Personal case study on the usefulness of philosophy to biology

At the start of this month, one of my favourite blogs — Dynamic Ecology — pointed me to a great interview with Michela Massimi. She has recently won the Royal Society’s Wilkins-Bernal-Medawar Medal for the philosophy of science, and to celebrate Philip Ball interviewed her for Quanta. I recommend reading the whole interview, but for this post, I will focus on just one aspect.

Ball asked Massimi how she defends philosophy of science against dismissive comments by scientists like Feynman or Hawking. In response, she made the very important point that for the philosophy of science to be useful, it doesn’t need to be useful to science:

Dismissive claims by famous physicists that philosophy is either a useless intellectual exercise, or not on a par with physics because of being incapable of progress, seem to start from the false assumption that philosophy has to be of use for scientists or is of no use at all.

But all that matters is that it be of some use. We would not assess the intellectual value of Roman history in terms of how useful it might be to the Romans themselves. The same for archaeology and anthropology. Why should philosophy of science be any different?

Instead, philosophy is useful for humankind more generally. This is certainly true.

But even for a scientist who is only worrying about getting that next grant, or publishing that next flashy paper. For a scientist who is completely detached from the interests of humanity. Even for this scientist, I don’t think we have to concede the point on the usefulness of philosophy of science. Because philosophy, and philosophy of science in particular, doesn’t need to be useful to science. But it often is.

Here I want to give a personal example that I first shared in the comments on Dynamic Ecology.
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Algorithmic lens as Alan Turing’s wider impact

Today is Alan Turing’s birthday. He would have turned 106.

It has been too long since I last wrote about him on TheEGG. Today, I want to provide an overview of some of his most important work based on my and other’s answers on this old cstheory question. This will build slightly on a post I wrote two years ago for the Heidelberg Laureate Forum, but it will share a lot of text in common.

Turing is far from obscure. Every computer scientist and programmer has heard his name. The Nobel prize of Computer Science is named after him. He has even joined the ranks of mathematicians with feature-length films. Although a film that misrepresents much history. But even outside of film, I feel that our perceptions and representations of Turing are shaped too heavily by the current boundaries and constraints of computer science. Or at least how computer science is popularly (mis)understood.

Also, it is just easier to film the building a giant machine than about proving theorems and revolutionizing how we think about the world.

As the great breadth of his work shows, Turing would not recognize the disciplinary boundaries that confine computer science to technology. Like Abel Molina, he would see many motivations for computer science, from Science and Technology to Mathematics and Philosophy to Society. Turing viewed the whole world through the algorithmic lens. A wide ambition that is sometimes lacking in modern computer science.

In this post, I want to highlight some of the aspects of the world that Turing looked at.
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Double-entry bookkeeping and Galileo: abstraction vs idealization

Two weeks ago, I wrote a post on how abstract is not the opposite of empirical. In that post, I distinguished between the colloquial meaning of abstract and the ‘true’ meaning used by computer scientists. For me, abstraction is defined by multiple realizability. An abstract object can have many implementations. The concrete objects that implement an abstraction might differ from each other in various — potentially drastic — ways but if the implementations are ‘correct’ then the ways in which they differ are irrelevant to the conclusions drawn from the abstraction.

I contrasted this comp sci view with a colloquial sense that I attributed to David Basanta. I said this colloquial sense was just that an abstract model is ‘less detailed’.

In hindsight, I think this colloquial sense was a straw-man and doesn’t do justice to David’s view. It isn’t ignoring any detail that makes something colloquially abstract. Rather, it is ignoring ‘the right sort of’ detail in the ‘right sort of way’. It is about making an idealization meant to arrive at some essence of a (class of) object(s) or a process. And this idealization view of abstraction has a long pedigree.

In this post, I want to provide a semi-historical discussion of the the difference between (comp sci) abstraction vs idealization. I will focus on double-entry bookkeeping as a motivation. Now, this might not seem relevant to science, but for Galileo it was relevant. He expressed his views on (proto-)scientific abstraction by analogy to bookkeeping. And in expressing his view, he covered both abstraction and idealization. In the process, he introduced both good ideas and bad ones. They remain with us today.

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QBIOX: Distinguishing mathematical from verbal models in biology

There is a network at Oxford know as QBIOX that aims to connect researchers in the quantitative biosciences. They try to foster collaborations across the university and organize symposia where people from various departments can share their quantitative approaches to biology. Yesterday was my second or third time attending, and I wanted to share a brief overview of the three talks by Philip Maini, Edward Morrissey, and Heather Harrington. In the process, we’ll get to look at slime molds, colon crypts, neural crests, and glycolysis. And see modeling approaches ranging from ODEs to hybrid automata to STAN to algebraic systems biology. All of this will be in contrast to verbal theories.

Philip Maini started the evening off — and set the theme for my post — with a direct question as the title of his talk.

Does mathematics have anything to do with biology?

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Abstract is not the opposite of empirical: case of the game assay

Last week, Jacob Scott was at a meeting to celebrate the establishment of the Center for Evolutionary Therapy at Moffitt, and he presented our work on measuring the effective games that non-small cell lung cancer plays (see this preprint for the latest draft). From the audience, David Basanta summarized it in a tweet as “trying to make our game theory models less abstract”. But I actually saw our work as doing the opposite (and so quickly disagreed).

However, I could understand the way David was using ‘abstract’. I think I’ve often used it in this colloquial sense as well. And in that sense it is often the opposite of empirical, which is seen as colloquially ‘concrete’. Given my arrogance, I — of course — assume that my current conception of ‘abstract’ is the correct one, and the colloquial sense is wrong. To test myself: in this post, I will attempt to define both what ‘abstract’ means and how it is used colloquially. As a case study, I will use the game assay that David and I disagreed about.

This is a particularly useful exercise for me because it lets me make better sense of how two very different-seeming aspects of my work — the theoretical versus the empirical — are both abstractions. It also lets me think about when simple models are abstract and when they’re ‘just’ toys.

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