This suggests -- and this is supported by brain mass studies -- that if the brain is not subjected to strong selection for power/efficiency/ability then you get a kind of "fatty brain" that takes up space but doesn't work that well.
You see analogs in e.g. "sclerotic" corporations with lots of employees doing nothing. My bio professor said "life doesn't work perfectly... it just works." Evolution isn't "survival of the fittest," but selection for a "surviving subset of the sufficiently fit." Economics is the same, thus the sclerotic corporation/government analogy.
Of course if evolution actually was "survival of the fittest" it would reduce to a greedy hill climbing algorithm and would converge on the first local maximum it encountered and stay stuck there forever. Tolerance for variation is a (probably provable) prerequisite for anything particularly interesting, and diversity implies inefficiency among other things.
When I was playing with alife and genetic algorithms a lot, I found that relaxing selective pressure often improved performance in terms of overall best solution generated. There's a number of papers out there that draw similar conclusions in a variety of systems. Sometimes you can get GA/GP systems that escape local maxima and find much more clever and interesting solutions by setting up a selection function that adjusts its strictness curve based on measured diversity (we jokingly called this affirmative action), or by creating an environmental topology that encourages diversity (many compartments / demes, etc.).
You see analogs in e.g. "sclerotic" corporations with lots of employees doing nothing. My bio professor said "life doesn't work perfectly... it just works." Evolution isn't "survival of the fittest," but selection for a "surviving subset of the sufficiently fit." Economics is the same, thus the sclerotic corporation/government analogy.
Of course if evolution actually was "survival of the fittest" it would reduce to a greedy hill climbing algorithm and would converge on the first local maximum it encountered and stay stuck there forever. Tolerance for variation is a (probably provable) prerequisite for anything particularly interesting, and diversity implies inefficiency among other things.
When I was playing with alife and genetic algorithms a lot, I found that relaxing selective pressure often improved performance in terms of overall best solution generated. There's a number of papers out there that draw similar conclusions in a variety of systems. Sometimes you can get GA/GP systems that escape local maxima and find much more clever and interesting solutions by setting up a selection function that adjusts its strictness curve based on measured diversity (we jokingly called this affirmative action), or by creating an environmental topology that encourages diversity (many compartments / demes, etc.).