Open-ended evolution on hard fitness landscapes from VCSPs
December 26, 2018 5 Comments
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.
Reductionism: to computer science from philosophy
December 29, 2018 by Artem Kaznatcheev 3 Comments
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.
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Filed under Commentary, Preliminary Tagged with algorithmic philosophy, philosophy of math, philosophy of science