You might ask: okay, but then where do the two types come from? This graph is just illustrating (conjectured) cause-and-effect relationships, but if we were actually to flesh it out as a complete Bayesian network, there would be additional data that quantitatively specifies what (probability distribution over) values each node takes conditional on the values of its parents. When I claim that Blanchard–Bailey–Lawrence's two-type taxonomy is a useful approximation for this causal model, I'm claiming that the distribution represented by this Bayesian network (if we had the complete network) could also be approximated a two-cluster model: _most_ people high in the "femininity" factor will be low in the "autogynephilia" factor and _vice versa_, such that you can buy decent predictive accuracy by casually speaking as if there were two discrete "types".
-Why? The key has to do with the parents of femininity and autogynephilia in the graph.
+Why? It has to do with the parents of femininity and autogynephilia in the graph. Suppose that gay men are more feminine than straight men, and autogynephilia is the result of being straight plus having an "erotic target location error", in which men who are attracted to something (in this case, women), are also attracted to the idea of _being_ that thing.
-Gay men are more feminine than straight men.
+Then the value of the sexual-orientation node is pushing the values of its children in _opposite_ directions: gay males are more feminine and less autogynephilic, and straight males are less feminine and more autogynephilic, leading to two broadly different etiological trajectories by which transition might seem like a good idea to someone, even while it's not that the two types have nothing in common. For example, this model predicts that among autogynephilic males, those who transition are going to be selected for higher levels of femininity compared to those who don't transition—and in that aspect, their stories are going to have _something_ in common with their androphilic sisters, even if the latter are broadly _more_ feminine.
-And
+(Of course, it's also the case that the component factors in a liability-threshold model would negatively correlate among the population past a threshold, due to the effect of conditioning on a collider, as in the famous Berkson's paradox. But I'm claiming that the degree of bimodality induced by the effects of sexual orientation is substantially greater than that accounted for by the conditioning-on-a-collider effect.)
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-(Of course, it's _also_ the case that the component factors in a liability-threshold model would negatively correlate
-
-the factors of a
-
-, due to Berkson's paradox,
+An advantage of this kind of _probabilistic_ model is that it gives us a _causal_ account of the broad trends we see, while also not being too "brittle" in the face of a complex world. The threshold graphical model explains why the two-type taxonomy looks so compelling as a first approximation, without immediately collapsing the moment we meet a relatively unusual individual who doesn't seem to quite fit the strictest interpretation of the classical two-type taxonomy.
[TODO—
-[The sexual orientation node increases femininity and decreases AGP, so those pathways are anti-correlated; however, the fact that straight AGP men also vary somewhat in their degree of femininity; some informal accounts (link Sailer) have emphasized how masculine (even hypermasculine) AGPs are, but this seems wrong]
-[briefly mention ETLE]
-[People who don't quite seem to fit the coarse taxonomy might still be explained by the graph and a threshold model]
-]
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+example: hi femininity + AGP
+example: cultural factors
+care must be taken to avoid rationalization]
You might ask: okay, but why do I believe this? Anyone can name some variables and sketch a directed graph between them. Why should you believe this particular graph is _true_?
The v-structure between sexual orientation, erotic target location erroneousness, and autogynephilia has been documented by Anne Lawrence:
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The autogynehilia→transition arrow has
The cultural-factors→transition arrow is obvious if you haven't been living under a rock for the last decade.
projects / Ultimately_Untrue_Thought.git / blobdiff