Red Pill Logic: A Diagnosis of Oneitis

This essay was left in the draft folder for a while, but a question relating to it came up in the last 21 convention podcast, so I decided to finish it up.

In medicine, a syndrome is a set of signs and symptoms that are correlated with each other, and the word itself stems from the Greek word meaning “concurrence”. For instance, “metabolic syndrome”, which is rapidly gaining in market share around the world, consists of increased blood pressure, high blood sugar, excess body fat around the waist, abnormal cholesterol levels and triglyceride levels [1]. Normally, I’m skeptical of the pathologizing that takes place in much of public discourse, as it appears to have become quite common to utilize it as a rhetorical gambit in order to paint perspectives different from one’s own as stemming not from reason but from underlying psychological or physiological conditions. However, in this case, I found it to be quite an apt description of the phenomena that this post aims to describe, namely a combination of signs and symptoms that are correlated with blue pill thinking, and especially with oneitis.

Perhaps the most interesting factor in the the oneitis disorder is that actually being in a relationship with the woman is not a pre-requsite to trigger the disorder. In fact, many of the cases that I’ve observed are by men who exist outside the woman’s sphere of awareness, the “secret admirer” type, who builds an elaborate fantasy about a woman who has no idea that he exists. Continue reading


Gendernomics: Untangling Variables

One of the more challenging tasks when doing research is the removal of superfluous variables. In the simplest terms you want to study one independent variable, meaning a variable that you or nature manipulates, and measure the change in the independent variable to the change in the dependent variable. For instance, if you want to understand the relationship between protein intake (independent variable) and lean muscle gain (dependent variable), you want to manipulate protein intake and measure the change in muscle gain.

However, reality is rarely this simple,there are other variables besides protein intake that affects lean muscle gain, such as resistance training, overall calorie intake and calorie expenditure, hormone levels, and various others. Which is why most modern analyses use multiple variables. For instance, if you wanted to determine what effect protein intake had on muscle gain, you would need to determine what effects other variables had on muscle gain, so that you could isolate out how much of lets say a 3 lb muscle gain in 6 months was due to protein, and how much could be attributed to other variables.

These are based on a mixture of our experiences and what we have been trained to do, and in some cases they make perfect sense, in other cases not so much. In some cases a person has intuitively correctly identified relationships between independent and dependent variables, and thus has an innate grasp of influence and outcome. In other cases a person has made a connection that makes no sense, this is quite interesting when observed in people suffering from delusions, in that their logic can often be sound, but is based on a flawed cause and effect relationship.

This is a major challenge for trained and experienced researchers, and it’s even more of a challenge for people who are not familiar with logic and epistemology, because our minds are constructed to make cause and effect determinations on the fly all day, every day. Athletes have a reputation for making sometimes hilarious cause and effect errors that lead to things like a team not washing their jock-straps for the entire season on a winning streak, various pre-game rituals and so on. Continue reading