Life, complexity, and evolution

Next to the purely religious arguments -- those that boil down to "it's in the Bible, so I believe it" -- the most common objection I hear to the evolutionary model is that "you can't get order out of chaos."

Or -- which amounts to the same thing -- "you can't get complexity from simplicity."  Usually followed up by the Intelligent Design argument that if you saw the parts from which an airplane is built, and then saw an intact airplane, you would know there had to be a builder who put the parts together.  This is unfortunately often coupled with some argument about how the Second Law of Thermodynamics (one formulation of which is, "in a closed system, the total entropy always increases") prohibits biological evolution, which shows a lack of understanding both of evolution and thermodynamics.  For one thing, the biosphere is very much not a closed system; it has a constant flow of energy through it (mostly from the Sun).  Turn that energy source off, and our entropy would increase post-haste.  Also, the decrease in entropy you see within the system, such as the development of an organism from a single fertilized egg cell, does increase the entropy as a whole.  In fact, the entropy increase from the breakdown of the food molecules required for an organism to grow is greater than the entropy decrease within the developing organism itself.

Just as the Second Law predicts.

So the thermodynamic argument doesn't work.  But the whole question of how you get complexity in the first place is not so easily answered.  On its surface, it seems like a valid objection.  How could we start out with a broth of raw materials -- the "primordial soup" -- and even with a suitable energy source, have them self-organize into complex living cells?

Well, it turns out it's possible.  All it takes -- on the molecular, cellular, or organismal level -- is (1) a rule for replication, and (2) a rule for selection.  For example, with DNA, it can replicate itself, and the replication process is accurate but not flawless; the selection process comes in with the fact that some of those varying DNA configurations are better than others at copying themselves, so those survive and the less successful ones don't.  From those two simple rules, things can get complex fast.

But to take a non-biological example that is also kind of mindblowing, have you heard of British mathematician John Horton Conway's "Game of Life?"

In the 1960s Conway became interested in a mathematical concept called a cellular automaton.  The gist, first proposed by Hungarian mathematician John von Neumann, is to look at arrays of "cells" that then can interact with each other by a discrete set of rules, and see how their behavior evolves.  The set-up can get as fancy as you like, but Conway decided to keep it really simple, and came up with the ground rules for what is now called his "Game of Life."  You start out with a grid of squares, where each square touches (either on a side or a corner) eight neighboring cells.  Each square can be filled ("alive") or empty ("dead").  You then input a starting pattern -- analogous to the raw materials in the primordial soup -- and turn it loose.  After that, four rules determine how the pattern evolves:

1. Any live cell that has fewer than two live neighbors dies.
2. Any live cell that has two or three live neighbors lives to the next round.
3. Any live cell that has four or more live neighbors dies.
4. Any dead cell that has exactly three live neighbors becomes a live cell.

Seems pretty simple, doesn't it?  It turns out that the behavior of patterns in the Game of Life is so wildly complex that it's kept mathematicians busy for decades.  Here's one example, called "Gosper's Glider Gun":

Some start with as few as five live cells, and give rise to amazingly complicated results.  Others have been found that do some awfully strange stuff, like this one, called the "Puffer Breeder":

What's astonishing is not only how complex this gets, but how unpredictable it is.  One of the most curious results that has come from studying the Game of Life is that some starting conditions lead to what appears to be chaos; in other cases, the chaos settles down after hundreds, or thousands, of rounds, eventually falling into a stable pattern (either one that oscillates between two or three states, or produces something regular like the Glider Gun).  Sometimes, however, the chaos seems to be permanent -- although because there's no way to carry the process to infinity, you can't really be certain.  There also appears to be no way to predict from the initial state where it will end up ahead of time; no algorithm exists to take the input and determine what the eventual output will be.  You just have to run the program and see what happens.

In fact, the Game of Life is often used as an example of Turing's Halting Problem -- that in general there is no way to be certain that a given algorithm will arrive at a solution in a finite number of steps.  This theorem arises from such mind-bending weirdness as the Gödel Incompleteness Theorem, which proved rigorously that within mathematics, there are true statements that cannot be proven and false statements that cannot be disproven.  (Yes -- it's a proof of unprovability.)

All of this, from a two-dimensional grid of squares and four rules so simple a fourth-grader could understand them.

Now, this is not meant to imply that biological systems work the same way as an algorithmic mathematical system; just a couple of weeks ago, I did an entire post about the dangers of treating an analogy as reality.  My point here is that there is no truth to the claim that complexity can't arise spontaneously from simplicity.  Given a source of energy, and some rules to govern how the system can evolve, you can end up with astonishing complexity in a relatively short amount of time.

People studying the Game of Life have come up with twists on it to make it even more complicated, because why stick with two dimensions and squares?  There are ones with hexagonal grids (which requires a slightly different set of rules), ones on spheres, and this lovely example of a pattern evolving on a toroidal trefoil knot:

Kind of mesmerizing, isn't it?

The universe is a strange and complex place, and we need to be careful before we make pronouncements like "That couldn't happen."  Often these are just subtle reconfigurations of the Argument from Ignorance -- "I don't understand how that could happen, therefore it must be impossible."  The natural world has a way of taking our understanding and turning it on its head, which is why science will never end.  As astrophysicist Neil deGrasse Tyson explained, "Surrounding the sea of our knowledge is a boundary that I call the Perimeter of Ignorance.  As we push outward, and explain more and more, it doesn't erase the Perimeter of Ignorance; all it does is make it bigger.  In science, every question we answer raises more questions.  As a scientist, you have to become comfortable with not knowing.  We're always 'back at the drawing board.'  If you're not, you're not doing science."

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Overlooking simplicity

In the Tao Te Ching, Chinese philosopher (and founder of Taoism) Lao Tse writes, "To attain knowledge, add things every day; to attain wisdom, remove things every day."

There are a couple of interesting pieces to this concept.  First, that knowledge does not necessarily confer wisdom.  The implication is that knowledge (by itself) is less desirable than understanding, and understanding less desirable than wisdom.  If so, this definitely has some bearing on how science is taught in public schools -- often as a list of vocabulary words and definitions that do little more than scratch the surface of what's out there to learn.

Second, that doing a mental decluttering is better than trying to figure things out by jamming more stuff in.  Here, I'm reminded of what happens in my fiction writing when I'm at an impasse.  Slamming my fists against the obstacle almost never works; what frequently does is doing something else entirely, especially something stress-clearing like going for a run or playing with my dogs.  As counterintuitive as it might be, it seems like ceasing to think about the problem at all frees my brain up to figure out a solution.

How exactly that works on a neurophysiological level, I have no idea.

Lao Tse by Nicholas Roerich (1943) [Image is in the Public Domain]

As more support for Lao Tse's observation, consider the paper in Nature this week called, "People Systematically Overlook Subtractive Changes," by Gabrielle Adams, Benjamin Converse, Andrew Hales, and Leidy Klotz of the University of Virginia, which looked at another facet of this same issue -- that when approaching a solution to a complex problem, people often fail to consider solutions that require removing pieces of it or ceasing to do certain actions.  The authors write:

Improving objects, ideas or situations—whether a designer seeks to advance technology, a writer seeks to strengthen an argument or a manager seeks to encourage desired behaviour—requires a mental search for possible changes.  We investigated whether people are as likely to consider changes that subtract components from an object, idea or situation as they are to consider changes that add new components.  People typically consider a limited number of promising ideas in order to manage the cognitive burden of searching through all possible ideas, but this can lead them to accept adequate solutions without considering potentially superior alternatives.  Here we show that people systematically default to searching for additive transformations, and consequently overlook subtractive transformations.  Across eight experiments, participants were less likely to identify advantageous subtractive changes when the task did not (versus did) cue them to consider subtraction, when they had only one opportunity (versus several) to recognize the shortcomings of an additive search strategy or when they were under a higher (versus lower) cognitive load.  Defaulting to searches for additive changes may be one reason that people struggle to mitigate overburdened schedules, institutional red tape, and damaging effects on the planet.

We're so well-trained by years and years of education that the way to find a solution to a problem is to throw more stuff at it that we don't even think of looking at solutions that require simplification.

"Additive ideas come to mind quickly and easily, but subtractive ideas require more cognitive effort," study co-author Benjamin Converse said, in an interview with Science Daily.  "Because people are often moving fast and working with the first ideas that come to mind, they end up accepting additive solutions without considering subtraction at all."

Now, there's a caveat here; not all problems have simple solutions.  When I was a teacher, I used to call this the "why don't we just...?" approach.  I remember students saying, "Why don't we just use chemical reactions that absorb carbon dioxide to fix climate change?" (it's completely unfeasible to do this on a large enough scale to help), and "why don't we just pass laws protecting wilderness areas and make mass deforestation illegal?" (not only does this run afoul of private ownership and eminent domain laws, it causes problems with resource acquisition, and ignores the fact that most of the threatened wilderness in the world is outside of the United States and therefore out of our jurisdiction -- not to mention the elephant in the room of global, societally locked-in wealth inequity as the root problem).  

Complex problems rarely have simple solutions.

But the basic idea here is that the answer doesn't always lie in fixing things by doing more stuff, and the human mind doesn't tend to see those kinds of solutions as easily as ones that require further or more intense action.

So give it a try.  When you're facing a difficult problem, give a shot to a Marie-Kondo-esque simplification approach.  What could you remove (or stop doing) that might help solve the problem?  Maybe a mental decluttering would help in a lot of realms other than overcoming writers' block.

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If, like me, you love birds, I have a book for you.

It's about a bird I'd never heard of, which makes it even cooler.  Turns out that Charles Darwin, on his epic voyage around the world on the HMS Beagle, came across a species of predatory bird -- the Striated Caracara -- in the remote Falkland Islands, off the coast of Argentina.  They had some fascinating qualities; Darwin said they were "tame and inquisitive... quarrelsome and passionate," and so curious about the odd interlopers who'd showed up in their cold, windswept habitat that they kept stealing things from the ship and generally making fascinating nuisances of themselves.

In A Most Remarkable Creature: The Hidden Life and Epic Journey of the World's Smartest Birds of Prey, by Jonathan Meiberg, we find out not only about Darwin's observations of them, but observations by British naturalist William Henry Hudson, who brought some caracaras back with him to England.  His inquiries into the birds' behavior showed that they were capable of stupendous feats of problem solving, putting them up there with crows and parrots in contention for the title of World's Most Intelligent Bird.

This book is thoroughly entertaining, and in its pages we're brought through remote areas in South America that most of us will never get to visit.  Along the way we learn about some fascinating creatures that will make you reconsider ever using the epithet of "birdbrain" again.

[Note: if you purchase this book using the image/link below, part of the proceeds goes to support Skeptophilia!]