Methods and morals for mathematical modeling

About a year ago, Vincent Cannataro emailed me asking about any resources that I might have on the philosophy and etiquette of mathematical modeling and inference. As regular readers of TheEGG know, this topic fascinates me. But as I was writing a reply to Vincent, I realized that I don’t have a single post that could serve as an entry point to my musings on the topic. Instead, I ended up sending him an annotated list of eleven links and a couple of book recommendations. As I scrambled for a post for this week, I realized that such an analytic linkdex should exist on TheEGG. So, in case others have interests similar to Vincent and me, I thought that it might be good to put together in one place some of the resources about metamodeling and related philosophy available on this blog.

This is not an exhaustive list, but it might still be relatively exhausting to read.

I’ve expanded slightly past the original 11 links (to 14) to highlight some more recent posts. The free association of the posts is structured slightly, with three sections: (1) classifying mathematical models, (2) pros and cons of computational models, and (3) ethics of models.

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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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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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Cataloging a sparse year of blogging: IMO workshop and preprints

Happy 2018!

With 2017 finally behind us, TheEGG enters its 8th calendar year. This past year has been a slow one for the blog, with only 10 new articles and two posts cataloguing 2016 (on cancer and on more theoretical aspects of evolution and general modelling). Half the months were barren: I posted nothing in March, April, May, July, August, September; and only October and November saw more than one post. But those two months of activity were good. We saw the list of TheEGG authors joined by David Robert Grimes, Vincent Cannataro, and Matthew Wicker; plus the return of Robert Vander Velde.

If you’re keeping score at home, this means that I only wrote six new articles last year.

As in the past, I want to start the new year by summarizing the old.

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Cataloging a year of blogging: complexity in evolution, general models, and philosophy

Last month, with just hours to spare in January, I shared a linkdex of the 14 cancer-related posts from TheEGG in 2016. Now, as February runs out, it’s time to reflect on the 15 non cancer-specific posts from last year. Although, as we’ll see, some of them are still related to mathematical oncology. With a nice number like 15, I feel that I am obliged to divide them into three categories of five articles each. Which does make for a stretch in narrowing down themes.

The three themes were: (1) complexity, supply driven evolution, and abiogenesis, (2) general models and their features, (3) algorithmic philosophy and the social good.

And yes, two months have passed and all I’ve posted to the blog are two 2016-in-review posts. Even those were rushed and misshapen. But I promise there is more and better coming; hopefully with a regular schedule.

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Cataloging a year of cancer blogging: double goods, measuring games & resistance

Happy year of the Rooster and 2017,

This month marks the start of the 7th calendar year of updates on TheEGG. Time to celebrate and summarize the posts of the year past. In 2016 there was the same number of posts as 2015, but instead of being clustered in a period of <7 months, they were more uniformly distributed across the calendar. Every month had at least one new post, although not necessarily written by me (in the case of the single post by Abel Molina in October). There were 29 entries, one linkdex cataloging 2015, and two updates on EGT reading group 51 – 55 & 56 – 60.

In September, as part of my relocation from Tampa to Oxford, I attended the 4th Heidelberg Laureate Forum. I wrote two pieces for their blog: Alan Turing and science through the algorithmic lens and a spotlight on Jan Poleszczuk: from HLF2013 to mathematical oncology. You can read those (and more posts coming this year) on their blog. I won’t go into more detail here.

As before, this post is meant to serve as an organizing reference and a way to uncover common themes on TheEGG. A list of TL;DRs from 2016. The year was split up into four major categories: cancer, complexity & evolution, other models, and philosophy. The cancer posts make up almost half the articles from last year, and are further subdivided into three subsections: double goods game, experimental game theory, and therapy resistance. I want to focus on these cancer posts for this linkdex, and the other three categories in the next installment.

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EGT Reading Group 56 – 60

Since my last update in February, the evolutionary game theory reading group has passed another milestone with 5 more meetings over the last 4 months. We looked at a broad range of topics, from life histories in cancer to the effects of heterogeneity and biodiversity. From the definitions of fitness to analyzing digital pathology. Part of this variety came from suggested papers by the group members. The paper for EGT 57 was suggested by Jill Gallaher, EGT 58 by Robert Vander Velde, and the second paper for EGT 60 came from a tip by Jacob Scott. We haven’t yet recovered our goal of regular weekly meetings, but we’ve more than halved the time it took for these five meetings compared to the previous ones.

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