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ref: -0 tags: evolutionary psychology human mating sexuality discrimination wedlock date: 01-09-2011 18:22 gmt revision:1 [0] [head]

From Why Beautiful people have more daughters:

"Abuse, degradation, and intimidation are all part of men's unfortunate repertoire of tactics employed in competitive situations. In other words, men are not harassing women because they are treating them differently than men (which is the definition of discrimination under which harassment legally falls), but the exact opposite: men harass women because they are not discriminating between men and women."

Interesting argument. But in sexual discrimination cases, the women are not being treated the way they want to be treated - this is more a problem than the inequality.

The author then goes on to pose that current sexual discrimination law and policy in US corporations actually inhibits welcome sexual/romantic interest/advances. Many people do find partners at work. Again, I beg to differ: if there is passion between people, things will fall as they should; if policy and culture serves to make this more civilized (provided it's not completely inhibited, as the author suggests), then all the better.


In related news: An Analysis of Out-Of-Wedlock Births in the United States

Central hypothesis: Contraceptive technology shifted the balance of power between the sexes: prior the pill, women could force the men into promising to marry; in the case of preganancy, cultural standards forced marriage - shotgun marriage. Men accepted these terms because they were uniform across all women - sex implies pregnancy implies child rearing. When contraception became available, this was decoupled, as sex did not beget pregnancy; those women who negotiated on the old terms were likely to lose their mate, hence shotgun marriages (the result of such negotiations) gradually disappeared from culture.

The author generally approves of the idea of shotgun marriage, and suggests that a governmental body should enforce a form of it through child support payments. Presently about 40% of children in the US are born out of wedlock.


Finally, Serial monogamy increases reproductive success in men but not in women. It rests upon data, only recently gathered, that supports that having multiple partners increases reproductive success more strongly in male than in female humans. This implies that the variance of the fertility of men should be higher than that of women - again, which is borne out in the data, but only weakly: men have 10% higher variance in # of offspring than women. This effect is correlated to serial monogamy - "Compared with men with 1 spouse, men with 3 or more spouses had 19% more children in the total sample". This did not hold with women, nor did varying spouse number in men change the survival rate of their offspring.


Irregardless, this reading was spurred by someone mentioning that a genetic analysis of human populations reveals that while 80% of women reached reproductive success, only 40% of men did - implying that historically a few more successful men fathered a large fraction of children. I was unable to find evidence to support this on the internet (and indeed the Behavioral Ecology article gives much less dramatic figures), but it makes intuitive sense, especially in light of some patterns of male behavior.

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ref: -0 tags: meta learning Artificial intelligence competent evolutionary programming Moshe Looks MOSES date: 08-07-2010 16:30 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

Competent Program Evolution

  • An excellent start, excellent good description + meta-description / review of existing literature.
  • He thinks about things in a slightly different way - separates what I call solutions and objective functions "post- and pre-representational levels" (respectively).
  • The thesis focuses on post-representational search/optimization, not pre-representational (though, I believe that both should meet in the middle - eg. pre-representational levels/ objective functions tuned iteratively during post-representational solution creation. This is what a human would do!)
  • The primary difficulty in competent program evolution is the intense non-decomposability of programs: every variable, constant, branch effects the execution of every other little bit.
  • Competent program creation is possible - humans create programs significantly shorter than lookup tables - hence it should be possible to make a program to do the same job.
  • One solution to the problem is representation - formulate the program creation as a set of 'knobs' that can be twiddled (here he means both gradient-descent partial-derivative optimization and simplex or heuristic one-dimensional probabilistic search, of which there are many good algorithms.)
  • pp 27: outline of his MOSES program. Read it for yourself, but looks like:
  • The representation step above "explicitly addresses the underlying (semantic) structure of program space independently of the search for any kind of modularity or problem decomposition."
    • In MOSES, optimization does not operate directly on program space, but rather on subspaces defined by the representation-building process. These subspaces may be considered as being defined by templates assigning values to some of the underlying dimensions (e.g., they restrict the size and shape of any resulting trees).
  • In chapter 3 he examines the properties of the boolean programming space, which is claimed to be a good model of larger/more complicated programming spaces in that:
    • Simpler functions are much more heavily sampled - e.g. he generated 1e6 samples of 100-term boolean functions, then reduced them to minimal form using standard operators. The vast majority of the resultant minimum length (compressed) functions were simple - tautologies or of a few terms.
    • A corollary is that simply increasing syntactic sample length is insufficient for increasing program behavioral complexity / variety.
      • Actually, as random program length increases, the percentage with interesting behaviors decreases due to the structure of the minimum length function distribution.
  • Also tests random perturbations to large boolean formulae (variable replacement/removal, operator swapping) - ~90% of these do nothing.
    • These randomly perturbed programs show a similar structure to above: most of them have very similar behavior to their neighbors; only a few have unique behaviors. makes sense.
    • Run the other way: "syntactic space of large programs is nearly uniform with respect to semantic distance." Semantically similar (boolean) programs are not grouped together.
  • Results somehow seem a let-down: the program does not scale to even moderately large problem spaces. No loops, only functions with conditional evalutation - Jacques Pitrat's results are far more impressive. {815}
    • Seems that, still, there were a lot of meta-knobs to tweak in each implementation. Perhaps this is always the case?
  • My thought: perhaps you can run the optimization not on program representations, but rather program codepaths. He claims that one problem is that behavior is loosely or at worst chaotically related to program structure - which is true - hence optimization on the program itself is very difficult. This is why Moshe runs optimization on the 'knobs' of a representational structure.