🔖 [1703.04184v2] A Theory for Gender Differences in Variability by Theodore P. Hill and Sergei Tabachnikov

Bookmarked [1703.04184v2] A Theory for Gender Differences in Variability by Theodore P. Hill, Sergei Tabachnikov (arxiv.org)
A selectivity theory is proposed to help explain how one gender of a species might tend to evolve with greater variability than the other gender. Briefly, the theory says that if one sex is relatively selective, then more variable subpopulations of the opposite sex will tend to prevail over those with lesser variability; and conversely, if one sex is relatively non-selective, then less variable subpopulations of the opposite sex will tend to prevail over those with greater variability. This theory makes no assumptions about differences in means between the sexes, nor does it presume that one sex is selective and the other non-selective. Two mathematical models are presented: a statistical analysis using normally distributed fitness values, and a deterministic analysis using a standard system of coupled ordinary differential equations with exponentially distributed fitness levels. The theory is applied to the classical greater male variability hypothesis.
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🔖 Information theory, predictability, and the emergence of complex life

Bookmarked Information theory, predictability, and the emergence of complex life (arxiv.org)
Abstract: Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated to detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated to maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.
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