-Notably, Plomin and Turkheimer aren't actually disagreeing here: it's a difference in emphasis rather than facts. Polygenic scores _don't_ explain mechanisms—but might they end up being useful, and used, anyway? Murray's vision of social science is content to make predictions and "explain variance" while remaining ignorant of ultimate causality. Meanwhile, my cursory understanding (while kicking myself for [_still_](/2018/Dec/untitled-metablogging-26-december-2018/#daphne-koller-and-the-methods) not having put in the hours to get farther into [_Daphne Koller and the Methods of Rationality_](https://mitpress.mit.edu/books/probabilistic-graphical-models)) was that you need to understand causality in order to predict what interventions will have what effects—maybe our feeble state of knowledge is _why_ we don't know how to find reliable large-effect environmental interventions that still yet might exist in the vastness of the space of possible interventions.
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-There are also some appendicies at the back of the book! Appendix 1 (reproduced from one of Murray's earlier books) explains some basic statistics concepts. Appendix 2 ("Sexual Dimorphism in Humans") goes over the prevalence of intersex conditions and gays, and then—so much for this post broadening the [topic scope of this blog](/tag/two-type-taxonomy/)—transgender typology! Murray presents the Blanchard–Bailey–Lawrence–Littman view as fact, which I think is basically _correct_, but a more comprehensive treatment (which I concede may be too much too hope for from a mere Appendix) would have at least _mentioned_ alternative views ([Serano](https://rationalwiki.org/wiki/Intrinsic_Inclinations_Model)? [Veale](/papers/veale-lomax-clarke-identity_defense_model.pdf)?), if only to explain _why_ they're worth dismissing. (Contrast to the eight pages in the main text explaining why "But, but, epigenetics!" is worth dismissing.) Then Appendix 3 ("Sex Differences in Brain Volumes and Variance") has tables of brain-size data, and an explanation of the greater-male-variance hypothesis. Cool!
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-... and that's the book review that I would _prefer_ to write. A science review of a science book, for science nerds: the kind of thing that would have no reason to draw your attention if you're not _genuinely interested_ in Mahanalobis _D_ effect sizes or adaptive introgression or Falconer's formula, for their own sake, or (better) for the sake of [compressing the length of the message needed to encode your observations](https://en.wikipedia.org/wiki/Minimum_message_length).
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-But that's not why you're reading this. That's not why Murray wrote the book. That's not even why _I'm_ writing this. We should hope—emphasis on the _should_—for a discipline of Actual Social Science, whose practitioners strive to report the truth, the whole truth, and nothing but the truth, with the same passionately dispassionate objectivity they might bring to the study of beetles, or algebraic topology—or that an alien superintelligence might bring to the study of humans.
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-We do not have a discipline of Actual Social Science. Possibly because we're not smart enough to do it, but perhaps more so because we're not smart enough to _want_ to do it. Not one has an incentive to lie about the homotopy groups of an _n_-sphere. (The <em>k</em><sup>th</sup> group is trivial for _k_ < _n_, and isomorphic to the integers thereafter. _You're welcome._) If you're asking questions about homotopy groups _at all_, you almost certainly care about getting the _right answer for the right reasons_.
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-But as soon as we start to ask questions _about humans_—and far more so _identifiable groups_ of humans—we enter the domain of _politics_. Everyone _and her dog_ has some fucking _agenda_—and the people who claim not to have an agenda are lying. (The most I can credibly claim for myself is that I try to keep my agenda reasonably _minimalist_—and the reader must judge for herself to what extent I succeed.)
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+Notably, Plomin and Turkheimer aren't actually disagreeing here: it's a difference in emphasis rather than facts. Polygenic scores _don't_ explain mechanisms—but might they end up being useful, and used, anyway? Murray's vision of social science is content to make predictions and "explain variance" while remaining ignorant of ultimate causality. Meanwhile, my cursory understanding (while kicking myself for [_still_](/2018/Dec/untitled-metablogging-26-december-2018/#daphne-koller-and-the-methods) not having put in the hours to get much farther into [_Probabilistic Graphical Models: Principles and Techniques_](https://mitpress.mit.edu/books/probabilistic-graphical-models)) was that you need to understand causality in order to predict what interventions will have what effects [TODO: explain why with example]