Jacob Kimmel

Like, okay, let's just pretend everything I said was wrong.

Can I still make an argument that maybe evolution hasn't maximally optimized our for our longevity?

One argument that comes up

And I'll caveat and say, I don't know how strong some of the mathematical models that people put together here are.

You can find people using the same idea to argue for and against.

But there's this notion of what's called kin selection, that if you sort of take a selfish gene view of the world, that really this is the genome optimizing for the genome's propagation.

It's not trying to optimize for any one individual.

then actually optimizing for longevity is a pretty tricky problem because you have this nasty regularization term, which is that if you're able to make a member of the population live longer, but you don't also counteract the decrease in their fitness over time, meaning you maybe extend maximum lifespan, but you haven't totally eliminated aging, then the number of net calories contributed to the genome as a function of that person's marginal year and their own calorie consumption is less than if you were to allow that individual to die and actually have two 20-year-olds, for instance, that sort of follow behind them.

And so there is a notion by which a population being laden demographically with many aged individuals, even if they did have fecundity persisting out some period later in life, is actually net negative for the genome's proliferation.

And that really a genome should optimize for turnover and population size at max fitness.

So I think this is where another ML analogy is helpful, which is something like, well, actually a two-layer neural network is technically a universal approximator, but we can never actually fit them in such a way.

And why does that occur?

People will wave their hands, but it basically comes down to we don't really know how to optimize them, even if you can prove out in a formal sense that they are universal approximators.

And so I think we have similar optimization challenges with our genome as the parameters and evolution as the optimization algorithm.

And one of those is that your mutation rate basically bounds the step size you can take.

So if you imagine that at each generation, you get some number of inputs, you can select for some number of alleles.

Well, the max number of variations in the genome is set by mutation rate.

If you dial your mutation rate up too high, you probably get a bunch of cancers.

So you're selected against.

If you have it too low, you can't really adapt to anything.