My favorite analogy is that of an automated control system, like a radio receiver. The main goal is to lock onto the station and maximize the signal fidelity, but once it’s done, there is always the noise of the locking algorithm steering around the optimal point. The drive to optimize doesn’t disappear, but ends up cranking up the gain on this residual optimization noise and trying to find something else to optimize. In psychology this is phenomenologically described as the Maslow’s hierarchy. This (misapplied) drive to optimize creates this complexity, because it is trying to work on its own internal optimization algorithms, leading to a lot of non-linearity and spurious chaotic behavior. Hmm, wonder how hard it would be to model this mathematically or numerically.
My favorite analogy is that of an automated control system, like a radio receiver. The main goal is to lock onto the station and maximize the signal fidelity, but once it’s done, there is always the noise of the locking algorithm steering around the optimal point. The drive to optimize doesn’t disappear, but ends up cranking up the gain on this residual optimization noise and trying to find something else to optimize. In psychology this is phenomenologically described as the Maslow’s hierarchy. This (misapplied) drive to optimize creates this complexity, because it is trying to work on its own internal optimization algorithms, leading to a lot of non-linearity and spurious chaotic behavior. Hmm, wonder how hard it would be to model this mathematically or numerically.