Metaptation
I recently read a well-thought-out and elegantly written (though rather dense) article, Metaptation: The Product of Selection at the Second Tier, by David King. The concept is not a particularly new one; King riffs on the evolution of evolvability, learning how to learn, and Hofstadter's metaphor of knob-twiddling vs. knob-creation. The article is worth reading for King's eloquence and careful reasoning even if you're already familiar with all of the material (I'd estimate about 2/3rds familiarity myself).
Briefly, metaptations are adaptations which are selected for in an evolutionary system not because of their direct effects on fitness, but because they tend to lead to the appearance of meaningful adaptations. An example would be a reorganization of an organism's genome that had no effect on it's phenotype, but made deleterious mutations less common and/or "helpful" mutations more common (e.g., varying the size of an entire organism is more likely to succeed than varying the size of an individual organ). This kind of second-tier organization is particular dear to me because it forms the intellectual grounding for my current research efforts, designing evolutionary-probabilistic learning algorithms that incorporate explicit metaptive mechanisms for on-the-fly creation of meaningful varaibles (i.e., Hofstadterian knobs).
Note that this is a kind of group-level selection effect (which I am generally leery of), but carefully reasoned and circumscribed. The selfish gene paradigm is not negated but extended to recognize a hierarchy of selective levels. I could go on, but if you're still interested at this point, just go read the article :).

3 Comments:
Fascinating stuff, to my understanding you have adaptive "content" on one hand, on the parameters of that content adapting on the other. In order to support the initial generation/seed for this sort of actvity, you'd need a meta-grammar, a language of languages, in which you can code the potential dynamics of adaptation and metapation. The question then, is is a meta-grammar enough? Isn't there a risk of infinite regress of metas in this process?
Hi Patrick,
Thanks :). In a (weak) theoretical sense, this sort of regress is avoided once one has a representation which is Turing-complete; results from theory of computation and algorithmic information theory give us that any such representation can simulate/convert itself into any other Turing-complete or simpler representation with at most a constant overhead. In practice however, there's no telling how large this constant may be!
A more pragmatic answer is that the physical universe is constructed in such a way (cf. the anthropic principle) that biological evolution could get off the ground with "good enough" representations to rewrite themselves to a satisficable extent (no, that's not a typo). Ditto with our own mental representations- presumably they are simply one of possibly many "good enough" starting points allowing use to reach rationality, somewhat. cf. inductive bias- there's lots more to say here, appologies for being somewhat cryptic.
In terms of AI, one arguably needs to emulate human mental representations to some level of detail- Ben and I actually have paper related to this for an upcoming AI symposium ("Between a Rock and a Hard Place: Congitive Science Meets AI-Hard Problems"). :)
Hope this helps!
Your comment about mental evolution actually isn't cryptic to me, I think I understand it in memetic terms as rationality developing out of its utility to the host, thereby increasing the survival odds of the "rational" memeplex, which could be evaluated as turing complete and reproducable, e.g. the scientific method.
What I'm trying to do is figure out how to create a metaptation framework for creating content for user-mediated environments, preferably in a language which is artist friendly. That may be a harder task than Hard AI, but I'm probably just saying that because I'm not smart enough to realize how hard Hard AI is.
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