Lessons from: The Great Mental Models Volume 1: General Thinking Concepts

Name: The Great Mental Models Volume 1: General Thinking Concepts
Author(s): Parrish, Shane ; Beaubien, Rhiannon

Synopsis

"The Great Mental Models" is part 1 of 3 books by Shane Parrish and other authors to provide models/frameworks that can help us view situations from diverse perspectives, make better decisions, decode complexity and generally better navigate our lives. This volume provides concepts (models) of a general nature (unlike the other two which delve deeper into "Physics, Chemistry and Biology" and "Systems and Mathematics"). This volume provides 12 such concepts.

Mental models are basically ways to understand how the world (our lives as we live them every day) works. Our lives involve navigating a complex system of systems (social, cultural and so on) and models provide us a way of simplifying them to get to the core/root of subjects (after which we can do with them what suits our goals). As we learn, contextualize and start using models we'll get better at navigating our lives and apparently new situations will seem a variation of a model that we've dealt with before. Journaling (or blogging if you're so inclined) is a good way to ensure that we are gaining and learning from the experience of using models.

Core ideas

1. The world today is complex (increasingly so) that its not possible for anyone to be omniscient, hence these models are useful. Apt, therefore, that the author(s) start the book with this as the first model all of us need to understand: "the map is not the territory", which is basically another way of saying that we should realize the usefulness of models only as an approximation/ simplification/ abstraction /reduction of reality and keep that in mind always while using them. We should not make the mistake of thinking that the model itself is reality and start taking decisions that make sense in the model without first examining whether reality is still concurrent with the model (or has it changed since the model was last updated). Whenever we make a decision based on the model that turns out to be incorrect, then its the model that needs updating (and not reality that needs to be "rejected").
2. The world is (and has always been) interconnected and while we may isolate a specific field of study while assuming the rest of the world to be ceterus paribus, its important that we bring back that knowledge and fit that (like a jigsaw) into a latticework of mental models. The better way to learn models is by creating a "meta model" which should allow for "meta thinking". So, create a model of models where, whenever you learn a new model, you fit that with the other models so that you can see the entire machinery work together. This sounds hard, maybe it is, but it makes sense to me - And as is pointed out in the book, the number of models one will have to learn will still pale in comparison to practically countless number of specific concepts out there. Plus, I think pareto principle will apply here too and after, maybe, 10-12 models we'll have covered most of them to have a working meta model. Basically, if you're learning things by rote without putting them in relation to the rest of the world (and studying that interplay) then you might as well not "learn" at all.
3. You can cut through pseudo-science (and other things non-scientific) by applying the principle of falsifiability to it - Basically, if it can't be proven false then be careful about accepting it as true.
4. Reasoning from first principles is important: it can help us clarify complicated problems by separating the underlying ideas or facts from any assumptions based on them. To apply this, there are two techniques we can use: Socratic questioning and the Five Whys.
5. Do not confuse necessity with sufficiency: the latter is a subset of the former. If you're doing what is necessary, it does not mean what you are doing is sufficient.
6. Develop a good understanding of probability: Since that is what most of life is - It will allow you keep a level head and take level decisions. Bayesian probability must also be learnt for good measure since everything in life is connected to everything else. Be careful about systems/situations where more "fat tail" curves have been observed, which basically means that extremes are more likely to occur that you might not be prepared for (the bad extremes that is).
7. Consider second order consequences as a way to make better decision: Can be reduced to: "a moment on the lips, a lifetime on the hips". Another useful (but related) idea that comes later in the book is of inversion (or as I have heard used elsewhere "back casting") - I found this to be especially useful since we often set goals but when we make the plans (or systems if you're so inclined) to achieve those goals they might not always be connected to the end result we want to achieve. Inversions helps by having us ask the question "Say, that I do in fact end up achieving the goal - If that actually happened - Then what else would have to be true about my life that would make the achievement of the goal a mere logical consequence?" - This is really useful to me and I will be using it going ahead. BTW, alternatively one can also ask: "For me to achieve my goal, what are those things that must not do?". Also BTW, inversion can help us understand if we're dealing with true causation or mere correlation.
8. Simpler explanations are more likely to be true than complicated ones (Occam's razor).
9. Don't attribute to malice what you can to stupidity/laziness (Hanlon's razor): Allows us to chill and focus on correcting genuine problems with the world instead of thinking that people are out there to get us. Needs a bit of experience to get right though, type 2 errors can have serious consequences here.

Notable quotes

• The skill for finding the right solutions for the right problems is one form of wisdom.
• Organizations over a certain size often remove us from the direct consequences of our decisions.
• The further we are from the feedback of the decisions, the easier it is to convince ourselves that we are right and avoid the challenge, the pain, of updating our views.
• The chief enemy of good decisions is a lack of sufficient perspectives on a problem.
• If you get into the mental habit of relating what you’re reading to the basic structure of the underlying ideas being demonstrated, you gradually accumulate some wisdom.
• What successful people do is file away a massive, but finite, amount of fundamental, established, essentially unchanging knowledge that can be used in evaluating the infinite number of unique scenarios which show up in the real world.
• No model contains the entire truth, whatever that may be.
• Whenever we are getting advice, it is from a person whose set of incentives is not the same as ours. It is not being cynical to know that this is the case, and to then act accordingly.
• This means a good theory must have an element of risk to it—namely, it has to risk being wrong.
• This is why any comprehensive thought process considers the effects of the effects as seriously as possible.
• Stupidity is the same as evil if you judge by the results.
• Failing properly has two major components. First, never take a risk that will do you in completely. (Never get taken out of the game completely.) Second, develop the personal resilience to learn from your failures and start again. With these two rules, you can only fail temporarily.
• Luckily there is a way to tell between a real improvement and something that would have happened anyway. That is the introduction of the so-called control group, which is expected to improve by regression alone.

In closing

Good book, always one that can be thumbed through from time to time to keep our thinking fresh. Should be taught to high schoolers to create foundation for thinking well.