I suggest interpreting phenomenon as multi-level nested optimization paradigm: many systems can be usefully described as having two (or more) levels where a slow sample-inefficient but ground-truth ‘outer’ loss such as death, bankruptcy, or reproductive fitness, trains & constrains a fast sample-efficient but possibly misguided ‘inner’ loss which is used by learned mechanisms such as neural networks or linear programming group selection perspective.
There is more to the post though! I recommend reading it, especially if you’re confused what this could possibly concretely mean when all natural selection is is an update process, and no real outer loss is defined. Especially the section Two-Level Meta Learning. I do not think he makes this mistake out of naivete.
Gwern talks about natural selection like it has a loss function in Evolution as Backstop For Reinforcement Learning:
There is more to the post though! I recommend reading it, especially if you’re confused what this could possibly concretely mean when all natural selection is is an update process, and no real outer loss is defined. Especially the section Two-Level Meta Learning. I do not think he makes this mistake out of naivete.