Reductionism: to computer science from philosophy

A biologist and a mathematician walk together into their joint office to find the rubbish bin on top of the desk and on fire. The biologist rushes out, grabs a fire extinguisher, puts out the blaze, returns the bin to the floor and they both start their workday.

The next day, the same pair return to their office to find the rubbish bin in its correct place on the floor but again on fire. This time the mathematician springs to action. She takes the burning bin, puts it on the table, and starts her workday.

The biologist is confused.

Mathematician: “don’t worry, I’ve reduced the problem to a previously solved case.”

What’s the moral of the story? Clearly, it’s that reductionism is “[o]ne of the most used and abused terms in the philosophical lexicon.” At least it is abused enough for this sentiment to make the opening line of Ruse’s (2005) entry in the Oxford Companion to Philosophy.

All of this was not apparent to me.

I underestimated the extent of disagreement about the meaning of reductionism among people who are saying serious things. A disagreement that goes deeper than the opening joke or the distinction between ontological, epistemological, methodological, and theoretical reductionism. Given how much I’ve written about the relationship between reductive and effective theories, it seems important for me to sort out how people read ‘reductive’.

Let me paint the difference that I want to discuss in the broadest stroke with reference to the mind-body problem. Both of the examples I use are purely illustrative and I do not aim to endorse either. There is one sense in which reductionism uses reduce in the same way as ‘reduce, reuse, and recycle’: i.e. reduce = use less, eliminate. It is in this way that behaviourism is a reductive account of the mind, since it (aspires to) eliminate the need to refer to hidden mental, rather than just behavioural, states. There is a second sense in which reductionism uses reducere, or literally from Latin: to bring back. It is in this way that the mind can be reduced to the brain; i.e. discussions of the mind can be brought back to discussions of the brain, and the mind can be taken as fully dependent on the brain. I’ll expand more on this sense throughout the post.

In practice, the two senses above are often conflated and intertwined. For example, instead of saying that the mind is fully dependent on the brain, people will often say that the mind is nothing but the brain, or nothing over and above the brain. When doing this, they’re doing at least two different things. First, they’re claiming to have eliminated something. And second, conflating reduce and reducere. This observation of conflation is similar to my claim that Galileo conflated idealization and abstraction in his book-keeping analogy.

And just like with my distinction between idealization and abstraction, to avoid confusion, the two senses of reductionism should be kept conceptually separate. As before, I’ll make this clear by looking at how theoretical computer science handles reductions. A study in algorithmic philosophy!

In my typical arrogance, I will rename the reduce-concept as eliminativism. And based on its agreement with theoretical computer science, I will keep the reducere-concept as reductionism.

Open-ended evolution on hard fitness landscapes from VCSPs

There is often interest among the public and in the media about evolution and its effects for contemporary humans. In this context, some argue that humans have stopped evolving, including persons who have a good degree of influence over the public opinion. Famous BBC Natural History Unit broadcaster David Attenborough, for example, argued a few years ago in an interview that humans are the only species who “put halt to natural selection of its own free will”. The first time I read this, I thought that it seemed plausible. The advances in medicine that we made in the last two centuries mean that almost all babies can reach adulthood and have children of their own, which appears to cancel natural selection. However, after more careful thought, I realized that these sort of arguments for the ‘end of evolution’ could not be true.

Upon more reflection, there just seem to be better arguments for open-ended evolution.

One way of seeing that we’re still evolving is by observing that we actually created a new environment, with very different struggles than the ones that we encountered in the past. This is what Adam Benton (2013) suggests in his discussion of Attenborough. Living in cities with millions of people is very different from having to survive in a prehistoric jungle, so evolutionary pressures have shifted in this new environment. Success and fitness are measured differently. The continuing pace of changes and evolution in various fields such as technology, medicine, sciences is a clear example that humans continue to evolve. Even from a physical point of view, research shows that we are now becoming taller, after the effects of the last ice age faded out (Yang et al., 2010), while our brain seems to get smaller, for various reasons with the most amusing being that we don’t need that much “central heating”. Take that Aristotle! Furthermore, the shape of our teeth and jaws changed as we changed our diet, with different populations having a different structure based on the local diet (von Cramon-Taubadel, 2011).

But we don’t even need to resort to dynamically changing selection pressures. We can argue that evolution is ongoing even in a static environment. More importantly, we can make this argument in the laboratory. Although we do have to switch from humans to a more prolific species. A good example of this would be Richard Lenski’s long-term E-coli evolution experiment (Lenski et al., 1991) which shows that evolution is still ongoing after 50000 generations in the E-coli bacteria (Wiser et al., 2013). The fitness of the E. coli keeps increasing! This certainly seems like open-ended evolution.

But how do we make theoretical sense of these experimental observations? Artem Kaznatcheev (2018) has one suggestion: ‘hard’ landscapes due to the constraints of computational complexity. He suggests that evolution can be seen as a computational problem, in which the organisms try to maximize their fitness over successive generations. This problem would still be constrained by the theory of computational complexity, which tells us that some problems are too hard to be solved in a reasonable amount of time. Unfortunately, Artem’s work is far too theoretical. This is where my third-year project at the University of Oxford comes in. I will be working together with Artem on actually simulating open-ended evolution on specific examples of hard fitness landscapes that arise from valued constraint satisfaction problems (VCSPs).

Why VCSPs? They are an elegant generalization of the weighted 2SAT problem that Artem used in his work on hard landscapes. I’ll use this blog post to introduce CSPs, VCSPs, explain how they generalize weighted 2 SAT (and thus the NK fitness landscape model), and provide a way to translate between the language of computer science and that of biology.

Local peaks and clinical resistance at negative cost

Last week, I expanded on Rob Noble’s warning about the different meanings of de novo resistance with a general discussion on the meaning of resistance in a biological vs clinical setting. In that post, I suggested that clinicians are much more comfortable than biologists with resistance without cost, or more radically: with negative cost. But I made no argument — especially no reductive argument that could potentially sway a biologist — about why we should entertain the clinician’s perspective. I want to provide a sketch for such an argument in this post.

In particular, I want to present a theoretical and extremely simple fitness landscape on which a hypothetical tumour might be evolving. The key feature of this landscape is a low local peak blocking the path to a higher local peak — a (partial) ultimate constraint on evolution. I will then consider two imaginary treatments on this landscape, one that I find to be more similar to a global chemotherapy and one that is meant to capture the essence of a targetted therapy. In the process, I will get to introduce the idea of therapy transformations to a landscape — something to address the tendency of people treating treatment fitness landscapes as completely unrelated to untreated fitness landscapes.

Of course, these hypothetical landscapes are chosen as toy models where we can have resistance emerge with a ‘negative’ cost. It is an empirical question to determine if any of this heuristic capture some important feature of real cancer landscapes.

But we won’t know until we start looking.

Causes and costs in biological vs clinical resistance

This Wednesday, on These few lines, Rob Noble warned of the two different ways in which the term de novo resistance is used by biologists and clinicians. The biologist sees de novo resistance as new genetic resistance arising after treatment has started. The clinician sees de novo resistance as a tumour that is not responsive to treatment from the start. To make matters even more confusing, Hitesh Mistry points to a further interpretation among pharmocologists: they refer to the tumour remaining after a partial but incomplete response to treatment as de novo resistant. Clearly this is a mess!

But I think this is an informative mess. I don’t think it is a matter of people accidentally overloading the same word. Instead, I think it reflects a conceptual difference in how biologists and clinicians think about resistance. A difference that is a bit akin to the difference between reductive and effective theories. It is also a difference that I had to deal with during the revisions of our recent work on measuring the games played by treatment sensitive and treatment resistance non-small cell lung cancer (Kaznatcheev et al., 2018).

Effective games from spatial structure

For the last week, I’ve been at the Institute Mittag-Leffler of the Royal Swedish Academy of Sciences for their program on mathematical biology. The institute is a series of apartments and a grand mathematical library located in the suburbs of Stockholm. And the program is a mostly unstructured atmosphere — with only about 4 hours of seminars over the whole week — aimed to bring like-minded researchers together. It has been a great opportunity to reconnect with old colleagues and meet some new ones.

During my time here, I’ve been thinking a lot about effective games and the effects of spatial structure. Discussions with Philip Gerlee were particularly helpful to reinvigorate my interest in this. As part of my reflection, I revisited the Ohtsuki-Nowak (2006) transform and wanted to use this post to share a cute observation about how space can create an effective game where there is no reductive game.

Suppose you were using our recent game assay to measure an effective game, and you got the above left graph for the fitness functions of your two types. On the x-axis, you have seeding proportion of type C and on the y-axis you have fitness. In cyan you have the measured fitness function for type C and in magenta, you have the fitness function for type D. The particular fitnesses scale of the y-axis is not super important, not even the x-intercept — I’ve chosen them purely for convenience. The only important aspect is that the cyan and magenta lines are parallel, with a positive slope, and the magenta above the cyan.

This is not a crazy result to get, compare it to the fitness functions for the Alectinib + CAF condition measured in Kaznatcheev et al. (2018) which is shown at right. There, cyan is parental and magenta is resistant. The two lines of best fit aren’t parallel, but they aren’t that far off.

How would you interpret this sort of graph? Is there a game-like interaction happening there?

Of course, this is a trick question that I give away by the title and set-up. The answer will depend on if you’re asking about effective or reductive games, and what you know about the population structure. And this is the cute observation that I want to highlight.

Plato and the working mathematician on Truth and discourse

Plato’s writing and philosophy are widely studied in colleges, and often turned to as founding texts of western philosophy. But if we went out looking for people that embraced the philosophy — if we went out looking for actual Platonist — then I think we would come up empty-handed. Or maybe not?

A tempting counter-example is the mathematician.

It certainly seems that to do mathematics, it helps to imagine the objects that you’re studying as inherently real but in a realm that is separate from your desk, chair and laptop. I am certainly susceptible to this thinking. Some mathematicians might even claim that they are mathematical platonists. But there is sometimes reasons to doubt the seriousness of this claim. As Reuben Hersh wrote in Some Proposals for Reviving the Philosophy of Mathematics:

the typical “working mathematician” is a Platonist on weekdays and a formalist on Sundays. That is, when he is doing mathematics, he is convinced that he is dealing with an objective reality whose properties he is attempting to determine. But then, when challenged to give a philosophical account of this reality, he finds it easiest to pretend that he does not believe in it after all.

What explains this discrepency? Is mathematical platonism — or a general vague idealism about mathematical objects — compatible with the actual philosophy attributed to Plato? This is the jist of a question that Conifold asked on the Philosophy StackExchange almost 4 years ago.

In this post, I want to revisit and share my answer. This well let us contrast mathematical platonism with a standard reading of Plato’s thought. After, I’ll take some helpful lessons from postmodernism and consider an alternative reading of Plato. Hopefully this PoMo Plato can suggest some fun thoughts on the old debate on discovery vs invention in mathematics, and better flesh out my Kantian position on the Church-Turing thesis.