| The author makes a mistake here. It's fine to think of entropy as messiness; that's the Boltzmann picture of statistical mechanics. The mistake is thinking that lowering entropy, or getting rid of the mess, is a satisfactory strategy. Think of it as a negative feedback system, like a thermostat. Keeping entropy low means continually correcting errors. This is a successful strategy only if the world always stays the same, but it notoriously does not. Some degree of messiness is needed to remain flexible, strange as it may sound. There must be room to make the good kind of mistakes and happy little accidents (as Bob Ross would put it). Because the author chose an analogy rooted in statistical mechanics, here's another: supercooled water. Take a bottle of purified water and put it in the cooler. It gets chilled below freezing temperature without freezing. If you give it a shake, it instantly freezes. The analogy may sound a bit vapid, but noise is the crucial ingredient for the system to "find" its lowest-energy state. The system crystallizes from some nucleation site. It's the same with evolution. Mutations are a must. Keeping our genetic entropy low isn't a viable option, because that means we'll get stuck and die out. There must be opportunity for randomness, chance, chaos, noise; all that jazz. This is how China became an economic powerhouse under Deng Xioping, for instance. They experimented with various policies and if something accidentally turned out to work great, it became something of a "nucleation site". The policy that worked in, say, Shaowu, would spread all across China. But it would never have worked if they stuck with a rigid, Confucian strategy of keeping "entropy" low at all times. Entropy isn't necessarily fatal. Entropy can be used as a strategy for adaptation. |
Most 'best practices' are good defaults, but the superlative rhetoric comes with the unstated assumption that any deviation is necessarily inferior, and that the best cannot be iterated upon. This drains agency from actors within a system, selecting for predictable mediocrity rather than risky innovation.