Evolution

I first learned about evolution in elementary school. Of course, this is an overstatement; I was handed the concept without the tools to grasp it. For many years, before I could actually consider the absurdity of the idea, I thought that monkeys just became humans. How would a werehuman¹ monkey just magically adjust to human society? Why do we even keep monkeys in zoos if this is the case? Where can I go to see this happen? All of these were valid questions. None of them occurred to me.

As I took more biology-adjacent classes, I learned more about evolution and understood that humans don’t really come from monkeys and evolution takes place over a very large timescale. But, it would be the same naïveté to claim that I had learned. No, I still did not ask the questions that needed asking. Why do worker bees kill themselves to protect the beehive? Well, clearly it benefits the colony.² However, this made no sense for my understanding of evolution at the time. Clearly, asking questions is important.

Since I was curious about the actual mechanisms of evolution, I read The Selfish Gene by Richard Dawkins. Though somewhat dated, the gene’s-eye view it promotes is still mostly accepted³ and a liberal use of footnotes allows for corrections. This provided me with the tools to ask and approach the questions that needed asking. For example, a worker bee is very closely related to its peers, even more than its mother, so self-sacrifice is the best decision for the propagation of its genes. Even more than that, it allowed me to understand what evolution is: a law of the universe, not just biology.

While most associate evolution solely with biology and adopt the biases of terrestrial life, I think it is valuable to decouple it from that. Let us first discuss the conditions necessary for evolution. From the biology class I took last semester, these are:

1. Variation
2. Heritability
3. Differential success

We can observe that none of these actually require a living organism. All we need is a replicator, which is some thing that can make copies of itself (these need not be accurate). Well, not just one replicator, but a variety of them, and they have to have different levels of success at replicating. The success of a replicator could be driven by how fast it replicates, how long it survives, and how accurately it replicates. Then, naturally, the more successful replicators will propagate and make a larger and larger proportion of the population.

These replicators could also interact with one another (either directly or indirectly), so we also have to take into account the idea of an evolutionary stable state. A certain mix of replicators could be stable if population dynamics or the “invasion” of another replicator cannot force it to change. Notice that none of this actually requires “life,” and this could easily play out with simple molecules. In fact, this is the basis for many theories regarding the origin of life.

What strikes me most about this process is how it can create complexity without any intelligence. Given enough time, replicators, and luck, complexity emerges on its own. Often, we assume that complexity implies intent, but evolution inverts this intuition entirely. This is perhaps the most radical idea in the history of science, and it follows almost inevitably from the three conditions above.

Once you accept evolution as a substrate-independent process, examples outside of biology become difficult to ignore. Languages evolve rapidly, as new generations introduce new slang⁴, cultural interaction results in new forms, and old ones quietly die out. Cultural practices evolve similarly, with Dawkins coining the term “meme” in The Selfish Gene to describe the cultural analogue of a gene. Economic markets are also evolutionary systems, with companies competing for resources. In computer science, genetic algorithms often pop up, using evolution to search for a solution we don’t know how to design directly. The process is everywhere.

Yet evolution is ultimately blind and local. It cannot plan ahead and often gets stuck in local optima with no way out. It also never converges to a final stable state, because the fitness landscape is always shifting. In today’s world, we can see that the very organisms that evolution shaped are changing the landscape⁵, changing what it means to be successful. This is why the process never settles, because the goalposts are also evolving.

Anyways, this is where I currently am in my understanding. There is a lot more to explore, including how evolution creates specific instances of complexity, the constraints of developmental biology, and the impact of random chance. Some of the books I plan to read next to explore evolution more deeply are listed below:

• Endless Forms Most Beautiful by Sean B. Carroll
• The Extended Phenotype by Richard Dawkins
• Some Assembly Required by Neil Shubin⁶
• A Series of Fortunate Events by Sean B. Carroll
• The Ancestor’s Tale by Richard Dawkins
• On the Origin of Species by Charles Darwin