How We Ensure Accuracy: The 3 Pillars of Mastery

the three pillars of learning

This article describes the “three pillars of learning”.

The “three pillars of learning” is an approach to comprehension and knowledge designed to provide the most thorough, realistic, and effective representation of reality.

It’s based on three pillars, each representing the most important and necessary elements for knowledge and comprehension -and, next, for mastery and life-effectiveness-.

Each pillar is then broken down into sub-components, and this article describes how the general method works, as well as how to optimize each pillar.

The 3 pillars of learning
  1. Experience
    1. First-hand experience: actively doing it, or being on the receiving end
    2. Observation: from your environment, from society, and from others
  2. Science
    1. Data: the quantitative results of studies and experiments, including data analysis to properly assess those results (probability, intervals, chi squares, etc.)
    2. Scientific method: to properly structure the collection of data. Knowledge of the scientific method also improves critical thinking, as well as supporting the critical analysis of other people’s work
  3. Critical analysis
    1. Analysis: reflecting, and seeking to make sense of experiences and science
    2. Logic: makes sure that the process of connecting the dots is rational
    3. Critical thinking: a meta-level analysis (analysis of analysis). It looks for holes in the data, possible blind spots, biases, personal biases, what data sets to prioritize, and keeps the three pillars in balance. It’s also the “watchdog” of analysis, making sure analysis remains logical as well as grounded in experience, science, and doesn’t wander off to create systems and theories based on thought processes alone (which is the philosopher approach / pies in the skies)
    4. Systemic thinking: making sense of all data points to develop coherent narrative and theories

The final product are:

  • Theories, Mental Models, & Systems. Coherent narratives that make sense of all the data points, explain reality, and allow for predictions.
    Also allow for:
    • General principles, or what some marketers may call “laws”
    • Strategies. The better the model, the more granularity at the individual level becomes possible
    • Techniques. The better the model, the more granularity
  • Real-world outcomes, based on the mindsets, approaches, strategies and techniques derived from the model

This is the process we also use for Power University.

1. Experience

First-hand experience includes everything that one sees, does, or experience himself.

There are two levels of first-hand experience:

  1. Personal experience
  2. Observation

1.2. First-Hand Experience

Very simply:

  • For socialization, your experience socializing
  • For dating, your dating experience
  • For relationships, your relationship experiences

1.2.2. What Counts Most for Mastery

Not all personal experiences are created equal.

For example”experience” in workplace politics after 30 years doing the same job, with no upward movement, with no self-development, and with no experimentation… Well, that’s generally poor experience.

As a rule of thumb, good personal exerience comes from:

  1. Successful experience: to learn advanced social skills, one must have been either a leader in his social group(s), or near the top of several social groups, and/or enjoy several solid friendships
  2. Repeated success: “every dog has its day”, goes the saying.
    The best experience comes from repeated successes, which shows that the strategies do work. Repeated success also means repeated experience, which provides more data points for learning.
    In the case of social group strategies, successful experience could mean that one has entered different social groups, and climbed to the top or near the very top in all of them
  3. Repeated successes in different environments: to broaden one’s own experience, one has ideal experienced and succeeded in many different environments, with different types of people, and within different cultures

1.2.3. Broaden Experience With External Feedback

First-hand experience that relies only on results misses a lot of data points.

A simple way of making up some of those data points it is to ask others for feedback.
For example, what they think of our actions, words, and general personality.

People who don’t ask are going to either guess, and possibly get it wrong, or completely miss out on data-points they might have never considered.

It’s important to pay particular attention to people who have gotten especially close to us, since they provide a vantage position to provide feedback on our behavior and strategies.
Asking things like:

  • “What did you think when first saw me…”
  • “What did you think when we first spoke…”
  • “How did you feel when I… “
  • “What did you like/dislike.. “

More than once some people -and some ladies- it was strange of me to ask so many questions.
But a good chunk of my personal development came from asking and listening.

1.3. Observation

Experience is not limited to what happens to us or what we do ourselves.

Observation of others and of the world around us is also part of experience.

Good observation skills allow to multiply our first-hand observation points by 10x.

The good social-researcher “in the field” observes others attentively, and seeks to link their behaviors, mindsets, and the results they obtain.
Special attention goes to people who act and/or obtain out of the ordinary results.
To deepen his understanding, the social researcher seeks to get close to them, engage with them, ask questions, and learn even more.

The best experience combines personal experience, plus observation.
But great observation skills make it possible to reach a mastery-level of understanding without necessarily having personally done the same.

1.3.2. Observation Added Value

Observation complements first hand experience in many important ways:

  • Learning from people different than we are

The social researcher actively seeks to behave differently and act out of character sometimes, just to learn how different behaviors and action perform.

But there is a limit to how differently he can act, since he can’t change his physiology, or might have difficulties and/or moral be mean or value-taking.

And that’s why observing people who are very different than us provides the biggest bang for the bug in terms of data-gathering.

  • Learning from negative examples

As Anon said, removing bad knowledge, as well as removing behavior and strategies -“negative knowledge”- is as important as adding new one.

This is also important when you actually know what’s poor behavior.
People don’t study how to perform better in order to fail.
And social researchers don’t learn social strategies to fail.

So, especially when their skills grow further, their best personal experiences when it comes to failures might come from other people. And they’re as precious as the positive examples

Advice to Expand Experience

Some tips to learn from personal experience:

  • Try different things: the more you get stuck in behaving all the same, the more difficult it will be to change. But trying you must
  • Push yourself out of your comfort zone: do or say something that you wouldn’t otherwise do or say
  • Seek different environments: join people who are very different than you are, see how you can get along with them
  • Seek different people: especially when you’re learning, seek to engage with people are as different than you are as possible

Advantages of Experience

Experience is the first of our pillars.

It’s not always and necessarily the first pillar of any discipline, but it is so for social skills, social strategies, and general social success.
Personal experience when it comes to social skill and social strategies is a “condition sine qua non”: there can’t be social mastery without social experience.

Among the benefits of first-hand experience:

Experience can be reliable if it’s based on repeated successes

If the results have been reproduced many times over, then life experience is possibly the most effective approach to tease out the best strategies for achieving outcomes.

However, three caveats must be added right away:

  1. Those results might not necessarily be generalizable to someone else
  2. Those results might be limited to the environment in which the experiences have taken place
  3. Teasing out those strategies also requires good analysis and critical thinking, especially so if one wants to generalize them to different environments, and to different individuals

With especially poor analysis, one might even ascribe his success to different causes, and teach others the wrong strategies.
And personal biases are always a risk when it comes to analyzing success.
For example, the CEO says he succeeded “because of his intelligence”, and the fact that he was the son of the company’s founder never enters into his biased analysis.

Experience provides highest-quality data points for social intelligence

Few people are born socially intelligent.

Even those who are, they must go through many social interactions before they can become really good.
And for most other people who were born with average social intelligence, we must go through a lot of different social environments to properly develop our social intelligence.

Again, we must note here to develop social intelligence from experience, good critical analysis is necessary.

Experience provides the highest-quality data for pattern recognition

As a rule of thumb:

The deeper broader the experience, the more patterns people can spot and recognize.

And patterns allow for good predictions and strategies, as well as forming the basis for theories and systems.

Again, forming patterns requires analysis and critical thinking. Forming patterns is about “connecting the dots”.
The personal experiences are the dots, and the analysis allows to build bridges and meaning based on those dots, which in turn will lead to greater understanding, and eventually to overarching theories.

Limitations of Experience

For all its importance, there are also important limitations to personal experience, including:

Some results cannot be reproduced many times over

Good experience, as we said, is repeated experience.

Some experiences can be repeated, like:

  • Talking to strangers can be reproduced over and over
  • Approaching the opposite sex can also be repeated over and over

And so can seduction, making friends, developing social groups, or climbing social hierarchies.

But some experienced cannot be repeated nearly as often.

As a matter of fact, in some human endeavors, individual success is inversely proportional to quantity of experience.
This can be problematic to generalize results.

Take for example dating and relationships.
You may say the most successful dater is the man who met his best possible partner on his first date ever, and lived happily ever after.
That type of personal success may not allow him to generalize his experience.

And if a guy is so successful at work that he becomes CEO of the first company he joined… That also limits his experience in different work environments.

Some successful experiences can be borne out of chance, leading to poor mental models

With one-time successes, there is also the possibility that one “lucked it out”.

If a guy teaches entrepreneurship after he’s had one successful business, couldn’t it be that he was just at the right time, with the right product?

Critical thinking can help differentiate those who lucked it out, from those who won because of mindsets and effective strategies.

Personal experience needs critical thinking for higher-level mastery

Experience can not stand alone.

Not for personal mastery, and even less so for teaching master.

Personal experience without good analysis is limited by one’s own experience.

If one is not able to tease out general principles from experience, he will fail as soon as some elements in his life or environment change.

Experience that is very deep, but not broad, leads to one-trick ponies

In “Antifragile” Nassim Taleb says that trained fighters are poor street fighters because of their over-experience in a specific environment (deep experience, but not broad).

I don’t necessarily agree with the example, but the general rule holds.

There is an inverse proportion between the uniqueness of the environment in which the experiences took place, and the transferability of those results to different environments and different people.

Experiences based on unique environments lead to poor general understanding and to poor theories
A successful individual within a specific environment who doesn’t understand the peculiarities of his situation (lack of critical thinking) will likely lead some people astray when he teaches the method that worked for him and within his environment.

Critical thinking can help mitigate this factor.

Personal experience is limited by the individual’s specific conditions

Same as above, but applied to the uniqueness of personality traits, rather than environments.

In general terms:

The results of the specimen do not generalize to the species, and his strategies for success do not necessarily apply to any other random specimen

As an example, take the handsome and financially well-off guy teaching seduction.
Can a handsome man’s strategies also extend to less attractive, jobless men?
Well, some of it, yes, but not all of it.
The handsome man cannot teach effective strategies to everyone unless he also expands his understanding beyond his personal experience.

2. Science


  • Studies
  • Surveys
  • Experiments
  • Big data
  • Tracking data (analytics software)

As well as:

  • Scientific method, approach, and attitude

Science and data complement personal experience and observation and, in some fields, but not in social skills, it takes the lion’s share of the data points.

In social skills, science can be used to confirm or disprove personal experiences or theories.
In the case of confirmation, one can rest safer about the validity and reproducibility of his experience and observation. And the opposite is true: if science disproves his experience-based first-level conclusions, then he knows he must dig deeper.

Also note that the scientific method overlaps with logic and critical thinking skills.
That approach can be applied to both personal experience and analysis, and it enhances the individual’s ability to get to the truth.

Science Added Value

Evidence and data provide a few things that personal experience can’t, including:

Mitigates the risks of inductive reasoning (Fooled by Randomness)

Whenever I hear someone:

  • Rely too heavily on their own experience
  • Base their advice on what they saw, heard, or what someone said to them
  • Rely too heavily on anecdotal evidence

It’s a red flag of someone with little systemic thinking and relying too heavily on “inductive reasoning”, such as going from one case -or a small number of cases-, to the general rule.
And those people are the most liable to be fooled by randomness, such as to jump to conclusions from too small a sample.

Enter, science.
Science allows analyses to be based on more quantitative data than personal experience, and that makes it a central pillar for any good theory.

High quantity of data at once

Data and surveys can provide quantitative data points that more qualitative personal experience can’t match.

For example, it would be hard to estimate, say, the average sexual partner in a specific country just by asking around.

And yet, that’s very important information to assess the sexual dynamics of a given sexual market place (on average: high partner count means easier access to casual sex, higher incidence of cheating, and fewer life-lasting marriages).

Science can provide structured or structur-able data

Same example as above, imagine the social researcher who’s all about real-life experience would stick to asking around.

By the time he’s reached 100 women, he forgot what the first 10 said, he mostly forgot what the next 30 said, and he mostly remembers, and overblows, what the last couple of them said (recency effect & salience effect).

Result: he gets an idea, but it’s such a rough idea, that it’s as likely to lead him astray, as to lead him to a good conclusion.

Instead, with a survey, he can get reliable data about his market, plus subgroup the data into different cohorts (say: religious VS non-religious women), plus compare it with other markets.
And that allows to develop far better theories and systems, which in turn make him a far better strategist.

Experiments offer insights on situations one could hardly replicate in real life

Some real-life situation are difficult to replicate in the lab.

but the opposite is also true: some important notions of human psychology and behavior might be easier to tease out in a lab, than in real life.

Take for example the famous Milgram experiment on obedience.

One could have hardly tested it in real life how dramatically submissive most people get in front of a highly assertive authority figure.
But thanks to the lab, the social researcher can use that information to craft better persuasion strategies (or defenses against persuasion).

Science can help quantify phenomena that would be hard to quantify in real life

While good social researchers have a “feel” for social dynamics, sometimes you need more than a “feel”, and you need to quantify things more precisely.

Studies can help isolate single variables, re-run the experiment with several different people, and provide a more accurate measurement.

For example, measuring the effect of “priming” on persuasion and behavioral change is nearly impossible in real life.
But a series of smart experiments can find out whether -and how well- priming actually works.

Limitations of Science

Recent populist movements aside, our world reveres science.

And, if you ask me, that’s a great thing, and mostly well deserved. Science is a true engine of progress.

However, “revering” anything is actually very un-scientific.
Proper science includes knowledge and awareness of the limits of science and, even more so, of the limits of a specific paper, experiment, or survey.

This is all the truer in the social sciences, where measuring things like influence, power dynamics, and social strategies is particularly difficult.

Some of the limitations of science when it comes to social mastery:

Research-specific limitations

Research limitations and biases can make poorly-designed research not only meaningless, but sometimes actively misleading.

Take the example of priming we’ve just mentioned.
It was exactly when priming failed to replicate that the world of psychology was thrown upside down -and that, too, was largely an overreaction-.

That’s where critical thinking can help: looking at the methodology, or the number of people involved, provide precious information on how much weight to assign to a study.
And the more advanced one becomes, the more he’ll be able to assess a specific study against his large knowledge and experience.

In social sciences, some research applications to the real world can be dubious (external validity)

Some studies can be perfectly executed, and still not provide great insights about real-world dynamics -“external validity”.

Studies of human behavior in the lab tend to have weak external validity, since many real-world factors are distorted or eliminated.

Renowned psychologist Martin Seligman says that external validity is one of the banes of psychology research.
He says:

This is the “white rats and college sophomore” issue: researchers can control and measure what rats and sophomores do in the laboratory, but any finding’s application to real human problems is always a strain

And he adds that “public doubts about the applicability of basic, rigorous science are often warranted, and this is because the rules of external validity are not clear“.

So also research must be taken with a grain of salt.
Critical thinking, supported by personal experience, can step in and help separate the scientific wisdom from the scientific noise.

The Tree-Researcher Can Lose The Forest

Another issue is the specificity of many studies.

And if you were to look at a series of studies alone, without having or without developing a critical-thinking driven higher system, then you’d be missing out on the real wisdom that allows for practical strategies.

It would be like the man trying to find a way out of a forest, with a million coordinates of individual trees, rather than a map of the whole forest. If that man could load all the million maps in his head and connect them all, he’d have huge knowledge. But the human brain doesn’t work that way, so that man is better off losing some individual-specific information, for a more actionable, higher-level picture.

Lots of false positives & false negatives for complex & high-variance

This is another issue connected to specificity of many studies and experiments.

I’ll give you an example:

Betsy Prioleau in “Swoon” says that women prefer women with feminine faces.
So imagine you want to know the “truth” to date better.
Better to look more feminine, or more masculine?
I thought Prioleau was a bit biased and not too experienced herself, so I started researching. Turns out, there are plenty of studies that seem to suggest that women prefer more feminine faces.
And there are also plenty of studies that seem to suggest that women prefer more masculine faces.

This is often the case when we are investigating more complex issues with lots of variance -ie.: different individuals have very different preferences-.

So you will hardly get a final answer by looking at individual studies alone.
A meta-analysis may help. But that needs the critical thinking of those who compile those meta-analyses.
What also helps is a man with critical thinking skills who can spot trends. For example, a man hypothesizes that younger women tend to prefer more feminine faces based on boyband fandom, while older ones prefer more masculine faces based on rock bands fans and personal experience.

Science Power Dynamics & Incentives Do NOT Align With Truth

Scientists are humans, and just like any other human pursue personal interest.

And, often, may place personal interest above “candid descriptions of truth”.

Some of the issues with published research:

  • The full published literature is inherently biased towards larger effect sizes because
    • Publishers are more likely to publish largely significant, shocking, or significant results rather than mild, confirming, or “null” results. That skewes the results of ALL published -and available- studies, including
    • Researchers seek not truth, but results that are most likely to be published. So they “massage” the data or experiment (“p-hacking”). This is probably very, very common in the last decades of published research (and the reason why only a minority of published research replicated, or replicated at similar levels of significance).
  • Fraud, or wholly made-up data
    See for example the cases of Francesca Gino and the Stanford Scandal. Albeit without any concrete evidence, even more worrying to me are the comments I’ve read of many former researchers who have been threatened or mobbed into ignoring data fudging.
  • Go from scientist to “guru”, publish too early, over-promote already fudged data, or over-sell smallish (and little effective) findings.
    For example, Amy Cuddy and her power poses. Or Angela Duckworth and “grit”. Even valid tools like a “growth mindset” can turn ineffective when taken to an extreme and when forgetting about the cons.

The popularization -or bastardization- of science is often the hardest to correct because it acquires huge megaphones when it trickles down to various clueless but popular self-help gurus.

A good chunk of pre-replication crisis results are overstated or misleading

The incentives were particularly badly skewed before the replication crisis.

And researchers had much more freedom and leeway to “torture the data” until it showed the results they -or their bosses- wanted.

This is NOT to say that you can’t trust any paper published before the replication crisis, of course.
But it means that you must add some extra grains of salt to… A heck a lot of research, I’m afraid.

3. Critical Analysis

Critical analysis is the CPU that makes sense of all the available data.

Good critical analysis could be boiled and simplified as “thinking well”.

Once upon a time, I used to think:

The Power Moves’ popularity comes from combining experience, with science.

Today I think differently.

There are a million folks who create based on their experience and over-generalize and/or jump to the (wrong) conclusions.
And a million folks who read a few papers, barely understand them, never consider their limitation, and jump to conclusions that they brand as “scientific”.

So today I believe that the true added value of this website is in the critical analysis.

Critical analysis includes:

  • Critical thinking
  • Logic principles
  • Skepticism, that protects us against the countless fake gurus, bullshitters, and well-meaning but poor thinkers
  • Scientific method and principles, also applicable outside of research
  • Falsification / critical refutation. The process of looking at why a theory or opinion may not be true. Or not apply to specific people or situations. It’s where I see the most people fall short of

There are two levels to critical analysis:

  1. Analysis
  2. Critical thinking

Analysis and critical thinking are the two aspects that allow for systems to emerge (this third pillar was at first called “systemic thinking”).

Some examples of applying critical thinking to developing effective approaches:

  • Analyzing and reflecting on one’s own, as well as others’ behavior and results
  • Asking “what if”, “what could have I done… “
  • Asking others for opinion and strategies
  • Comparing own experience and results with research
  • Comparing own experience and results with other people’s advice / theories
  • Incorporating latest experiences with general understanding / theories
    • Reinforcing the theory if the experience matches
    • Revising and improving the theory if the experience fits but adds a different shade
    • Or asking “why this time it was so different” if the experience goes against the theory and the previous experiences

3.2. Analysis

Analysis is the mental engine processing and structuring the data.

There are two levels of analysis, one is to connect the dots, and the other is to raise the newly connected web to a higher level (systemic level).

3.2.2. Making sense of the data

Analysis looks at experiences and data to make sense of them, and learn more from them.

Critical analysis empowers us to learn from experience.
Not all analyses are created equal, so superior analysts make for better theories, strategies, as well as for better teachers.

On top of learning more from each example, analysis helps to “generalize” the single instances into theories.
That’s the ‘systemic analysis aspect.

3.2.3. Systemic Analysis

Systemic analysis, or systemic thinking, seeks to turn data into coherent theories and actionable strategies.

This is why analyzing one’s own actions and results are so important: it helps to internalize the learning, as well as to place the data points into a larger and coherent picture of “how things work”.

Systemic analysis is fundamental but, as a rule of thumb, the farther analysis strays from data and experience, the more speculative it becomes.
And the farther it strays from good data, the more prone it becomes to being twisted -or manipulated- by personal biases.

3.3. Critical Thinking

Critical thinking empowers analysis, while also at the same time limiting analysis.

I know, this might sound strange.
Why would you want to limit analys?

Because the overzealous analyst might go off on a tangent if left alone. The tendency of analysis is too keep on going and “build upon itself”. That’s where critical thinking steps in and brings analysis closer to reality. Such as, closer to both science and experience.

Critical thinking is the watchdog against over-analysis.

It makes sure that analysis stays factual, includes data and experience, chooses which of the two matters most when they diverge, and it makes sure that analysis does not stray off too far, building “pies in the skies” theories.

Good analysis includes critical thinking.
The two go hand in hand.

But, as we shall see, not all analyses are good analyses.
In social sciences, analyses without critical thinking leads to:

  • Pop psychology
  • Psychologizing (see Freud)
  • Pop evolutionary psychology: a good chunk of what you read in the red pill
  • Generalizations: which in turn lead to poor sub-standard strategies

In short, analysis without critical thinking is often poor analysis, and leads to poor theories.

Critical Analysis Added Value

Solid critical analyses allows for:

Critical thinking deepens understanding through exception-finding

There is nothing like a nice theory where everything adds up.

Except… theories where everything adds up are often a trap.

Referring to entrepreneurship, Jay Samit says that the best ideas are “zombie ideas”. Such as, they are left standing after they survived a thousand attacks, and look ugly and maimed, but… Are still standing, and have potential.
General theories are the same.

Proper critical thinking is not about finding out “why the theory is true”, but more about finding out “why it’s not true”.

And this is why mismatchers -people looking at exceptions, rather than confirmation-, often make for better critical thinkers than matchers.


Simple-minded investment analyses tell you “over the long run the stock market will (always) go up (because it has always gone up)”.
Similarly, simple-minded analyses of human history say that “humanity is in constant improvement”.

Personal experience and data both might confirm that.

But critical thinking is what allows you to find the fault in both data and experience.
Critical thinking tells you that per each increment of future time horizon in a project, our lifespans become progressively more meaningless, until it won’t mean anything anymore when we’re talking about infinity.
And critical thinking tells you can rarely make reliable future predictions by looking at the past because the past might be a good indicator, but (almost) never the past can be the proof of what will happen in the future.

With the same approach, critical thinking can also help unearth the blind spots that data and experience hadn’t even touched.

By looking at what lay outside your data and view, you can sometimes find out that piece you were missing that you can’t even examine… but that you need to research indirectly, or maybe by asking someone else.

The Cherry-Picking Trap

There is an approach very popular these days among self-help gurus.

The approach says that you take “everything that is good” from what someone says, and leave the rest.
That seems to suggest that:

  1. There is always something to take
  2. Any criticism is pointless, since there is always something good to take

That mindset is akin to “taking what’s good from the rotten fruit”.

But at the first hitten of rotten, the fungi spores of the have likely spread across the whole fruit, so there is little “good left”.
This is a bit of an exaggeration, but the main concept is that if you’re not critical enough while doing your wisdom-cherry picking, you’re probably also ingesting a lot of crap.

Furthermore, the moment you spot rotten, you’ve also stumbled upon important signals of poor critical thinking, bias, and generally shaky systems (more on systems later).

Time is limited. So instead of “cherry-picking” among the trash, why not leaving the trash altogether?
Of course there might be one good thing in a book like “The Secret”, which tells you not to work on things but just to “manifest” them -you know, you might have a wobbly table :)-. But you’ll find a much higher
You’re far better off looking for high quality, pristine fruits (theories and authors), than trying to salvage the “good parts” of a rotten theory.

Critical analysis troubleshoots inconsistencies

A poor researcher looks for confirmation.

A great researcher looks for exceptions and inconsistencies, and see if they disprove the theory or not.

There are many types of inconsistencies.
For example, between past and new experiences, or between first-hand experience and personal observation, or between experience and data.

The sign of a great theory indeed is not in having no inconsistencies.
The sign of a great theory is the ability to explain the inconsistencies in a way that either reinforces, or remain consistent, with the general true.

Critical analysis decides whether those inconsistencies weaken, undo, or generally leave a theory intact.
Some inconsistencies can also strengthen a theory, if one realizes they are extremely rare. For example, male attraction for very old women is so rare that it does not undo, but reinforces the general theories that men generally prefer fertile-age women.

Critical analysis tells if personal experience or science shall prevail

In the battle for “data” or personal experience, in today’s world, “science” seems to always bludgeon personal experience.

Yet, that is not always the case.

And critical thinking can help you understand what you should give more weight to, as well as why the two diverge (since in a good theory, they shouldn’t diverge)

For example, imagine in a survey 95% of men say they have more power in their relationship.
And 73% of the women confirm it.
Alright “the data is in”, one might think. But what if personal experience suggests otherwise?

Critical thinking sees a potential for bias in the data: most men wouldn’t feel comfortable to come out and admit “my wife has more power”.
The disparity of the results between genders is also an indicator of “male pride” getting in the way of proper analysis.

And if we are living in a country where femininity and “being a good housewife” is still associated with submissiveness, then that might also explain why most women would actively avoid stating that they have more (soft) power than men have.

In this case, personal observation might be a better analytical tool than data.

And there are of course cases in which data and science trump personal experience.
For example, in deciding which sales page works better, personal experience need not apply against tracking conversion rates.

Critical analysis turns data into theories

We’ve already seen this one scattered in the “experience” pillar.

Good analysis:

  1. Makes sense of data points
  2. Connect the dots between different data points
  3. Generalizes from many experiences and data sets into general theories

Later we will see the benefits of theories.

Advice to Improve Critical Analysis

Since critical analysis is so crucial, here are some ideas to develop it:

  • Let go of biases

You can either learn the truth and in turn develop effective strategies to win, or you can hold to your biases.
To learn more about your biases, read here.

  • Let go of politics

Maybe you’re conservative.
But dismissing off-hand any research showing the positive effects of minimum wages doen’t make you any more right, it makes you more ignorant.

You can accept discordant information with your beliefs while still holding to your political preferences. But by accepting all information you will develop better models of reality, which in turn make you more effective.

  • When disliking somene, ask yourself: what if he was right

Close-minds and biases are the anathema of development, improvement, and good theories and strategies.
If you don’t like someone, or what he says, it’s all the more important you truly consider they might be right -at least about something-.

  • When disagreeing about something, ask “what if it was true”

Similar as above.

There is of course a level at which you can say “OK, I’m not willing to investigate this any further and I’ll consider it not true for now”.

For example, I personally considered “law of attraction” in the sense that you can “manifest” things not true and unworthy of my investigative time.
I still keep a small window open to the possibility that it might be true, but don’t waste time investigating it or researching it.
The day someone proves to me you can manifest shit, I’ll change my mind immediately -and boy, would that be a shocker :)-.

For more, also read:

Limitations of Analysis

Good analyses depends on:

  • Good analysis needs good data points

Critical thinking without real-world experience and/or data is based on… Nothing.

General theories produced without solid data and personal experience are the equivalent of big castles built on shaky foundations.
No matter how great the architect -or the thinker- was, the theory is empty elucubrations with untested hypotheses and strategies.

The same goes for bad data points, including limited, or biased.

Stystems based on very poor data lead to poor real-world predictions, and poor strategies.

  • Good analysis requires absence of personal biases

Good analysis must be unbiased analysis.

Biased analysts make for poor systems, theories, and strategies.

Biased thinkers can still acquire power with their biased theories though. Their bias does not appeal to anyone who wants truth, but it’s appealing appeal within certain politically-motivated organizations, hate-based groups, or people looking for an excuse or outlet for their frustrations.

As a matter of fact, the more skilled a biased thinker is, the more he can twist reality and mislead people -and sometimes himeslf- to believe that his theory is factually “correct”.

End Product: Systems

The end product of the three are higher-level

End Benefit: Systemic Automation

And finally, we get to the end results of the 3 pillars.

The end game is the formation of higher-level systems that allow for quicker processing, automatic behavior, high-level strategic thinking, and replicable results in different environments.

three pillars of knowledge results
The 3 pillars of learning as they start forming systems (then turning into “pillars of knowledge”)

This end goal is always developed by the “critical analysis” pillar.
In that sense, graphically, one may add a roof on top as the “final result” (see here).

There have been different and overlapping ways of defining higer-level representations in psychology, including:

  • Mental representations (Anderson, 2016): Anderson defines it as “pre-existing patterns of information that make it possible to process large amount of information to answer quickly and effectively in certain types of situations”
  • General theories of mind (Fallon, 2013): more tailored to empathy and interpersonal relationships, and it’s the ability to understand what others think and feel
  • Systemic thinking (Gallon, 2009): the ability to think and observe at a higher level of abstraction

They’re all similar.
In simple words, without making things unnecessarily complicated, high-level system thinking means that you understand, and have internalized, how things work (don’t you just love it when you say the same thing, but in simple words?).

And whichever name you prefer, this is what system-level allows you to achieve:

  • Pattern recognition: you meet someone, and can quickly draw conclusions about his/her character. Or you’re in a new social situation, and can and recognize the social and power dynamics
  • Effective information handling: you make sense of the new information coming in and can think faster than others can
  • Effective responses: your actions and responses tend to be more effective than most other people in achieving certain (social) goals, and more and more of your responses become automatic

To stick with our predilection for simpler words, in our case of people skills, higher-level system thinking also means that you have reached advanced social skills level, including:

  1. Good general grasp of human nature
  2. Advanced understanding of general social dynamics
  3. Good understanding of each subset of socialization (dating, relationships, persuasion, business, etc.)

Different social exchanges, like dating, relationships, friendships, or business relationships, all have their own peculiarities.
But they also share a more general, higher-level system of “how people are” and “general rules of socialization” that apply to every single sub-system.

It’s similar to what we said for mating intelligence with this chart:

mating intelligence chart with other types of intelligence

Once you develop the higher level, the ones below all draw from the top one.

In the case of social skills, on top you’d have “general psychology”, then “social skills” rather than “mating intelligence”, and then all different realms of socialization.

For more on reaching social mastery, see Matthew’s post:

Systems Added Value

The advantages of higher-level systems include:

Versatility: turning general principles into quick actions even in unknown situations

High-level social skills turns a bunch of one-trick ponies -your single-entries data points- into a purebred, all-terrain beast that performs in any environment.

A guy who doesn’t connect dots would only be able to perform based on what he’s experienced in the past. Such as, he is limited by his past experience, rather than empowered.
But a guy who connects the dots develops higher-level theories and general principles.
Those higher-level principles allow him to perform even when the environment changes, since he can go from the general theory, to the new specific situation.

For example, you might have never experienced a boss yelling at you, and you might have never seen any research based on “the most effective responses to a boss yelling at you”.
But based on similar experiences, as well as a general good understanding of social and power dynamics, you can come up with a fairly good response even if it’s your first time.

First-time responses might not be perfect, even for guys with general high social intelligence, but they are usually effective enough to perform well.

System-level understanding makes for great teachers

System-level thinking allows people to teach well, since they can apply the general principles to different people, in different situations.

That’s why it’s better to not just choose people who are good at doing something, but people who are good at doing something because they understand the principles and dynamics.
The former are “just” good performers. The latter are also good teachers.

Speaking of teachers:

Failures of The 3-Pillars Approach

In simple terms, it’s “crap in – crap out”.

The more crap at each link of the “knowledge chain” the poorer the theories, the poorer the outcomes based on those theories.

Bad data points (poor bricks) and bad data analysis (poor construction process) make for bad theories (poor buildings)

The Law of Balance

The law of balance also applies to epistemology.

The three pillars need to be in balance.

Any excess into one of them without being balanced by the others leads to distortions.
And distortions lead to poorer final theories.

The failure of the 3 pillars systems they’re not balanced

The failures are:

  • All science / empirical data, lacks real world testing. Lacks an overarching theory. Can’t offer any guidance on novel situations outside of the specific test conditions it relies on
  • All experience, fails to systematize, generalize, and replicate for others. Struggles to self-correct and improve as well
  • All analysis, fails to ever test its theories.

Intersecting 2 pillars with each other improves the outcomes.
But still not ideal.

Let’s now look at some examples of failing to apply all the 3 pillars:

Freud: Little Data, Much Analysis. Castles In The Skies Philosophy

Freud was a high-powered, potentially dark-triad smart-alec
  • Personal experience: medium to high
  • Science: low to very low
  • Analysis: very high
    • Critical thinking: very low

Freud’s personal experience came from his praxis, so it’s medium/high.
The data was also from his praxis, but it wasn’t properly quantified and systematized, so it’s very low in scientific relevance.

Freud is very telling, since he was, in a way, a genius.
But he went too far, and history proved him to be a genius… Who was also full of BS.

It’s an important cautionary tale.
If you stray too far away from empirical evidence, you’re almost bound to get out of touch with reality and build too many “castles in the skies”.
No matter how “genius” you are.

It becomes self-refential theory that builds upon itself.
The connections are among your theoretical constructs and “intuitions”, not among good data points.
The odds of spurious and random connections increase exponentially.

Freud’s castles in the skies sounded like coherent theories.
But they only had internal validity within the confines of his theoretical system.
It had little grounding in data, so it was philosophy.

There is more in Freud’s case.

Freud may have indeed been more after power, than after truth.
Jordan Peterson, for example, says that he started a semi-cult (Peterson, 2021).

And says Seligman (Seligman, 2002):

The events of childhood are overrated (in Freudian psychoanalysis).

It has turned out to be difficult to find even small effects of childhood events on adult personality, and there is no evidence at all of large—to say nothing of determining—effects.

This means that the promissory note that Freud and his followers wrote about childhood events determining the course of adult lives is worthless.

Marting Seligman

Of course, these castles in the sky can be very interesting.
We’re not dissing philosophy here.
It’s just not the best tool for our purposes.

Tucker Max: Experience, No Science, No Analysis: Unreplicable

Tucker Max was a “frat boy’ type of player.

He had relatively good success sleeping around with an “asshole style“.

mate book cover
  • Personal experience: very high
  • Science: low
  • Analysis: medium
    • Critical thinking: low

Tucker Max is the author of a few books on “being an asshole“, and he launched a few products on dating seduction.

Tucker has lots of first-hand experience in his field, since he was sexually successful as a frat boy and, once he became famous, he passed the thousand on partners’ count.
Yet, his experience is limited to:

  • University
  • Asshole style
  • With women who sought him because he was famous

That type of experience is extensive in depth, but limited in breadth.

Tucker’s teaching could still be very useful for frat boys and people who became famous.

But most guys are not frat boys, and they’re not famous.
That makes Tucker Max an example of a personally successful man, with high experience, but a poor teacher for others.

Homo Economicus: Much Science & Analysis. But Poor Critical Thinking. Fails At Life

The “homo economicus” is an ideal of human reason that executes perfect decision-making, in perfect rationality, to effectively maximize his returns.

Early game theorists and some economists developed theories and mathematical models around this man… Who doesn’t exist in real life.

  • Personal experience: low
  • Science: very high
  • Analysis: high
    • Critical thinking: very low

Says psychologist Richard Thaler, on the limitations of the “homo economicus” theories:

As economists became more mathematically sophisticated and their models incorporated their new methods of sophistication, the people they described evolved as well.


Calculate the present value of social security benefits that will start 20 years from now? No problem!

Stop by the tavern on payday and spend intended for food? Never!

Thaler, “Misbehaving

Perfect example of:

  1. Science getting out of touch with personal experience
  2. Analysis building upon itself unchecked by critical thinking

That data and science was good in terms of internal validity, but very poor in actually describing reality (poor external validity).

A proper critical thinking function should have stepped in and said: “wait a second, do the people I see every day around me act like perfect rational creatures? Actually they don’t… “

Mainstream Books & Podcasters: Little Everything. Wastes Your Time

Apparently, in just a few weeks during the Covid pandemic Joe Rogan had become an expert on infectious diseases, masks, and vaccines. A good case that little science without critical thinking misleads more than help

Finally, we get to the various “thought leaders” and mass-market popular books.

They’re written by non-experts and non-scientists.
To sound authoritative, these authors read some papers here and there and quote “science”.

However, they don’t have the knowledge and critical thinking to properly assess those papers, and put them in perspective of the full literature.

So they end up misleading -themselves and others- more than they add any value.

Little science is worse than no science at all

– Lucio Buffalmano

Picking The Right Teachers

Now we’ll explore a few ideas on how to pick the best teachers.

Spot the Marketing

It’s common for marketers to present one of the approaches as a selling proposition:

  • Experience: “I’ve got tons of experience in this”, “I’ve seen my students crush it with this”
  • Science: “This is science-backed”, “all based on hard-data”
  • Systemic: “It all adds up, just plain-sense advice”, “prove it wrong, if you can” (and gets into a dialectic diatribe)

Obviously, marketing is marketing, and it’s fair game. This website also markets PU as “evidence-backed” in some of its sales copy.
But your task is to make sure that the individual who wants to teach has all the 3 pillars well in place.
In my experience, pushing too hard on a single pillar is a red flag that one might be lacking in the other two.

In this example marketer Derek Drake plays down his personal experience, which makes him come across as (fake) genuine, while playing up the science & systemic pillars of his product:

A marketing email pitching “Shogun Method“, one of the sleaziest marketing I’ve ever seen

Spot the Over-Reliance on One Pillar

Rule of thumbs:

  • When you see someone relying only or too much on anecdotal evidence and events in their lives…
  • When you see someone quoting study after study, dropping references left and right, and saying things like “the data says… “
  • When you see someone building overly complex and convoluted systems and theories, with little experience and little data…

Then make sure your critical thinking is activated.

Spot the Bias

The most dangerous teachers seem to have all 3 pillars, but are marred but high personal bais.

Personal biases include:

  • Theories developed to justify personal anger / jealousies (“that race is inferior”, “that gender is bad”, etc.)
  • Theories developed to justify personal interest / political beliefs
  • Sharing what’s good for the teacher, not what works (feminism is an example)
  • Information presented to increase people’s dependency on the teacher, rather than independence (keep in mind the conflict of interest)

Albeit this website is very “red pill”, the red pill itself is biased, so we’ll take some popular red pill theorists as examples:

  • Little or no exceptions, the general rule always apply

There is almost no rule without exceptions.
Making little pace in one’s theory for exceptions is either the sign of poor critical thinking, or of bias.

  • One party is almost always virtuous, the other almost always bad

In the red pill there is a constant message of women being amoral, while men are virtuous.
Albeit it’s certainly possible that is the case, when one group is depicted as “generally OK” and the other as “generally bad”, that should at least raise a big red flag of bias.

  • Lots of black and white, few grey areas

Like Robert Sapolsky says in his monumental review of human nature:

It’s complex

Actually, once you reach a good level of understanding, things are not that complex. System-level thinking after all is about simplifying information into higher-level, coherent narratives.

Still, the higher you go in abstraction, the more exceptions you’ll have.
And, as a rule of thumb, things with people are rarely binary and black and white, and it’s more shades of grey.

So when someone’s theory or teaching is very black and white, it’s usually a sign of poor understanding of the deeper complexities -or of bias-.

The Power Moves Approach

Three-pillars, or nothing

The Power Moves is based on the 3 pillars of knowledge

Here at TPM I chose to tackle topics where I have all 3 pillars in place.

That’s not always possible, though.
In some fields, not all 3 sources are available.
For example, when it comes to career strategies, there is little science and research available, and I personally have a brief experience (albeit large in breadth and deep observation).
But since most mainstream advice is full of platitudes, such as very low in analysis and critical thinking, I believe The Power Moves can still add lots of value there.

Finally, much of power dynamics as a discipline is not codified.
So part of this website’s work is to put together the body of knowledge around power dynamics, and systematize it.

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