When we get into statistics, a major issue that we deal with is how well a model fits the data. We refer to this as “Goodness of fit” and what it represents is usually a summary of the differences between observed values and expected values. The reason for doing such tests is to gauge how well our model works to explain the observations.
This could be as simple as the relationship between the independent variable (the one which we control or change) and the dependent variable (the one on which the effect of changes in the independent variable is seen), to complex multivariate analyses which attempt to gauge the effect of multiple variables on a single variable.
Models are means to an end, they are the extension of a theory, and work to test how well that theory fits reality. They represent our hypothesis of cause and effect, based on the theory we have formulated about how something works. To give an example, if we wanted to know if there is a relationship between number of repetitions affect hypertrophy, we would formulate 2 hypotheses:
Null-hypothesis – There is no relationship between number of repetitions and muscle hypertrophy
Hypothesis 1 – There is a relationship between number of repetitions and muscle hypertrophy
We would then conduct an experiment where we take a sample, have them perform a given number of repetitions across a period of time at a frequency held constant, and then measure the change between the start of the experiment and the end of the experiment. From this we get an answer of whether the independent variable (number of repetitions) affects the dependent variable (hypertrophy). The results of this experiment could then be validated, replicated and serve as a foundation for future research into hypertrophy.
This research, and future research can then serve to guide us when we are aiming to gain more muscle so that we find the most efficient route between point A and point B. We began with a question “How does number of repetitions affect muscle hypertrophy“, we did our research, formulated a theory and then tested that theory in an experiment, the results of which are utilized to amend our model of reality, which can then be tested again.
This is how most research works, a researcher starts by wanting to answer a question, does research into the question, based on the question a theory is formulated, this theory is tested through observation or experiment, and the results of said experiments are then integrated into the theory.
Our goal when conducting research is to generate mind-independent information, meaning that human minds constantly generate cause and effect hypotheses, then test them against reality. However, we are also prone to many errors of reasoning that lead us to believing in false relationships, a prime example being the superstitions of various athletes or sports-teams.
The Red Pill and Models
The reason why I’ve become quite prone to using weight training as an example in these essays is that as a field it has one major overlap with the red pill. Namely, that practitioners were and still are, far ahead of the scientific research in the field in many areas. This has changed a little as more and more practitioners have gotten degrees and are conducting research, attempting to bring science into strength. The bodybuilding and strength training community has been reliant on “bro-science”, which were poorly controlled and defined n=1 experiments for the most part, conducted by dedicated practitioners. This meant that it was very difficult to separate the good information from the bad, in this case what works from what does not, meaning those areas where there is a cause and effect relationship, and those where there is not.
This is very similar to how game evolved, in that practitioners had a question, formulated their theories of cause and effect relationships, then tested them empirically. Those of you who read my Tinder Experiment essay, will recognize the methodology. My reasoning behind doing the experiment was simply based on me wanting to know which Tinder pictures I should use to best achieve my desired end.
When the Red Pill is defined as a praxeology, this simply means that it’s a study of human action based on a premise that humans engage in purposeful behavior. If one looks to Austrian economics, the main research method is a use of praxeology and deduction in order to determine economic principles. From the perspective of an Austrian economist, starting with the action axiom defined by Ludwig Von Mises as follows:
Human action is purposeful behavior. Or we may say: Action is will put into operation and transformed into an agency, is aiming at ends and goals, is the ego’s meaningful response to stimuli and to the conditions of its environment, is a person’s conscious adjustment to the state of the universe that determines his life. Such paraphrases may clarify the definition given and prevent possible misinterpretations. But the definition itself is adequate and does not need complement of commentary. Von Mises “Human Action p. 11”
One can form conclusions about human behavior that are both empirical and universal. For instance, observation that some men have much higher efficacy in seeking sex, was a foundational observation of early pick-up artists, who observed these men in the field. The fact that women sought out such men and behaved differently towards them, implied that there is a purpose and a motivation behind such behavior.
Over time many theories were formulated, and tested to identify the variables. One could argue that a series of hypothesis tests were conducted formulated as follows:
Null-hypothesis: [Insert behavior] has no effect on female proclivity to engage in sex.
Hypothesis 1: [Insert behavior] has a positive effect on female proclivity to engage in sex.
Hypothesis 2: [Insert behavior] has a negative effect on female proclivity to engage in sex.
This is obviously generalized, but in practice what the early PUA did were to observe men in the wild who were naturally good with women, do exactly what those men did to the best of their ability and observe the results. Over time this moved from specific, scripted action to generalized principles.
These observations, tests and results, eventually became the Alpha and Beta constructs. These constructs have been subject to more testing and debate than most other constructs within the manosphere for the simple reason that most men want to be in the former category and desire a list of things they have to do in order to qualify. This is the same underlying approach as the man who is stuck in a sex-less relationship and states “I will do whatever she wants to get her to sleep with me“.
Over time many such observations of theories have formed a large body of knowledge about human mating and seduction, with varying accuracy, and empirical support. Some of it is based on the methodology outlined in the introduction section of this essay, which is classic deduction, that goes from theory, to hypothesis, to observation and finally confirmation.
Examples of this in practice is something that I did in the Tinder experiment. Based on underlying theories, mainly the Alpha/Beta constructs and the sexual market value graph, I formulated a few hypotheses, and tested them to confirm. The experiment had some limitations, among others the sample size, lack of control for confounding variables and data that was unavailable (profile views to right swipes), however in this particular area of knowledge one will never have the full picture until we have A.I.
Other parts of the body of knowledge is based on induction, moving from specific observations, to patterns, to a tentative hypothesis which forms the basis for a theory. Grounded theory as referenced earlier is perhaps the most well-known example of induction used in academic research. The theory generated from a grounded theory research project can then be used as the foundation for more empirical research.
Summary and Conclusions
Nassim Nicholas Taleb shared a quote on twitter some time ago that has become one of my favorites rather quickly, while I cannot guarantee that I’m quoting it accurately from memory it went something like:
“To a normal person if something works in theory, but not in practice it doesn’t work. To an Academic if something works in practice but not in theory, it does not exist“
I quite liked this quote, because my own journey from academic to practitioner has demonstrated it to be true time and time again. It’s quite common for young and inexperienced professionals, often fresh out of school to attempt to force fit everything into the set of models they were trained to use as part of their schooling, in that process ignoring things that do not fit neatly into the theory. Alternatively, they may insist on a completeness in model or framework utilization that is beyond scope for the purpose at hand.
In both cases, they are making the same mistake that is frequently observed in academia, namely forgetting the reason for why the information came to exist in the first place, namely a practical application. This is not to say that the practical application of a given set of research must be immediately practically applicable to have value, in fact it took quite a while before Einstein’s research was used to create Geo-synchronous satellites. Sometimes, the only practical purpose of research is to serve as the basis for future research which may also serve the same purpose, only for practical application to come at a much later date.
However, what I’ve found as I’ve gained more experience and have shifted my focus throughout my life to be more of a man of action, rather than a man of ideas is that a lot of things that work may not have a 50 year history of research behind it, have been the subject of countless research projects, and be wholly empirically validated. Especially not if one is on the cutting edge.
This leaves us with in a tricky situation. Intersexual and intrasexual dynamics is not a STEM field, meaning that one cannot establish clear-cut, open and shut if A then B models. If I take my coffee cup, walk out onto my balcony and drop it, it will always fall to the ground. If I approached 100 women using the same opener, it will not yield 100% the same result every time. As an economist, this doesn’t really bother me because economics is a field that is permeated by the exact same issue.
In economics there is an infinite range of potential outcomes, with varying probability, where interdependencies, unclear cause-and-effect relationships and various other things muddy the waters. It is, at the same time a very simple system in extreme-micro and an extremely complex system in extreme macro. This is why I tweeted that a stock analyst that is right 51% of the time is brilliant, because if one is right 51% of the time, follow the principles of diversification, portfolio management and have a rule-set for investing, such a man will make a lot of money over time.
So, how does one decide what to do in such a system? I will openly admit that sometimes I wish I could work in a STEM field, because it would be really nice to work with things that are cut and dry, where every question has a clearly defined answer and where no debate is needed. However, since I don’t, I’ve found that staying up to date on both the academic research and the best practices on the cutting edge of the industry, along with remaining flexible is the best approach.
This is also the approach I take with intersexual dynamics and game. If someone formulated a body of knowledge that worked better tomorrow, I would try it out and if it yielded better results I would make the switch. However, the ultimate acid-test is “Does it work?” and “How well does it work?”. We can sit around and debate topics back and forth in various comment sections, forums and social media platforms, but does this create progress or are we just sitting around debating minutia and nit-picking?
The majority of content on this blog is not empirical, replicated and validated research that has yielded absolute facts. I make an attempt to cite my sources as frequently as possible, however I also extrapolate from the data and form theories based on limited available data. In fact, I doubt that intersexual dynamics can ever get to the point where it is a strategy guide to get your exact desired outcome with any woman. This is no different from how I doubt economics will ever get to the point where we can say with absolute certainty “If you do A, then B”, the best we can do is to say “If you do X, then you have a higher probability of getting B, than if you do Y.”
What I think people forget, is that there are as many desired ranges of outcomes, as there are humans, in fact probably more given than human desired outcomes can vacillate quite frequently. We saw this in “The Game”, Mystery and Style had formulated their model of game based on the desired outcome of rapidly sleeping with women they met in bars and clubs, but that model did not work well when they wanted a relationship. Of course it didn’t, just like an investment strategy focused on high growth companies, in a brand new industry with a lot of small companies is great for a high risk/high return strategy, but it’s not good for those who hate volatility and want a stable return.
This doesn’t mean that the underlying principles of good portfolio management, diversification, risk analysis and various others should be discarded, it just means that they must be adjusted a little to work for your desired outcome.
In the end, the ultimate question that determines the value of any idea is “Does it work?“, by which “Does it produce on average more desirable results than other competing ideas?“