For the past few days I’ve been trying to find a way to blog about my recent experiences with my climbing wall job, chess, cultural evolution class, other classes, research, and the stray thoughts I’ve been having on success. It wasn’t until watching Matt Damon in The Martian late last night that I found a unifying factor: problems. Not really problems themselves, not the kind you complain about, but about solving problems, and what that means to me and to humanity. You see, among many other unique behaviors, humans are the only creatures that solve problems for fun. Think about that for a moment. Every single day, people invent problems just so they or others can solve them. Taken one way, that’s the entirety of the scientific process. But solving problems is even more universal than that. We solve crossword puzzles, play games, make models. We create problems where everybody loses even if done perfectly and problems where everybody gains something even with flaws. There’s no debate: humans are problem solvers. Why? To make a problem out of problem-solving itself, why is it so common and core to our being that we do this? Why do we create problems to solve when we’ve already got enough problems to start with? Why do we get a little shot of pleasure each time we do solve some problem? The ending of The Martian catalyzed this question in my head, and when I compare it to the ideas I’m learning from my cultural evolution class, it makes perfect sense. We solve problems so we can solve problems. I know, I know. Circular logic is cheating. Let me explain.
If you haven’t seen The Martian yet, (it came out two days ago so there’s a good chance you haven’t) be warned that there might be some mild spoilers
littered throughout this post in the last three paragraphs. Tread carefully, and proceed at your own risk. No matter what I say, I’m sure the movie will be an excellent experience anyway. Maybe my perspective on problem-solving will even enhance the meaning for you.
Yesterday, I set my first two bouldering routes on the climbing wall at OU’s gym. I consider this a small yet gratifying milestone in my development as a climber, as many casual climbers never actually set a route. “Setting a route” basically means screwing different hand- and footholds into a climbing wall that a climber would then attempt to climb. Routes, or “problems” as they’re called specifically for bouldering, vary in difficulty, and when setting, you have to keep in mind what sort of difficulty you want your climbers to experience. To be a good setter, you have to imagine any sort of body type and climbing style that would be on your route, either accounting for or eliminating varying ways to ascend given your particular arrangement of holds. In effect, you’re tailoring a problem so that it becomes and achievable challenge for your target. This is a very interesting goal, and the first step towards understanding why we solve problems. Why shouldn’t I make all climbing routes simple if I have the materials to do so? Why shouldn’t I make them as difficult as the things they’ll find outdoors? That way, if they can do it in here then they can do what they need to out there. Instead, I set somewhere in between. I create a scenario that, should they meet the challenge, will expand their boundaries slightly, incrementally. This is why we create problems. With problems we control and devise, we can create intermediate steps between initial ability and the desired difficulty.
There’s an important distinction I need to make here. With climbing wall problem setting, I’m establishing intermediate practice problems. This is different from, say, research work, which often involves intermediates which must be solved in order to proceed. The latter is most important in mature, higher-risk situations, whereas the former takes precedent during development. Thus, there must be some mechanism by which practice problems facilitate success in practical problems. By extrapolation, those who spend longer in practice should eventually be able to complete more complex and abstract practical problems. Okay, this is all very good in theory, with assumption piled on assumption. Is there some empirical evidence to support such a convenient theory?
I think college is my best evidence for this. To paraphrase one of my professors, they can’t determine right now whether we’ll someday forget something that results in a nuclear plant exploding, so exams are the best substitute they have for evaluation. That seems like a relatively pathetic intermediate, even if it’s just practice, but it serves its purpose. As we advance within the degree, our exams come closer and closer to modeling the full complexity of a nuclear plant (for example). There are two important caveats to these exams that set them apart from practical problems. The first is that they’re virtual. This means that even in utter failure, no lives are at stake. The second is that they have increased tolerance. While leaving one detail unattended may blow up a plant, it won’t necessarily fail you out of nuclear physics 101. Together, these concessions of practice problems mean that we can learn complex and high-risk systems with relative safety and opportunity to learn from failure.
Sounds great, Nate! So what’s the downside? Well, students don’t exactly receive a living wage. Those who do drop out or, say, graduate with a bachelor’s degree and move into their career, will be solving practical problems sooner in life, supporting themselves and furthering their practical goals. But those who continue to learn in graduate school will be able to solve more complex problems in the future, as explained above. For their added time as a resource drain in learning, they’ll eventually work more efficiently. Which approach is better is not a judgement I would like to make any further than asserting that in our current state, both methods are necessary. As we’ve learned from both multi-billionaire dropouts (I believe Steve Jobs is one such example) and universe-defining eternal students (see Albert Einstein), both options can lead to incredible success.
So I’ve covered why we create practice problems, and how solving practice problems contributes to eventually solving practical problems. Are you still with me? Now all we have left is why we create practical problems and why we solve those. These questions are actually more straightforward.
There are two ways in which we “create” practical problems. I’ve got create in quotations because, by definition, it is counter-intuitive to make new practical problems. Attempting to do so would effectively result in a practice problem. Anyway, the first means of creation is asking a legitimately unanswered question. This is the common approach to both science and philosophy. In engineering, such questions tend to come unbidden. The other main method is to break a large problem into smaller practical problems. For example, the research project I’m working on hopes to determine how cancer metastasizes to bone. That’s one large, complex question that starts with figuring out how to create a model environment that exposes bone cells to viable and relevant cancer cells. Even more questions break down from that, such as how we can model 3D cancer and bone formations so that they’ll better represent the actual states of both. Eventually we get down to the tiny little question I’m solving right now, which is finding whether a certain protein will contribute to holding live cells onto scaffolds effectively enough to resist the shear stress they’d experience in a body.
Why do we break down large problems? The answer’s as easy as walking. Each step brings us closer to our goal, so the final step is only about as difficult as the first, rather than one giant, impossible leap.
Why do we ask questions we have no answer to?
I only have a partial answer to that, with the assistance of both my cultural evolution class and The Martian. This is wherethe Martian spoilers are going to be, so brace yourself.
Richard Dawkins suggested very briefly in his book The Selfish Gene that the complexity of the human mind exists because it makes us very adept at modeling situations in our head, which means we can mentally test out our responses to situations. If we can imagine each result accurately, the it provides with a very safe and relatively efficient means of ensuring our survival. Of course, we can’t always imagine results perfectly–that would be absurd. Thus we assist ourselves with both practice and practical problems to bring our mental state closer to reality. This is one of the ultimate goals of science: develop a thorough understanding of what the heck is actually going on all around us.
According to Alex Mesoudi in Cultural Evolution, (I’m liberally paraphrasing and extrapolating here) cultural evolution stands on the shoulders of genetic evolution, allowing us to solve problems intentionally whereas they’d otherwise be solved using random natural selection. This is an important step in evolution. Natural selection, the governing rule of Darwinism, means that any biological solution to any problem develops randomly and then succeeds because it is simply better than others. With the uniquely human problem-solving approach that results from cultural evolution, we actively attack the problems that would otherwise depend on nearly infinite possibilities of random mutations to solve. This breaks with Darwinism in that it actually results in a partially Lamarckian evolution In effect, problem-solving behavior makes us billions of times more efficient at evolution. Here’s the line from The Martian that brought it all together for me: (shoot, I really do have to paraphrase this one since none of the good quotes from the movie are online yet) “You get to work. You solve one problem, and then you solve another problem. If you solve enough problems, you get to live.”
Before Watney got to Mars, he solved a LOT of practice problems. This is shown by the fact that he was an advanced botanist and that he was chosen to be one of six astronauts on a trip to Mars. Then on Mars, he was faced with the ultimate series of practical problems. Because of his training with the practice problems, and because he wanted to live, he solved each of these problems. That’s it. We solve problems because we want to survive.
What a novel of a blog post! I’m glad you read it through, and I hope it was worth the read. I think I did an exceptionally good job of incorporating all my thoughts except for chess, which, you can imagine, is the ultimate glorification of a practice problem (originally used to represent war, how bizarre). Leave a comment if you’re intrigued or there’s some glaring hole in my logic you feel compelled to call out.