arguments must be carried out with reference to empirical evidence and then steps back, relinquishing control. Scientists are free to think almost anything they like about the connection between evidence and theory. But if they are to participate in the scientific enterprise, they must uncover or generate new evidence to argue with.
Naomi Oreskes says science must be understood as social practices–and that this is a reason to trust it, not dismiss it
There is now broad agreement among historians, philosophers, sociologists, and anthropologists of science that there is no (singular) scientific method, and that scientific practice consists of communities of people, making decisions for reasons that are both empirical and social, using diverse methods. But this leaves us with the question: If scientists are just people doing work, like plumbers or nurses or electricians, and if our scientific theories are fallible and subject to change, then what is the basis for trust in science?
I suggest that our answer should be two-fold: 1) its sustained engagement with the world and 2) its social character
Four commonalities in scientific practice
From UPenn’s short Coursera course on the philosophy of science which is a nice overview:
Science is not completely unified and that there is no master method or recipe that’s appropriate in all contexts. Nevertheless, there are certain elements common of these examples… So what are the commonalities? There at least four major ones first all four of our examples involve a sophisticated forms of observation… Second, simple observation wasn’t enough… [experimentation and simulation were used as well.] Third, in each case it was multiple lines of evidence generated using different experimental and observational techniques that convinced the scientific community of the relevant results. Simplistic pictures of science such as those that are taught in high school make it seem like scientific research miraculously uncovers the truth by simply verifying one hypothesis with a single experiment. While this does happen occasionally research more often looks like the cases I’ve talked about. Research done by multiple people using different approaches that point in the same direction. Or they don’t sometimes like in the case of children’s beliefs. Philosophers call this robustness or consilience. Fourth and finally all of our examples involve the accumulation of evidence over time. Each case involves scientific understanding that improves over time from an initial sense that the answer is at hand to greater accuracy and precision and measurements and a much greater appreciation of what is genuinely needed to explain a phenomenon. While scientists never achieve certainty, this is reserved for logic and Mathematics. The accumulation of evidence especially from multiple independent sources is the key to increasing confidence that a hypothesis is true.
Tim Lewens defends scientific realism
Scientific realism is the label for the philosophical view that science is in the truth business. Scientific realism says that the sciences represent those parts of the world they deal with in an increasingly accurate way as time goes by. Scientific realists are not committed to the greedy idea that the sciences can tell us all there is to know about everything; they can happily acknowledge that there is plenty to learn from the arts and humanities. Moreover, by denying that science gives us a perfectly accurate picture of the world, scientific realists are not committed to the manifestly absurd idea that science is finished..Why Trust Science p. 85-88
A moment’s reflection suggests that scientific realism is not the only sensible and respectful way to respond to the successes of science. Perhaps we should think of scientific theories in the way we think of hammers, or computers: they are remarkably useful, but like hammers or computers they are mere tools. It makes no senses to ask whether a hammer is true, or whether it accurately represents the world, and one might argue that the same goes for science: we should simply ask whether its theories are fit for their purposes…
Cutting to the chase, this chapter will argue in favor of scientific realism… First, we need to fend off… the argument from “underdetermination… [which] suggests that scientific evidence is never powerful enough to discriminate between wholly different theories about the underlying nature of the universe… Second, we need to ask whether there is any positive argument in favor of scientific realism. More or less the only argument that has ever been offered to support this view is known as the “No Miracles argument.” The basic gist of this argument is that if science were not true–if it made significant mistakes about the constituents of matter, for example–then when we acted on the basis of scientific theory, our plans would consistently go awry… Third, and finally, we must confront an argument known as the “Pessimistic Induction.” This argument draws on the historical record to suggest that theories we now think of as false have nonetheless been responsible for remarkable practical successes.”
The book is more a quick tour through the philosophy of science, and Lewens’ argument for realism was something of a detour.
Rorty says science is a tool and urges not to think of it purely with examples from physics
In [McDowell’s] picture, people like Quine (and sometimes even Sellars) are so impressed with natural science that they think that the first sort of intelligibility [associated with natural science rather than reason] is the only genuine sort.Pragmatism as anti-authoritarianism, p. 182-184
I think it is important, when discussing the achievements of the scientific revolution, to make a distinction which McDowell does not make: a distinction between particle physics, together with those microstructural parts of natural science which can easily be linked up with particle physics, and all the rest of natural science. Particle physics, unfortunately, fascinates many contemporary philosophers, just as corpuscularian mechanics fascinated John Locke…
To guard against this simpleminded and reductionistic way of thinking of non-human nature, it is useful to remember that the form of intelligibility shared by Newton’s primitive corpuscularianism and contemporary particle physics has no counterpart in, for example, the geology of plate tectonics, nor in Darwin’s or Mendel’s accounts of heredity and evolution. What we get in those areas of natural science are narratives, natural histories, rather than the subsumptions of events under laws.
So I think that McDowell should not accept the bald naturalists’ view that there is a “distinctive form of intelligibility” found in the natural sciences and that it consists in relating events by laws. It would be better to say that what Davidson calls “strict laws” are the exception in natural science–nice if you can get them, but hardly essential to scientific explanation. It would be better to treat “natural science” as a name of an assortment of useful gimmicks…
I think we would do better to rid ourselves of the notion of “intelligibility” altogether. We should substitute the notion of techniques of problem-solving. Democritus, Newton, and Dalton solved problems with particles and laws. Darwin, Gibbon, and Hegel solved others with narratives. Carpenters solve others with hammers and nails, and soldiers still others with guns.
Scientific progress is a mater of integrating more and more data into a coherent web of belief–data from microscopes and telescope with data obtained by the naked eye, data forced into the open by experiment with data with has always been lying about.Pragmatism as anti-authoritarianism p. 136
Rorty is looking to center epistemology on people. And of course in his earlier work rejects the idea that true belief is about correctly mirroring an external world. So how should we think about what seems like an external world?
The only other sense of “social construction” that I can think of is the one I referred to earlier: the sense in which bank accounts are social constructions but giraffes are not. Here the criterion is simply causal. The causal factors which produced giraffes did not include human societies, but those which produced bank accounts did.Pragmatism as anti-authoritarianism, p. 140
David Weinberger says the success of machine learning models (MLMs) challenges Western ideas about scientific laws
Our encounter with MLMs doesn’t deny that there are generalisations, laws or principles. It denies that they are sufficient for understanding what happens in a universe as complex as ours. The contingent particulars, each affecting all others, overwhelm the explanatory power of the rules and would do so even if we knew all the rules. For example, if you know the laws governing gravitational attraction and air resistance, and if you know the mass of a coin and of Earth, and if you know the height from which the coin will be dropped, you can calculate how long it will take the coin to hit the ground. That will likely be enough to meet your pragmatic purpose. But the traditional Western framing of it has overemphasised the calm power of the laws. To apply the rules fully, we would have to know every factor that has an effect on the fall, including which pigeons are going to stir up the airflow around the tumbling coin and the gravitational pull of distant stars tugging at it from all directions simultaneously. (Did you remember to include the distant comet?) To apply the laws with complete accuracy, we would have to have Laplace’s demon’s comprehensive and impossible knowledge of the Universe.
That’s not a criticism of the pursuit of scientific laws, nor of the practice of science, which is usually empirical and sufficiently accurate for our needs – even if the degree of pragmatic accuracy possible silently shapes what we accept as our needs. But it should make us wonder why we in the West have treated the chaotic flow of the river we can’t step into twice as mere appearance, beneath which are the real and eternal principles of order that explain that flow. Why our ontological preference for the eternally unchanging over the eternally swirling water and dust?
One I read but left out was Steven Pinker’s Rationality which I won’t try to sum up here in part because it’s not about science per se.
I guess having clipped all that together I’ll end with some posts I’ve done in the past few years on or related to epistemology:
- What evidence-based thinking leaves out
- A good paragraph on theory
- On explanation
- Theory and replication
- Notes on theory, evidence, and social science
- Evidence in public policy
- The social side of science
- Social learning
- More on social epistemology
- Objectivity as a social accomplishment
- Thinking clearly
- Trusting expertise
- Analysis vs. science
- The epistemology of Wikipedia
- Triangulating the truth
- Only empirical evidence counts
- The iron rule of explanation
- A limited version of objectivity worth defending
- Opinions, bias, explanation and journalism
- The boundary between opinion and expertise
- Econ 101 and model thinking
- 3 very different views of economic models
- More on how to think about economic models
- Data and theory in economics
- Durkheim on empiricism in economics
- Piketty on models