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The Great Mental Models, Volume 1: General Thinking Concepts by Shane Parrish

Apr 27, 202011 min read

“The quality of your thinking depends on the models that are in your head.” ~ The Great Mental Models, Volume 1: General Thinking Concepts1 by Shane Parrish & Rhiannon Beaubien

Thoughts

I was introduced to The Knowledge Project2 by AJ last year during one of our 1-1 walks around the campus. He sent me the link to Naval Ravikant’s podcast3 after the walk, and I listened to it in the bus on the way back home. It was one of the best podcasts that I had listened to and was totally taken aback. It was like listening to a philosopher. I subscribed to the podcast and to the newsletter. One of the newsletters earlier this month spoke about the release of Volume 2 of The Great Mental Models. And I was like, wow, I haven’t even read the first volume. In order to be not left behind, I bought the kindle version of the books right away and started reading it.

It is a good read. But I think, it is one of those books where the hardcover would be better. Better formatted, with right things in the right pages. The book has many ‘sidebar’ anecdotes or snippets that are very interesting but break the flow of the current topic at hand. Also, sometimes a picture that a paragraph refers to, could be found a couple of pages later. A kindle book is great when the content is mostly just text and it flows continuously. Going by the pictures of the book pages on fs.blog4, looks like the hardcover would be a good buy. Probably will wait for all the books in the series to come out.

What is a Mental Model?

As the book says, ‘A mental model is simply a representation of how something works.’ It is a way of looking at things and approaching problems that are presented to us. The chief goal in life, i.e if you want a comfortable life, is to have as few blind spots as one can. Either you work in a narrow area of your expertise or learn fast or try different things, you’re always consciously or unconsciously trying to figure out how to make tomorrow better than today. I feel that everyone has a mental model of what is needed to effectively do something. They are all probably smaller variations of some universal mental models. Knowing more, would enable us to think better and make informed decisions.

The book presents nine mental models as ‘general thinking concepts’. They are things that you’ve probably already heard of of know, but it is presented in a simple manner, with great quotes and memorable anecdotes.

The following are the mental models -

  1. The Map is not the Territory
  2. Circle of competence (Supporting Idea - Falsifiability)
  3. First principles thinking
  4. Thought experiment (Supporting Idea - Necessity and Sufficiency)
  5. Second-order thinking
  6. Probabilistic thinking (Supporting Idea - Causation vs Correlation)
  7. Inversion
  8. Occam’s razor
  9. Hanlon’s razor

Notes

1. The Map is not the territory

In ‘The Map is not the territory’ the Authors outline that the ‘map’ is a miniature version of the land and not the land itself. Knowing the map can help navigate the terrain but only going on the terrain gives full understanding of the terrain. Maps are very useful since they abstract away a lot of complexities and information bloat. When we read news or a book summary, we’re reading abstractions created by other people who have condensed the information to smaller chunks to represent the actual information. The key issue with using maps is many times, we mistake map for the reality.

If I think of religion, I think though most religions have doctrines that are universal, many have rules that were written in the context of some particular scenario which has probably no relevance now. Map is useful only if we’re able to use feedback loops to update the map based on the changed terrain.

The chapter gives example of Newtonian physics and how it couldn’t explain interstellar phenomena that Einstein’s Special theory of relativity explains well.

I didn’t quite understand if the sidebar on ‘Tragedy of Commons’ was cautioning against every person looking out to profit for themselves or not. Probably it means that if we’re working on a shared resource, the map becomes useless if everyone does things that doesn’t align with ‘sharing’ principle.

In order to use the map effectively, one must take 3 important considerations -

  • a. Reality is the ultimate update - The actual terrain may be different, map might have missed something.
  • b. Consider the cartographer - The map maker might not be objective. What you might be seeing is one representation.
  • c. Maps can influence territories - It is dangerous to fit complexity into simplification or extrapolate wrongly.

The chapter concludes by a short snippet about Frederick Taylor’s Scientific Theory of Management of breaking down tasks into smaller pieces and specializing people which worked great on the factory floor but had to be changed with time in different office settings. I couldn’t but wonder if Agile methodology is same as this!

2. Circle of competence

When one knows little, there is a sense of feeling of high confidence. The unknown unknowns are a lot. The chapter talks about a novice vs a lifer. A lifer is someone who has been in the place or industry for a long time and knows a lot of things about the domain. It would always make sense to get inputs from a lifer. To have a lifer as a mentor would be a great asset. Having sherpas as guides to climb Mt. Everest is a good example. A lifer/competent person knows what he/she doesn’t know and is able to make quickly and relatively accurately.

Also, even if you are an expert now, you should not operate as though the circle of competence is static. You must keep the knowledge updated. There are 3 key practices to build/maintain the circle of competence.

  1. Willingness to learn
  2. Monitor track record in areas that you have/want to have competence
  3. Occasionally solicit external feedback

3 parts to successfully operate outside the circle of competence -

  1. Learn by basics while acknowledging that you’re a novice.
  2. Talk to experts
  3. Use a broad understanding of the basic mental models of the world to augment your understanding of the new field.

The chapter cites examples of Queen Elizabeth 1, Warren Buffet’s advice on not straying from the circle of competence.

Falsifiability - If you can’t prove something wrong, you can’t really prove it right either. if you keep eliminating things that don’t work, you strengthen things that work (evolution example). Karl Popper, who propounded the theory of Falsifiability, also put forth another idea on our false notion of historicism. We use examples from the past to make definite conclusions about what is going to happen in the future. This is dangerous. Think of the worst events in history. We look at the worst in the past and prepare for that. But the future could be worse than the worst.

3. First Principles Thinking

This is a way of breaking down any complex system or ideas into simple fundamental things, or building blocks. Once you see everything in terms of first principles, you’d be able to see how disparate things are connected. It is like taking apart a computer or a machine and putting it together. Once you’re able to do that you’re not only able to debug issues faster but also build new systems from the building blocks.

Two techniques - Socratic questioning and the Five Whys. Socratic questioning - disciplined questioning process, stops one from relying on gut and limits strong emotional responses. Five Whys - Determine the root cause of something by repeatedly asking ‘Why’. Each why is based on the previous answer.

First principles thinking gives amazing results and innovative breakthroughs. E.g - H.pylori (stomach bacteria) discovery by Marshall and Warren, Curved carrier chute by Temple Grandin,First principles of meat, etc.

4. Thought Expriment

Thought exeperiment is a technique wherein one uses imagination to think through things that are not in existence or are not available currently. Lot of philosophical ideas have been based on thought experiements.

The chapter examines 3 areas in which thought experiments are very useful -

  1. Imagining physical impossibilities - e.g. Einsten’s theory of Relativity or the train and the platform example in Stephen Hawking’s ‘A Brief History of Time’.

  2. Re-imagining history - This is one of my favorite things to think about thing. Love TV series like ‘The Man in the High Castle’, ‘Outlander’, ‘12 Monkeys’, ‘Battlestar Galactica’, ‘The Expanse’, etc. which dwell not only on alternate history but also possible futures.

  3. Intuiting the non-intuitive - A thought experiment allows us to verify if our gut instincts are correct or not.

Necessity and Sufficiency -

The gap between what is necessary to succeed and what is sufficient is often luck, chance, or some other factor beyond your direct control.

The hard truth - There could be thousands of people who write well and are able to publish but everyone cannot be J.K Rowling. Talent and hardwork is necessary to become a published author but not sufficient to become J.K Rowling. The gap, is huge!

5. Second-Order Thinking

Almost everyone can think of immediate consequences of doing something. But about the ripple effects? Second-order thinking is the ability think farther than immediate consequences. A lot of disasters in the world could be attributed to lack of second-order thinking. Example - During the colonial rule, The British wanted to reduce the number of poisonous snakes in Delhi and announced a reward for people who brought in dead snakes. People did. By growing more snakes to make money, they inadvertently increased the number of snakes! Use of plastics, smoking, bad city planning, overgrazing - there are too many examples of lack of thought when we look around the world.

Stupidity is the same as evil if you judge by the results. ~ Margaret Atwood

What a quote !

The key think is to think forward as much as possible with all the information at hand. “Haste makes waste!“

6. Probabilistic Thinking

Probablistic thinking is to try to estimate the likelihood of an outcome by using data at hand. The whole world probably runs on Probablity! A self-driving car, a complex search algorithm, a Machine Learning model, they’re all using probability to produce an outcome that is most likely. The same is probably true of our minds also. The feedback loop is pretty great for our brains though. The success of ANN (Artificial Neural Networks) is greatly due to it being modeled after neurons. Probablistic thinking avoids analysis paralysis and also waiting for ever for all the perfect data to be made available.

Three important aspects that you need to be aware

  1. Bayesian thinking / Conditional probability - When making uncertain decisions, it is always important to check prior conditions.
  2. Fat-tailed curves - Beware of fat-tailed curves. They distort risk estimations. A small error is measuring the risk of an extreme event can mean we’re way off - by orders of magnitude, not 100 fold but 10,000 fold. I think the corona virus epidemic and the lack of preparation is a good example.
  3. Assymetries - How often do you leave “on time” and arrive 20% early? Almost never? How often do you leave “on time” and arrive 20% late? All the time? Or when you aim to get 25% returns on an investment, are you more likely to get 10% (-15) or 40% (+15) ?

7. Inversion

Inversion is a technique to think from the opposite end of the problem. Flip the problem, think backward, start from the end rather than beginning, etc. The chapter gives example of Edward Bernays’s revolutionary campaign to get women to smoke cigarettes. He approached by problem by imagining how it would be if smoking was already an accepted norm. If you’re stuck with something, invert. Even if not stuck, it always helps to think from different angles.

8. Occam’s razor

The simplest solutions are most likely to be the right ones. Simple explanations are more likely than complex ones. David Hume was very cautious about believing in miracles. Vera Rubin was able to explain behavior of galaxies using the simpler theory of ‘dark matter’

9. Hanlon’s razor

A lot of things that we attribute to malice might be more easily explained by stupidity or laziness. Brings me back to the earlier quote from Margaret Atwood - “Stupidity is the same as evil if you judge by the results.”

Book cover Image Credit: Amazon

References

Footnotes

  1. Parrish, Shane, et al. The Great Mental Models, Volume 1: General Thinking Concepts. Latticework Publishing, 2019. ↩

  2. The Knowledge Project - https://fs.blog/knowledge-project/ ↩

  3. Podcast - Naval Ravikant - https://fs.blog/knowledge-project/naval-ravikant/ ↩

  4. TGMM on fs.blog - https://fs.blog/tgmm/#volume_one ↩

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