Saturday, March 28, 2015
Friday, November 14, 2014
Thanks to some good work at Oxford, the paperback edition of my 2012 book is now available. (It is listed on Amazon at least, and should be on the OUP USA website soon.) As much as I wanted to, I resisted the urge to make corrections and improvements.
Monday, November 3, 2014
This week is the 2014 edition of the Philosophy of Science Association conference. A great program has been assembled here.
Due to an oversight on my part, a conflict developed, and I had to request that the program chair move the time for my talk. The talk will now be presented on Friday Nov. 7th in the 4-6pm session on Explanation. I am grateful to the program chair for accommodating this last minute request.
Title: Newton, Laplace and Salmon on Explaining the Tides
Abstract: Salmon cites Newton's explanation of the tides in support of a causal account of scientific explanation. In this paper I reconsider the details of how Newton and his successors actually succeeded in explaining several key features of the tides. It turns out that these explanations depend on elements that are not easily interpreted in causal terms. I use the explanations offered after Newton to indicate two different ways that non-causal factors can be significant for scientific explanation. In Newton's equilibrium explanation, only a few special features of the tides can be explained. A later explanation deploys a kind of harmonic analysis to provide an informative classification of the tides at different locations. I consider the options for making sense of these explanations.
Monday, October 27, 2014
There has not been much activity here lately, but I wanted to link to two new papers of mine that tackle the vexing issue of mathematical explanation in math and in science. I try to isolate a kind of "abstract" explanation using two cases, and explore their significance.
The Unsolvability of the Quintic: A Case Study in Abstract Mathematical Explanation
Philosophers' Imprint, forthcoming.
Abstract: This paper identifies one way that a mathematical proof can be more explanatory than another proof. This is by invoking a more abstract kind of entity than the topic of the theorem. These abstract mathematical explanations are identified via an investigation of a canonical instance of modern mathematics: the Galois theory proof that there is no general solution in radicals for fifth-degree polynomial equations. I claim that abstract explanations are best seen as describing a special sort of dependence relation between distinct mathematical domains. This case study highlights the importance of the conceptual, as opposed to computational, turn of much of modern mathematics, as recently emphasized by Tappenden and Avigad. The approach adopted here is contrasted with alternative proposals by Steiner and Kitcher.
Abstract Explanations in Science
British Journal for the Philosophy of Science, forthcoming.
A previous version of this paper is online here.
Abstract: This paper focuses on a case that expert practitioners count as an explanation: a mathematical account of Plateau's laws for soap films. I argue that this example falls into a class of explanations that I call abstract explanations. Abstract explanations involve an appeal to a more abstract entity than the state of affairs being explained. I show that the abstract entity need not be causally relevant to the explanandum for its features to be explanatorily relevant. However, it remains unclear how to unify abstract and causal explanations as instances of a single sort of thing. I conclude by examining the implications of the claim that explanations require objective dependence relations. If this claim is accepted, then there are several kinds of objective dependence relations.
It remains to be seen if this "ontic" approach is the best way to go, but I believe it is a promising avenue to explore.
Saturday, September 7, 2013
Wednesday, April 24, 2013
The October 2012 issue of Mind (posted today here) has an extended discussion section where mathematical fictionalists of various stripes respond to Colyvan's earlier article "There is No Easy Road to Nominalism". The discussion concludes with a detailed reply by Colyvan. While I am a fan of neither Colyvan's explanatory indispensability argument nor its fictionalist critics, I look forward to reading this discussion and engaging with it soon!
Jody Azzouni Taking the Easy Road Out of Dodge Mind (2012) 121(484): 951-965
Otávio Bueno An Easy Road to Nominalism Mind (2012) 121(484): 967-982
Mary Leng Taking it Easy: A Response to Colyvan Mind (2012) 121(484): 983-995
David Liggins Weaseling and the Content of Science Mind (2012) 121(484): 997-1005
Stephen Yablo Explanation, Extrapolation, and Existence Mind (2012) 121(484): 1007-1029
Mark Colyvan Road Work Ahead: Heavy Machinery on the Easy Road Mind (2012) 121(484): 1031-1046
Thursday, April 11, 2013
Prominent biologist and science writer E. O. Wilson has a provocative Wall Street Journal opinion piece about the link between mathematical ability and scientific achievement. Perhaps the central ambiguity of his argument is illustrated by the two different titles the article seems to have. The browser heading is "Great Scientists Don't Need Math", while the actual title is "Great Scientist Does not Equal Good at Math". While the latter claim is almost trivial, the former claim seems very contentious. Of course, I am biased on this issue, having written a book arguing that mathematics makes several crucial contributions to the formulation and justification of our scientific knowledge. But setting that philosophical discussion aside, it is somewhat disturbing to find such a simplistic view of the way mathematics helps in science being presented by such a distinguished scientist.
Wilson's basic idea is that great scientists don't need to be good at math because they can always call on specialists in the relevant areas of mathematics. On Wilson's picture, the great scientists come up with great ideas, and these ideas are then implemented and tested via mathematical models. But the ideas themselves are completely non-mathematical:
Fortunately, exceptional mathematical fluency is required in only a few disciplines, such as particle physics, astrophysics and information theory. Far more important throughout the rest of science is the ability to form concepts, during which the researcher conjures images and processes by intuition.
Everyone sometimes daydreams like a scientist. Ramped up and disciplined, fantasies are the fountainhead of all creative thinking. Newton dreamed, Darwin dreamed, you dream. The images evoked are at first vague. They may shift in form and fade in and out. They grow a bit firmer when sketched as diagrams on pads of paper, and they take on life as real examples are sought and found.
Pioneers in science only rarely make discoveries by extracting ideas from pure mathematics. Most of the stereotypical photographs of scientists studying rows of equations on a blackboard are instructors explaining discoveries already made. Real progress comes in the field writing notes, at the office amid a litter of doodled paper, in the hallway struggling to explain something to a friend, or eating lunch alone. Eureka moments require hard work. And focus.
Ideas in science emerge most readily when some part of the world is studied for its own sake. They follow from thorough, well-organized knowledge of all that is known or can be imagined of real entities and processes within that fragment of existence. When something new is encountered, the follow-up steps usually require mathematical and statistical methods to move the analysis forward. If that step proves too technically difficult for the person who made the discovery, a mathematician or statistician can be added as a collaborator.Now, it is clear that some ideas that drive scientific discoveries are non-mathematical. But I do not see much evidence that most of these ideas are like that or that scientists should trust non-scientists to implement their ideas in mathematical terms. It is precisely at this stage that some of the most important and innovative work is done, and it is not clear to me how collaborations can work if one side, the scientist, doesn't understand what the other side, the mathematician, is doing.
See here for another critique of Wilson.