“Regularity” is another term for “compressibility”, specifically, lossy compressibility relative to the compressing entity. And yes, they are used exactly for the purpose of “avoiding the “no free lunch” theorems, since they are both true and vacuous. Maps are abstractions, lossy compression of the territory used to minimize predictive error. Because lossy compressibility (i.e. mapping) is relative to the compressor, different compressors exploit different “regularities”. For example, you can create a different map based on audio, visual or scent information, and it takes a specific agent (i.e. compressor) to build and use each one of those.
“Regularity” is another term for “compressibility”, specifically, lossy compressibility relative to the compressing entity. And yes, they are used exactly for the purpose of “avoiding the “no free lunch” theorems, since they are both true and vacuous. Maps are abstractions, lossy compression of the territory used to minimize predictive error. Because lossy compressibility (i.e. mapping) is relative to the compressor, different compressors exploit different “regularities”. For example, you can create a different map based on audio, visual or scent information, and it takes a specific agent (i.e. compressor) to build and use each one of those.