They search in the same way because random sampling via variability is an effective way to search. However, humans could perform effective searches by variation at the individual or population level (for example, a sentient creature could model all different kinds of thought to think of different solutions) but I was arguing for the variation at the population level.
Variability at the population level is explained by the fact that we are products of evolution.
Of course, human searches are effective as a result of both kinds of variation.
Not that any of this was thought out before your question… This the usual networked-thought-reasoning verses linear-written-argument mapping problem.
random sampling via variability is an effective way to search
No it’s not. It is one of the few search methods that are simple enough to understand without reading an AI textbook, so a lot of nontechnical people know about it and praise it and assign too much credit to it. And there are even a few problem classes where it works well, though what makes a problem this “easy” is hard to understand without reading an AI textbook. But no, it’s not a very impressive kind of search.
Heh, I came to a similar thought walking home after asking the question… that it seems at least plausible the only kinda powerful optimization processes that are simple enough to pop up randomlyish are the ones that do random sampling via variability.
I’m not sure it makes sense that variability at the population level is much explained by coming from evolution, though. Seems to me, as a bound, we just don’t have enough points in the search space to be worth it even with 6b minds, and especially not down at the population levels during most of evolution. Then there’s the whole difficulty with group selection, of course. My intuition says no… yours says yes though?
Why would evolution’s search results tend to search in the same way evolution searches?
They search in the same way because random sampling via variability is an effective way to search. However, humans could perform effective searches by variation at the individual or population level (for example, a sentient creature could model all different kinds of thought to think of different solutions) but I was arguing for the variation at the population level.
Variability at the population level is explained by the fact that we are products of evolution.
Of course, human searches are effective as a result of both kinds of variation.
Not that any of this was thought out before your question… This the usual networked-thought-reasoning verses linear-written-argument mapping problem.
No it’s not. It is one of the few search methods that are simple enough to understand without reading an AI textbook, so a lot of nontechnical people know about it and praise it and assign too much credit to it. And there are even a few problem classes where it works well, though what makes a problem this “easy” is hard to understand without reading an AI textbook. But no, it’s not a very impressive kind of search.
Heh, I came to a similar thought walking home after asking the question… that it seems at least plausible the only kinda powerful optimization processes that are simple enough to pop up randomlyish are the ones that do random sampling via variability.
I’m not sure it makes sense that variability at the population level is much explained by coming from evolution, though. Seems to me, as a bound, we just don’t have enough points in the search space to be worth it even with 6b minds, and especially not down at the population levels during most of evolution. Then there’s the whole difficulty with group selection, of course. My intuition says no… yours says yes though?