The usage of the term "mental model" here seems really weird to me.

Once upon a time, a "mental model" was, in particular, a model: some sort of mental representation of a thing, usually simpler and clearer than the thing itself.

I'll give some concrete-ish examples.

- For anyone you know reasonably well, you can probably make a lot of predictions about how they would react to various things. Whatever you use to do that -- which may well be fuzzy and complicated and largely inaccessible to you -- is your mental model of that person.

- Sometimes you might explicitly model someone as a "homo economicus" money-maximizer, who will always do whatever gets them the most money. This is obviously an extreme simplification, but it has the merit of being something you can explicitly reason about.

- Your mental model of the US government might be that the president makes decisions and everyone else does exactly what he says. This would be a hopelessly wrong model, of course.

- If you're good with differential equations, your mental model of a pandemic might be something like the Kermack-McKendrick SIR model, and that might give you useful intuitions for the consequences of (e.g.) implementing social-distancing measures.

- If you're not so good with differential equations, you might adopt a simpler model that just says that the number of infections is an exponential function of time. Even that's enough to make better decisions than many people do.

But here "mental model" seems to be being used to be generalized way beyond that, to include any way of thinking or broadly applicable idea. A few examples from the Farnam Street list linked near the start of the article: "First principles thinking", "Thought experiment", "Second-order thinking", "Inversion", "Occam's Razor". Some of these are useful tools when building mental models. Some of them are useful things to do, or keep in mind, whatever sort of thinking you're doing. None of them is a model.

In fact, almost nothing in the Farnam Street list is a model. But some of the things in the list are useful metaphors, which you could consider to be tiny models, or model-parts. (For instance: "velocity". Velocity is not a mental model, but you might often want to build mental models that include something you could reasonably call "velocity".)

Is the usage of "mental model" to mean "any thinking tool at all" actually widespread? I've seen it before, but I think the other instances I've seen have been closely linked with this one -- all, I think, quoting Charlie Munger. (I don't know whether Munger's own use of the term is as broad as e.g. Farnam Street's.)

I hope it isn't; the narrower notion in which a mental model is actually a model seems to me a valuable one, and it seems easier to find other terms that convey the broader idea (e.g., "thinking tool", "general principle", "idea") than good replacements for the narrower one.

You're absolutely right. As a follow up I wrote https://commoncog.com/blog/the-mental-model-faq/ where I finally figured out the three categories that Farnam Street was mixing up:

The origins of mental models as a psychological construct may be traced back to Jean Piaget’s Theory of Cognitive Development. However, much of mental model writing today is not about Piaget’s original theory. It is instead used as a catch-all phrase to lump three different categories of ideas together:

1. Frameworks. A large portion of mental model writing is about frameworks for decision making and for life. Frameworks do not sound as sexy as ‘mental model’, so it benefits the writer to use the latter phrase, and not the former. An easy exercise for the reader: when reading a piece about mental models, substitute the word ‘mental model’ for ‘framework’. If this works, continue to substitute for the rest of the piece. You will notice that the word ‘framework’ comes with restrictive connotations that the term ‘mental model’ does not. For instance, writers will often claim that ‘mental models are the best way to make intelligent decisions’ — a claim they cannot make when talking about frameworks (nobody says ‘frameworks are the best way to make intelligent decisions!’). This is understandable: writers optimise for sounding insightful.

2. Thinking tools. A second, large portion of mental model writing is about thinking tools and techniques. Many of these techniques are drawn from the judgment and decision making literature, what I loosely call ‘rationality research’: a body of work that stretches from behavioural economics, philosophy, psychology, and finance. This category of mental model writing includes things like ‘reasoning from first principles’, and ‘cognitive bias avoidance’. The second part of my Putting Mental Models to Practice series concerns itself with this category of tools, and maps out the academic landscape that is of interest to the practitioner.

3. Mental representations. This is Piaget’s original theory, and it references the internal representations that we have of some problem domain. It is sometimes referred to as ‘tacit knowledge’, or ‘technê’ — as opposed to ‘explicit knowledge’ or ‘epistêmê’. Such mental representations are difficult to communicate through words, and must be learnt through practice and experience. They make up the basis of expertise (a claim that K. Anders Ericsson argues in his book about deliberate practice Peak).

tl; i read this as saying that you've not looked into the word 'regime'. seems you're talking about mental regimes while avoiding the word.

This is a great example of bad writing. I think you have an inkling of a premise, but spread it weakly across three paragraphs while muddling through your feelings and misunderstandings about models. You never actually define or defend a position. Couple times you've come close to contradicting yourself, but the premise is so weak I can't say for sure.

I do think you successfully communicate that you struggle with some of the concepts you're trying to refute.

Just looking at your highlighted statements:

> The most valuable mental models do not survive codification. They cannot be expressed through words alone.

Close to stating a premise but you've gone and blown away the the subject of models. I think you're trying to say farnam street is selling snake oil, but you're now arguing experience can't be taught, which is tangential and generally uninteresting

> When Warren Buffett studies a company, he doesn’t see a checklist of mental models he has to apply.

1) You don't know that and 2) "warren buffet studies a company by running through a checklist of mental models" is a claim you've just come up with to refute (strawmen seem to make up the majority of this post)

> How do you know if a computer program is badly designed? You don’t go through a mental checklist; instead, you feel disgust, coloured by your experience.

No. You've indicated you don't understand design, and again that a model = mental checklist, which is your own assertion

Author here. You're right, this was an initial attempt at criticism. Shane Parrish of Farnam Street reached out to me a few weeks after I published this, and then I spent the next year as a member of Farnam Street's learning community, executing a research program around this criticism so as to make it more constructive. A crisp version of that series may be found here: https://commoncog.com/blog/the-mental-model-faq/

The core of this series stems from the observation that all expertise is tacit. Polanyi and Papert has the best articulated expression of these ideas, and they match up to my experience in actually pursuing expertise.

I find quite funny the irony in the situation. You’ve come to the interesting realization (your tacit knowledge / mental model) that most useful knowledge is tacit. You’ve written an article on this, but that is very much like communicating a mental model — and readers are having a hard time making sense of it, and are therefore denying the message ¯\_(ツ)_/¯

You might find interesting the concept of “legibility” as discussed on the Ribbonfarm blog.

Who says my goal is to convince those who deny the message?

As per Papert, you're either ready to learn something or you aren't. The more people who do not get this, the larger the competitive advantage for those of us who do.

I never said you’re trying to convince readers; it is simply interesting to observe the impedance mismatch in play, as yet another case study :-)

The article is clever. By claiming it's impossible to explain good ideas, it preenutively dodges your criticism that the idea is poorly explained ;-)

In fact, simple online searches give ample references to Buffett and Munger writing at length about their models, going all the way back to Graham C's original Intelligent Investor.

Yeah this is a fairly weak article - calling something a _fallacy_ is exceptionally bold, as if it's logically inconsistent in some kind of provable way, and it's only barely backed up by the rest of the content. Sure, I guess taking people's word on a subject that they don't have personal experience themselves isn't the wisest decision. It would be hard to prove that there's _nothing_ to offer from listening to them like the article suggests.

> When Warren Buffett studies a company, he doesn’t see a checklist of mental models he has to apply

He's probably trying to explain the guidance from Farnam Street. The most popular answer on their internal forum on how to use/apply mental models was to treat mental models as checklist. I was a Farnam Street member until last year.

He's definitely trying to explain something, he just isn't sure what it is

Agree that writing could have been clearer and much more concise

The argument is basically a debased take on Wittgenstein’s Ladder, which is itself an incompleteness theorem over the manifold of information.

> When Warren Buffett studies a company, he doesn’t see a checklist of mental models he has to apply.

This is really a very poor and missguided assertion. It's as if the only person worth learning from with regards to how to invest is Warren Buffer, because all those poor miserable bastards who are 1% as successful as Warren Buffet, with their pathetically underperforming $1.2 billion fortunes, certainly can't teach you nothing that will help you improve yourself as an investor. No, no. Either you learn from the guy with a $120B fortune or you are better off doing something else like sitting on your ass browsing Instagram or reading drivel posted on a blog.

It's also COMPLETELY WRONG. Charlie Munger has said several times that the NUMBER ONE thing you should do to solve complicated problems is HAVE A CHECKLIST OF MENTAL MODELS AND USE IT WITHOUT EXCEPTION. So this guy is obviously making crap up. Buffet's business partner is the guy who wrote the fucking book on making investments and problem solving this way.

Cool. Have you done so successfully?

I have bought and read nearly every book about Charlie Munger. My conclusion is that 'have a checklist of mental models' is how he says he does what he does. He reasons a lot by analogy to things he's seen or read before, or to ideas in other fields. This does not mean it is the core of his expertise.

The second nuance is that checklisting works better in 'wicked' fields of expertise (I'm using the definition from Hogarth et all, 2015 https://journals.sagepub.com/doi/10.1177/0963721415591878). Stockpicking is one of those. If you are attempting to gain expertise in a field that isn't wicked, well ... good luck improving by having a checklist of mental models.

That wasn't the point. The response isn't pro-model model or anti-model. The point is the author is saying things that are factually, obviously incorrect. If they'd done even a marginal amount of homework, they wouldn't be claiming "Buffet does this or Buffet does that" when all public information and statements about this exact topic point to the contrary. It signals intellectual laziness at best and willful lying at worst.

I am the author.

I've read multiple biographies of Buffett, I've read criticisms of his approach, and I've read Hagstrom's book on Focused Portfolios (which Munger said was the most representative of what they were doing).

I have attempted to emulate Buffett in the past. It is difficult. Hagstrom failed as well, rather publicly, with his mutual funds.

I've begun to see Buffett not as a stock picker, as he hasn't really done such plays over the past decade or so. Rather, he is a capital allocator in the true sense of the word: he buys companies with high FCF, and reinvests the FCF in either stock if he finds an underpriced opportunity, or ownership of private companies if he does not. In the previous decade, he has mostly done the latter, as it has become more and more difficult to generate alpha in the former. He has also been willing to enter into advantageous contracts (warrants, etc) in times of economic turmoil.

I have demonstrated some understanding of the topic.

Now my question to you is this: are you as well read on this as I am? If so, point me to a chapter in one of the biographies and I will self correct.

That's all very interesting and does show that you're well familiar with the topic at hand. Which only makes me more skeptical of the fact that you have said nothing to back up your original assertion.

This article seems to be saying that theory is insufficient for understanding. Practice is required. I completely agree.

That doesn't negate the value of mental models. Mental models are absolutely required for understanding any complex system. They are literally how we think. They should be refined through direct experience. Listening to experts doesn't hurt either.

What has me worked up is that the examples in the article are just garbage.

A person skilled in tennis or MMA will have no problem communicating to you what it takes to acquire their skill. It so happens the most efficient way to communicate complex physical motions is to model them, then allow the student to attempt them, then critique the student's form. Finally the student will require many hours of practice of the correct forms to build muscle memory. There is nothing in here that says anything about mental models.

"How do you know if a computer program is badly designed? You don’t go through a mental checklist; instead, you feel disgust, coloured by your experience."

You absolutely better have a mental checklist and solid technical reasons for why it's badly designed. Imagine going to your boss, asking to do a complete re-write because you feel disgusted by the code base. How is that going to work out for you? Maybe it's algorithms that are not performant. It has inconsistent abstractions, too much abstraction, or too little. It may not accurately model the problem domain. A feeling of disgust is not going to get you anywhere. Technical knowledge and experience will.

Ok. Clearly this article hit one of my buttons. I think I'm done now.

You can be good at something and not be good at articulating why stuff is good or bad, just that it is. Learning to explain to others does tend to refine your own ability though.

I disagree with this almost as strongly as I possibly can. I've only ever encountered people with middling or mediocre skill in a subject express this attitude. People that are good, are good because they know precisely where the line between good and bad is, AND they've practiced "good" a LOT. Knowing is enough to articulate it. If you can't, you probably don't know it as well as you think.

This leads to a bit of a paradox. The things I'm best at are precisely those things in which I feel I still have the most to learn. Why? Because I know the difference between what I'm doing and what good would be.

> People that are good, are good because they know precisely where the line between good and bad is, AND they've practiced "good" a LOT. Knowing is enough to articulate it. If you can't, you probably don't know it as well as you think.

Formula 1 contradicts you.

One of the things that made Michael Schumacher worth the exorbitant salary he got paid was that he could articulate what happened while he was driving on the track in such a way that engineers could make relevant changes.

Ferrari had lots of really good drivers, but it took Schumacher until they had somebody who could communicate with the engineering team such that they could actually improve the car.

I would argue you're talking about a situation where a different skillset than was traditionally selected for was what actually became valuable. I'll concede that you could have a really really good driver that can't give useful feedback on their particular performance. Cognitive load is extremely high and they may not remember what something felt like as they were going. However, that same driver in the passenger seat could tell you what you're doing wrong with tremendous nuance: entering a turn too early, speed too slow, suspension not in the right position because you didn't set it up right when braking, etc.

What about the example of driving a car with manual transmission? (I admit that's not the most relatable thing everywhere in the world, still). I'd argue most experienced manual drivers are good at it when we talk about normal traffic (where the skill ceiling just isn't very high).

Try and ask a manual driver when/how they shift. Maybe they will give you rough speed numbers, but that's not really how they do it. Despite knowing very well when they have to shift in their own car, they won't be able to articulate/communicate it in a helpful way. It's become muscle memory guided by sound, haptics, speed perception and timings.

Your clutch and transmission will let you know the difference between good and bad very clearly. You practice "good" a lot. You know how to do it. But you can't articulate it.

One could argue that the scenario in question precludes the possibility to "excel". But the striking difference between "new driver learning a manual" and "experienced driver shifting without spending even a sliver of conscious though on it" is a clear separation of "bad" from "good" (not just "mediocre") and should suffice as counterexample to "if you can't articulate it, you're mediocre".

It seems to me like the disagreement here comes down to the difference between “good” and “good enough”. For a lot of habitual, daily life type skills like driving, good enough is usually fine. You don’t need to be Senna to commute in a 5 speed. Most people are middling/mediocre drivers, and that’s okay!

At the same time, I’ll submit that any competent manual driver should be able to sit in the passenger seat and diagnose when a learner is popping the clutch, lugging the engine, etc. If someone can’t, their own driving is probably faulty in ways they aren’t aware of.

>> It seems to me like the disagreement here comes down to the difference between “good” and “good enough”.

Yeah, theres a wide range and a lot of possible words to use. Good enough, Good, Great. Most importantly I think all of those are in contrast to someone who is "bad" at something and the other words to describe that end of the range of ability.

If you are good at something and can't articulate why your consistency is going to be pretty poor. Speaking from experience, I took a bowling class in college and got good enough that I could have "good days" but I couldn't tell you what I was doing differently on those days versus my "bad days". The lesson I took from that was that I was still a pretty terrible bowler and needed a coach to explain to me what I was doing wrong.

It seems to me that the gist of the article is to question whether mental models can be effectively transferred verbally, rather than their existence? I agree with you, sometimes verbally conveyed theory is quite useful and can speed up building mental models. But maybe the fallacy is just that people tend to overestimate the benefit of this aspect (hence buy self-help books etc)?

> Imagine going to your boss, asking to do a complete re-write because you feel disgusted by the code base.

You may have skipped some steps here. How do you know if a computer program is badly designed? You don't initially know it because you went through a checklist. (How would you know to apply the checklist?) How do you let your boss know it before doing a total rewrite? That is a totally separate question.

You should probably read this, then: https://commoncog.com/blog/the-mental-model-faq/

Yes!

Mental models provide context and insight to build on current efforts.

They can’t replace effort.

This article is right in my opinion. This 'mental model' thinking and the surrounding ecosystem of 'rationalists' where it's super popular to me seems like Oprah for slightly smarter tech bros.

The problem with it really is as the author suggests the lack of authentic experience on the one hand, but I think more importantly it's that the idea of "mental models" just tries to hand people a bag of disjointed tools.

When you look at what it means to really understand something and you look at say a world class pianist or something, then you'll almost certainly find they have an integrated perspective on what they do. They don't have model A and model B and model C and a bag of fortune cookie wisdoms, they have tacit knowledge and beliefs that are coherent and whole. Really understanding something ironically often leads to the inability to articulate how it is one understands it, because it's just become integrated into how someone operates in general.

Umberto Eco once made the great point that unread books are much more important than read books because known knowledge pales in the face of everything that is unknown, no matter how dedicated one is to reading. And it's the same thing with these mental models. You're not smarter because you know 200 models or 300 models or 400 models, just like reading 50 more books per year isn't going to make anyone any smarter in a sort of simple additive way.

This whole article, and your agreement, rests on a false dichotomy. "Fallacy" implies learning about mental models is useless. No one said mental models replace experience any more than good notetaking can replace thought; but both are useful in thinking and learning.

fully agree with this position. Mental models are just that, models. they are abstractions of a particular classification of a thing, they convey information. they carry no guarantee of understanding and (in the odd cases given in the article) no guarantee that understanding winner take all markets means you would instantly be an expert at gaming them. You would hopefully understand the essentials of them and therefore recognise them when you saw them. It seems to be me that some are conflating these abstractions as in some way claiming to convey expertise.

Concur. Knowing the literature isn't the same as having done the research, but it is likely to hasten getting through the learning curve.

i just dont agree with this at all. Mental models make no claims about expertise they are abstractions of accepted knowledge and capture the essence of something. They are, after all, called models. Maybe some have mis-appropriated mental models as being as short cut to expertise they simply arent and cannot be. They are simply models, abstractions of what is known. I dont think anyone is saying you will be smarter but you may be enlightened. Models are good at this. Imagine an abstraction of a computer. when all you know is that a computer is a box then your awareness of what that means is restricted to a cable coming our of a box with a switch in it. Take that a level deeper. look at the abstract model of computer architeture and you are then enlightened with the knowledge that there is a processor that calculates things, there is storage to store things you may want to use and there are primitive outputs for graphics and input from a keyboard or mouse. You dont need to know about nand gates, clean room fabrication, cores, ALUs, storage topology of anything to gain some valuable insight from this abstract model. Indeed im pretty sure many succesful software engineers dont really delve much deeper than this but the model, nevertheless, is useful. I cannot guarantee everyone will understand the model but i know many will and i know it will provide valuable insight. For me this is it with mental models. they convey the essence key information but absolutely not expertise and i am still wondering why many people, as you, are trying to argue the case from this perspective it simply isnt the case.

Any field of knowledge that has been dissected into a taxonomy must be dead. On the other hand, the taxonomy can give you words for what you already know, so it is not entirely useless.

I don't understand how you make the leap from knowledge being equated to the field being dead? Taxonomy provides a foundation for others to learn about something.

Of course. But thinking belongs to the practitioner, the point is that you have to do it and not just study the doing of it.

You acquire a mental model by doing the things that lead to having that mental model, not by reading about the model. Memorizing a taxonomy of cognitive biases doesn't necessarily make you a better thinker, anymore than memorizing design patterns necessarily makes you a better programmer.

> Any field of knowledge that has been dissected into a taxonomy must be dead.

As others have said, this is clearly untrue. Consider algorithms, for instance. We have categories like dynamic programming, and genetic algorithms (a subcategory of evolutionary algorithms).

Building taxonomies is the easy part, and occurs long before a field is 'completed'.

Also, building and learning taxonomies allows for discovery of new knowledge. This is especially obvious in mathematics, where you start with rigorously-defined symbols and operations and very little knowledge, and then manipulate the symbols to gain more knowledge. Indeed, it could be argued that taxonomies are necessary for understanding.

Chemistry, biology, botany, and geology are dead?

i think the point they're making is that many learners in a given field (e.g., undergrads) don't typically critique the textbooks for those subjects. it's as if all the knowledge therein is complete and accurate, and research happens at some amorphous fringe beyond the textbook knowledge. it's intellectually "dead" to those learners, not that the subjects themselves are dead.

That may (how shall we know?) have been the point they intended to make, but the statement we have to work with is: "Any field of knowledge that has been dissected into a taxonomy must be dead."

Is there some nuance of uncertainty in there I'm not picking up on?

The word "dissected" is doing a lot of work there. "Any" makes it, arguably, hyperbolic. Obviously we can't take too literally any statement about a field being alive or dead. Research is alive in these fields but pedagogy is mostly not.

ah, can we know anything at all then? can any statement, no matter how forcefully and obviously effused, be absolutely uncertain?

but i digress... as fun as the debate may be, hn is probably not where we solve epistemic dilemmas.

Well,we can put a man on the moon with less computing power than we carry in our pocket, and that's far from the most impressive thing we've accomplished in a long long list.

> hn is probably not where we solve epistemic dilemmas.

The repulsion to things like logic and epistemology on a programming website isn't the type of thing I believe we should strive for or celebrate, but I certainly can't disagree with your assessment.

This article seems to have divided people somewhat. A think a great reconciliation between the practical and the theoretical when it comes to learning is found in Mindstorms by Seymour Papert:

"An important part of becoming a good learner is learning how to push out the frontier of what we can express with words. From this point of view the question about the bicycle is not whether or not one can "tell" someone "in full" how to ride but rather what can be done to improve our ability to communicate with others (and with ourselves in internal dialogues) just enough to make a difference to learning to ride."

What an odd article, I have to admit I spent a good while questioning whether i'd got the my own understanding of the value of mental models totally wrong. My first issue with the article is that i've never really seen any claims that by understanding mental models you will be furnished with the skill to either win at MMA or beat the stock market (or anything else for that matter). This leaves me wondering why these very narrow examples of the value of mental models are used as a foundation to the argument.

Knowledge and wisdom is built on abstractions (this is well established). When i think about how i have distilled the things i know, im certain that its finding the correct abstraction - the correct picture or mental model. for example a mental modal about winner takes all markets allows you to understand that certain markets have this trait and therefore the mental model allows you to identify this class of market efficiently due to a nice terse abstraction. I really don't see the link the author makes between understanding the mental model of a winner takes all market meaning that you should all of a sudden be an expert in how to beat one, you may know the basics but i think most people would also know there is experience, nuance and instinct involved - very indefinite things, unlike a the classification that the mental model portrays. Knowledge is built upon abstractions and therefore mental models are just that, its the classification of knowledge that captures a model (generally static) of the world. In understanding a mental model you get some insight distilled into a neat abstraction. Believing you can win at anything just by understanding a mental model... thats crazy surely !?

It's also odd that there isn't more disagreement with the article. It essentially takes a nonexistent claim, and then debunks it via strawman analogies, with no accompanying proofs.

My favorite part:

> A Little Bit of Epistemology Goes a Long Way

Not if you've somehow come to fundamentally misunderstand the principles.

Although, I can't resist the urge to now read more articles by the author, perhaps this is actually an extremely clever example of gonzo advertising.

I agree. I think the author made some unstated assumptions at the beginning, set a pretty weird straw man, and then used weird examples to knock it down.

IMO mental models are relatively useless for what they're supposed to do on the tin, but they're useful for illuminating aspects of the world that would've been left otherwise undiscovered, like a unknown-unknowns thing. Like this article says, most mental models don't survive codification, but do make for fun reading, similar to a healthy-snack version of books.

It depends on what you do. I read about mental models for a long time before any of them became useful for me. Working at a startup right out of law school there were a few that were relevant for my job, but nothing earth-shattering. Years later in a different job where I make a lot of business decisions, consult internally on marketing strategy, and deal with a lot of metrics, I use a lot of the specific Farnam Street-style models constantly.

> The mental model fallacy is that it’s worth it to read descriptions of mental models, written and aggregated by non-practitioners, in the pursuit of self-improvement and success.

This is also why I think a project manager, if a project has one, need to be technically literate to have positive impact to a project's success

Author here. A crisper version of the criticism may be found here: https://commoncog.com/blog/the-mental-model-faq/

It is, in turn, a summary of a 30k word series on 'putting mental models to practice' https://commoncog.com/blog/a-framework-for-putting-mental-mo... — originally published in Farnam Street's Learning Community. To his credit, Shane Parrish of Farnam Street invited me to share my criticisms in his members-only forum, so I reciprocated by putting in the work to back up the ideas in this piece.

This is like saying "buying a tool set doesn't make you a handyman, you need years of experience"

Duh. You DO need tools at some point though. You can, in theory, build all of your tools from scratch. Assuming you're already familiar with the different types and paradigms of tools. But it might be better to look at a list of options before deciding which tools you want to deliberately practice using

In my humble opinion, mental models are like software design patterns. You kinda have to understand them to understand them, otherwise you end up falling into the "when all you have is a hammer everything looks like a nail" situation, which I guess is a mental model too!

I reacted to this article reflexively in response to the abundance of pop self help content that’s been with us forever, now in the form of non stop podcasts of people essentially giving the same advice. It’s fine, but the self-help realm is just, blah, too much of it can really be counter-productive. In so many words, the author is just echoing “there’s a lot of bullshit out there”.

Your basic Philosophy 101 class teaches interesting mental models like Cartesian doubt. That really shaped the way I thought about things for years to come.

With that said, it’s important to identify models properly. If you listen to MMA fighters or other athletes, you can start to see their incremental approach to increasing training intensity to achieve measurable results. Not parsing that out will leave you with a shadow of a mental model that, in this case, would be mostly comprised of non-core strategies (e.g Always stay positive, have no fear, never accept no for an answer, etc).

The fallacy with this article about a "fallacy": That mental models for a physical activity (MMA) are comparable to mental models about ideas.

Of course that doesn't mean you can learn everything from a mental model. By nature, they abstract away plenty of important details needed for expertise. But they are not deficient in terms of ability to convey some level of understanding.

If you think about it, this has to be the case. In many ways, knowledge is simply a series of mental models built on each other. Yes, much of it comes from empirical observation, but that ends up encoded into mental models.

This article is on the dot. This is especially true with Thiel-ean “secrets”. The most important secrets stay secret for a long time because most people are blind to them — they cannot even be communicated, because they would sound like gibberish/wrong to most listeners (cue: blub paradox). This is why Planck said that science progresses from funeral to funeral.

The other thing about lists is that all the items are roughly equally weighted. In real life, the value is almost never distributed that way — a couple of items absorbed well will often contribute immense value. But we water that down with lists (to make the source sound authoritative), and worse, bury the best stuff down the list for SEO & clicks & “engagement” metrics.

It’s interesting to imagine that as a corollary, this brushes aside almost all punditry, and a lot of context-free college education.

Here’s the thing... a good mental model is worth its weight in gold to a practitioner. There’s something magical about the deep intermingling of theory and practice, with each piggy-backing on the other. To have any shot at that, it is very important to be situated in a context, getting useful feedback from reality. If you think about it, the fields with the best theory also have the best experiments & feedback. Failing that, excessive theorizing is akin to the insanity of a dream world.

I'm convinced that everything has infinite fractal complexity and it's a fool's errand to reduce most non-trivial situations to metaphors or explicit systems. As I get older, I rely on instinct/gut more. Just like a master chess player doesn't see 10 possible moves before him, evaluating each as the start of a sequence. He sees just one move, the best one, based on his experience.

> I'm convinced that everything has infinite fractal complexity and it's a fool's errand to reduce most non-trivial situations to metaphors or explicit systems.

I think I agree with you. I also think there's a dangerous temptation to fail to recognize that foolishness, and cultivate a false impression of understanding by almost denying the reality of things some cherished model doesn't handle. That temptation increases with the elegance of the model and the number of limited cases where it can be applied with reasonable success.

Interestingly enough, people really good at understanding the complexity in systems are also people that seem to fall prey to, as you said, cultivating a false impression of understanding. Taleb is one that I think deserves criticism in that regard.

I stopped reading this kind of stuff a few months ago. I felt the content was very valuable and interesting, but only if you have prior experimental exposure. If not, the content is just like an empty shell. Words are wise, but you just can’t understand why.

I’ll read those books again in a few months/years, after practicing. I’m pretty sure it’ll be like reading entirely new books.

While it is an interesting article and I can agree with some of the arguments in it I can't help but think that in outlining the author's reasoning why reading about mental models is futile it inevitably contradicts itself (which I guess it is at least up front about, not many blog posts tell you to stop reading the blog entirely, halfway through the first section). Then again I suppose that writing a blog post on mental models that on the face of it is trivially false does end up proving the author's point.

On a more serious note, while we're comparing mental 'mental model' models. My experience has been that it's more useful to think of mental models as a vantage point. A good mental model grants more perspective while bad mental models obscure things. Of course this doesn't absolve you of understanding the basic concepts, perspective won't help you if you don't know what you're looking at.

...and it's not always simple to practice/apply mental models after reading them in a list. I used to be a member of Farnam Street until last year. Most of the members weren't sure how they could apply the mental models per their internal forum. The top answer was to use the list of models as a checklist.

Mental Models are definitely useful, so not a fallacy from that point of view.

It's kind of like using analogy as rhetorical device, it works up to a point; but when you get to the details it's not worth extending the analogy.

Mental models can be good for raising questions, but then you have to actually find answers to those questions.

The author of course is correct that communicating a model does not necessarily communicate expertise, and that many important elements of expertise are non-communicable (at least not easily) tacit knowledge.

But what is not addressed is that some mental models very clearly can be usefully shared. There is not a clear and distinct line between a "mental model" and communicating fundamental elements of how something works.

For example, my 8 year old was trying to figure out why his walkie-talkies kept making howling, screeching sounds. I explained the concept of feedback to him and he was immediately able to put that knowledge into practice (keep them further apart, or don't have both microphones on at the same time).

I would argue that for tech and business work, communicable models/techniques vastly outnumber noncommunicable models/techniques.

On the contrary, that mental model was only useful to your 8-year old because he had contextual experiences and was situated in a reality where he could easily explore its consequences. Most conversations about mental models are far less tangible.

> On the contrary, that mental model was only useful to your 8-year old because he had contextual experiences and was situated in a reality where he could easily explore its consequences.

This doesn't really make sense to me. How is it impossible that explaining feedback couldn't have been useful unless he had contextual experience and could explore the consequences?

There's a word hovering in the background throughout this article, but it never makes it onto the page: judgement.

In software development, for example, there is a lot of explicit knowledge to learn, and then there are the mental models about how one should apply that knowledge - e.g. SOLID - but these models do not tell you exactly what to do (in particular, there are all sorts of ways of using the rules to justify bad decisions.) It takes good judgment, honed by experience, to apply the rules effectively.

I was actually going for 'taste' when I wrote the piece, but this is a good word, as it's more generally applicable. Thank you.

> The mental model fallacy is that it’s worth it to read descriptions of mental models, written and aggregated by non-practitioners, in the pursuit of self-improvement and success.

Is this actually something that people believe? I've always assumed that people reading about doing $THING were procrastinating, rather than doing $THING.

No, this is the central strawman.

Is "mental model" an overloaded term? I've only heard it in connection with designing interfaces, which seems quite a bit different than what the article seems to be talking about.

The way I've heard it used in interfaces just means what the user knows (or thinks they know) about how your thing works.

A couple examples from Donald Norman's "The Psychology of Everyday Things" (which was renamed "The Design of Everyday Things" in later editions).

Consider a thermostat, which has a simple dial with an arrow painted on it, with a scale of temperatures printed around it. You can turn the dial to point the arrow at a temperature.

The way this thermostat actually works is that there is a bimetallic strip inside that bends as the temperature changes. When it gets cold enough it bends far enough to close a contact that turns the heater on. When the room warms up enough, the bending of the strip abates, opening the contact, and turning off the heater. Turning the dial modified the distance between the bimetallic strip and the contact. Turning the dial to a lower number moves them farther apart, so the strip has to bend more to close the contact, and so the room has to get colder before the heat turns on.

If you ask people how they think it works, some will know the above. Others will have other explanations.

Some people might think that it is just based on time. The system runs the heater for a variable time controlled by the dial, and then turns it off for a fixed time, and then repeats this cycle. Turning the dial to a lower number reduces that variable time.

There were, if I recall (it's been years since I read it) correctly, a few more explanations given, some quite wrong in the sense that no one would actually build a thermostat that way.

The interesting and important thing from a user interface point of view, though, was that all of them lead to the user doing the right thing when it comes to actually operating the thermostat. If the room is too hot, they move the dial to a lower number. If the room is too cold, they move the dial to a higher number.

The point was that users are going to have some kind of model for how your thing actually works. It is not important that the model they have is actually right--as long as their model leads them to the right control inputs that is fine. Be aware of what kind of models people are going to have, and design your interface to not encourage bad models.

An example of an interface design failure was a refrigerator/freezer Norman had. It had two sliders, labeled "Freezer" and "Fresh Food", both in the fridge compartment. The "Freezer" slider had settings A-E, and the "Fresh Food" slider had 0-9. The instructions said: "Normal settings C and 5", "Colder Fresh Food C and 6-7", "Coldest Fresh Food B and 8-9", "Colder Freezer D and 7-8", "Warmer Fresh Food C and 4-1", and "Off 0".

The labeling of the controls suggest or reinforces a model that leads to someone who wants to adjust the freezer temperature fiddling with just the freezer control, and someone who wants to adjust the fridge temperature fiddling the the other control. Probably something with thermostats in both compartments, each controlling a cooler unit for that compartment, with the sliders each controlling one of the thermostats.

The way that fridge actually worked is that there was a thermostat somewhere, controlling the cooling unit. The output of the cooling unit went through a valve that could direct part of it to the freezer and part of it to the fridge. One of the sliders controlled the thermostat, and one controlled the valve. Nothing really suggested which compartment had the thermostat (assuming that it is even in one of the compartments), or which control was for the thermostat and which was for the valve.

An average user of that fridge who, say, feels the fridge temperature us just right but would like the freezer to be a little warmer is in for a frustrating time of fiddling with the controls. Their model of how it works probably leads to different predictions of control response than the actual model. That way that fridge actually works is far enough away from the models the users are likely to have that the controls should have at the very least been labeled in a way that lets those user know that this fridge is difference.

Maybe label the slider that controls the cooling unit something like "Overall Cooling" and label the valve control something like "More to fridge/less to freezer <--> More to freezer/less to fridge". Still a pain to operate, but at least it is obvious it is going to be a pain. The user who wants a warmer freezer but is happy with the fridge temperature can tell from that labeling that they are going to have to decrease the overall cooling, and change the allocation toward "More to fridge/less to freezer" to keep the fridge temperature the same.

I'm a little tired of "X is a fallacy." Everything is a fallacy, or nearly so. Almost every method by which we naturally and intuitively reason have been shown to be error prone, deeply biased, or at best an approximate heuristic. This is why Wikipedia is able to list a hundred different kinds of fallacies[1] without even beginning to scratch the surface. I'm not a huge fan of teaching rationality by exhaustively listing fallacies because it's endless. But when maintaining a blacklist becomes too onerous, the solution is to switch to a whitelist. This turns out to be much easier, because the constructive list of techniques that work is very short. Of all the ways of reasoning that are intuitive appealing and naturally make sense to us, only two have stood the test of time:

1. Modus Ponens (If A implies B, and also A, then B.)

2. The Hypothetico-deductive model (The guess-and-check scientific method)

And frankly I'm a little suspicious of that second one!

These are better known as "math" and "science," or "deduction" an "induction." And yes, modus ponens is really the only inference rule you need for logic - Hilbert proved that[2].

The other 98% of the algorithms built into our brains are unreliable and cannot be trusted. Consider for example your optical cortex, which is attempts to patch up raw input in a dozen different ways, resulting in dozens of optical illusions, saccadic masking, not being aware of your own blindspot, and so on. We literally can't trust our eyes... or rather, we can't trust the instinctive processing our own brains do on raw visual input. So it is with the other parts of our brain. Or what Kahneman calls "System 1."[3] It's a patchwork of barely functional heuristics.

Scientists learn to shut out that 98% and use only the two reliable systems. Mathematicians take it even further and shut out 99%, leaving only modus ponens and methods of deduction.

People hate that this is true. They want to reason intuitively, naturally. They hope they can patch their hopelessly bugged brains into something useful if they can just memorize and avoid a list of pitfalls. I'm telling you there's a better way. Forget about fallacies. Stop looking for shortcuts like "mental models." Construct rigorous arguments inside of formal deductive systems. Use those to build formal mathematical models that describe reality. Test those models ruthlessly against experimental data, even to destruction.

You know this works. It put a man on the moon, for god sake. It predicted what a black hole would look like, then took a picture of it. It's cured so many diseases and so many problems that our main problem is that we don't have enough problems. Yet people still want to look for shortcuts. I can sympathize with that. We're all busy. But the real choice you face is this: be rigorous, or be wrong a lot.

[1]: https://en.wikipedia.org/wiki/List_of_fallacies

[2]: https://en.wikipedia.org/wiki/Hilbert_system

[3]: https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow