Learning from being wrong

Being wrong seems to be something to avoid, but it may be the price to pay if we want to improve or optimize something. This is the general philosophy behind Genetic/Evolutionary algorithms and even statistical sampling.

In Genetic/Evolutionar algorithms, the key ingredient is the introduction of mutation. In most cases a mutation will be destructive, but there will be occasions when the mutation will be beneficial. We improve by discarding the destructive mutations, while keeping the beneficial ones.

It was Thomas Bäck from Leiden University who told us how Toyota promotes mutation in the production system in order to make more efficient cars.

In statistical sampling, the objective is to explore the space of a probability distribution (explore the opportunities). Here we have the Metropolis (and Metropolis-Hastings) algorithm, where one is forced to be wrong sometimes in order to explore the probability distribution.

Ben Schumacher, the author of the quantum noiseless coding theorem has something to tell about this
Ben Schumacher on "Being wrong"

Disclaimer: The only way to profit from being wrong (mutate) is to be ready to correct ourselves as fast as possible.