John Maynard Smith on reductive vs effective thinking about evolution

“The logic of animal conflict” — a 1973 paper by Maynard Smith and Price — is usually taken as the starting for evolutionary game theory. And as far as I am an evolutionary game theorists, it influences my thinking. Most recently, this thinking has led me to the conclusion that there are two difference conceptions of evolutionary games possible: reductive vs. effective. However, I don’t think that this would have come as much of a surprise to Maynard Smith and Price. In fact, the two men embodied the two different ways of thinking that underlay my two interpretations.

I was recently reminded of this when Aakash Pandey shared a Web of Stories interview with John Maynard Smith. This is a 4 minute snippet of a long interview with Maynard Smith. In the snippet, he starts with a discussion of the Price equation (or Price’s theorem, if you want to have that debate) but quickly digresses to a discussion of the two kinds of mathematical theories that can be made in science. He identifies himself with the reductive view and Price with the effective. I recommend watching the whole video, although I’ll quote relavent passages below.

In this post, I’ll present Maynard Smith’s distinction on the two types of thinking in evolutionary models. But I will do this in my own terminology to stress the connections to my recent work on evolutionary games. However, I don’t think this distinction is limited to evolutionary game theory. As Maynard Smith suggests in the video, it extends to all of evolutionary biology and maybe scientific modelling more generally.

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Heuristic models as inspiration-for and falsifiers-of abstractions

Last month, I blogged about abstraction and lamented that abstract models are lacking in biology. Here, I want to return to this.

What isn’t lacking in biology — and what I also work on — is simulation and heuristic models. These can seem abstract in the colloquial sense but are not very abstract for a computer scientist. They are usually more idealizations than abstractions. And even if all I care about is abstract models — which I can reasonably be accused of at times — then heuristic models should still be important to me. Heuristics help abstractions in two ways: portfolios of heuristic models can inspire abstractions, and single heuristic models can falsify abstractions.

In this post, I want to briefly discuss these two uses for heuristic models. In the process, I will try to make it a bit more clear as to what I mean by a heuristic model. I will do this with metaphors. So I’ll produce a heuristic model of heuristic models. And I’ll use spatial structure and the evolution of cooperation as a case study.

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As a scientist, don’t speak to the public. Listen to the public.

There is a lot of advice written out there for aspiring science writers and bloggers. And as someone who writes science and about science, I read through this at times. The most common trend I see in this advice is to make your writing personal and to tell a story, with all the drama and plot-twists of a good page-turner. This is solid advise for good writing, one that we shouldn’t restrict to writing about science but also for writing the articles that are science. That would make reading and writing as a scientist (two of our biggest activities) much less boring. Yet we don’t do this. More importantly, we put up with reading hundreds of poorly written, boring papers.

So if scientists put up with awful writing, why do we have to write better for the public? I think that the answer to this reveals something very important the role of science in society; who science serves and who it doesn’t. This affects how we should be thinking about activities like ‘science outreach’.

In this post, I want to put together some thoughts that have been going through my mind on funding, science and society. These are mostly half-baked and I am eager to be corrected. More importantly, I am hoping that this encourages you, dear reader, to share any thoughts that this discussion sparks.

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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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A month in papers: mostly philosophy of biology

I’ve seen a number of people that have aimed for reading one paper a day for every day of the year. Unfortunately, I am not great at new years resolutions, and would never be able to keep pace for all 365 days. Instead, in April I tried a one paper a day challenge for the month. I still came up short, only finishing 24 of 30 papers. But I guess that is enough for one paper per weekday.

As I went along, I posted tweet-length summaries in a long thread. In this post, I want to expand on and share what I read in April. And in the future, I think I’ll transform the month-goals into week goals of five papers per week. Just to avoid colossal twitter threads. I tried that last week, for example. But I don’t think I’ll end up making those into posts. Although, as happened in April, they might inspire thematic posts.

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Token vs type fitness and abstraction in evolutionary biology

There are only twenty-six letters in the English alphabet, and yet there are more than twenty-six letters in this sentence. How do we make sense of this?

Ever since I first started collaborating with David Basanta and Jacob Scott back in 2012/13, a certain tension about evolutionary games has been gnawing at me. A feeling that a couple of different concepts are being swept up under the rug of a single name.[1] This feeling became stronger during my time at Moffitt, especially as I pushed for operationalizing evolutionary games. The measured games that I was imagining were simply not the same sort of thing as the games implemented in agent-based models. Finally this past November, as we were actually measuring the games that cancer plays, a way to make the tension clear finally crystallized for me: the difference between reductive and effective games could be linked to two different conceptions of fitness.

This showed a new door for me: philosophers of biology have already done extensive conceptual analysis of different versions of fitness. Unfortunately, due to various time pressures, I could only peak through the keyhole before rushing out my first draft on the two conceptions of evolutionary games. In particular, I didn’t connect directly to the philosophy literature and just named the underlying views of fitness after the names I’ve been giving to the games: reductive fitness and effective fitness.

Now, after a third of a year busy teaching and revising other work, I finally had a chance to open that door and read some of the philosophy literature. This has provided me with a better vocabulary and clearer categorization of fitness concepts. Instead of defining reductive vs effective fitness, the distinction I was looking for is between token fitness and type fitness. And in this post, I want to discuss that distinction. I will synthesize some of the existing work in a way that is relevant to separating reductive vs. effective games. In the process, I will highlight some missing points in the current debates. I suspect this points have been overlooked because most of the philosophers of biology are focused more on macroscopic organisms instead of the microscopic systems that motivated me.[2]

Say what you will of birds and ornithology, but I am finding reading philosophy of biology to be extremely useful for doing ‘actual’ biology. I hope that you will, too.

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