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An Introduction to Mental Models

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Charlie Munger explains worldly wisdom:

Well, the first rule is that you can’t really know anything if you just remember isolated facts and try and bang ’em back. If the facts don’t hang together on a latticework of theory, you don’t have them in a usable form.

You’ve got to have models in your head. And you’ve got to array your experience both vicarious and direct on this latticework of models. You may have noticed students who just try to remember and pound back what is remembered. Well, they fail in school and in life. You’ve got to hang experience on a latticework of models in your head.

What are the models? Well, the first rule is that you’ve got to have multiple models because if you just have one or two that you’re using, the nature of human psychology is such that you’ll torture reality so that it fits your models, or at least you’ll think it does…

It’s like the old saying, “To the man with only a hammer, every problem looks like a nail.” And of course, that’s the way the chiropractor goes about practicing medicine. But that’s a perfectly disastrous way to think and a perfectly disastrous way to operate in the world. So you’ve got to have multiple models.

And the models have to come from multiple disciplines because all the wisdom of the world is not to be found in one little academic department. That’s why poetry professors, by and large, are so unwise in a worldly sense. They don’t have enough models in their heads. So you’ve got to have models across a fair array of disciplines.

You may say, “My God, this is already getting way too tough.” But, fortunately, it isn’t that tough because 80 or 90 important models will carry about 90% of the freight in making you a worldly wise person. And, of those, only a mere handful really carry very heavy freight.(1)

Biases emanating from the Availability Heuristic:

1. Ease of Recall

2. Retrievability

Biases emanating from the Representativeness Heuristic

3. Bias from insensitivity to base rates

4. Bias from insensitivity to sample size

5. Misconceptions of chance

6. Regression to the mean

7. Bias from conjunction fallacy 

Biases emanating from the Confirmation Heuristic

8. Confirmation bias

9. Bias from anchoring

10. Conjunctive and disjunctive-events bias

11. Bias from over-confidence

12. Hindsight Bias

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