From the physical laws that dictate the natural world, to the social and economic realm that we’ve constructed, there is an overwhelming complexity to modern life.
By learning a few practical mental models we can help reduce that complexity into something more manageable.
Mental models are frameworks for making sense of information efficiently. Used correctly they will help you assess situations and make good quick decisions.
While some include cognitive biases and general concepts like ‘trust’ within the definition, it’s the tools and rules that can be practically applied that offer the most value.
Some are only narrowly applicable to the field they were developed in, others can be applied more generally across fields and in diverse situations. Those models with a wide sphere of influence offer greater value still.
By sticking to widely practical models, we get the most bang for the buck. As investor and mental model extraordinaire Charlie Munger said:
“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. The Pareto Principle
First on many lists of best mental models, the Pareto principle states that around 80% of output is produced through 20% of the input.
Also known as the 80/20 rule, it was spotted by the Italian economist Vilfredo Pareto when he noticed two examples of the distribution in quite unrelated areas:
- 80% of the land in his area was owned by only 20% of the people
- 80% of the peas in his garden came from 20% of the pea pods
Even Pareto himself would likely be surprised at how widely applied this model has become. Today you’ll find all over the place:
- 20% of your relationships cause 80% of the drama
- 20% of your relationships cause 80% of the good times
- 20% of your workout leads to 80% of the gains
- 20% of work accounts for 80% of the productivity
- 20% of your time is responsible for 80% of what you find rewarding
And so on. Using this idea you can work to identify what activities you are better off spending your time on, and which are so wasteful it’s worth finding a way to offload them.
The Pareto principle is the mental model for mental models. As Charlie said, only a handful of models carry very heavy weight. This is one of them.
Making the best decisions possible is usually beyond possible. Figuring things out means drawing from your limited pool of time and energy, and if there’s important information that you just don’t have you can add uncertainty to the equation.
Set the minimum requirements, and select the first option or solution that passes the bar. Don’t seek perfection, just seek ‘good enough.’
This mental model helps us avoid overthinking. There comes a point when investing more time and energy won’t return its value, look to satisfice so that time can be more fruitfully spent elsewhere.
Inversion is about looking at a problem from different angles. It encourages us to not only think about the desired outcome and all the ways we can achieve it, but also about the worst outcomes and what we could do that would cause them.
Sometimes in trying too hard to get the best results we make the silliest errors. Similar to satisficing, inversion opts not for perfection but for ‘good enough.’ Avoid those stupid mistakes before striving for the best outcome.
Per Charlie Munger: “You get further in life by avoiding repeated stupidity than you do by striving for maximum intelligence.”
Expectations also colour our experience of the outcome—if we’re too focused on the best outcome and fall slightly short we’ll be disappointed, but if we aim to pass the bar and clear it comfortably, we’ll feel better.
4. Regression to the Mean
In statistics, the law of large numbers is what gets us into trouble when we extrapolate from small sample sizes. The more results you can draw from, the more confidence you can have that your numbers reflect the average.
Regression to the mean builds on that. It states that when large deviations from the norm happen, you should expect a regression to that norm to follow.
It’s why incredible sports performances often precede less impressive performances, and hot-streaks in the casino inevitably come to an end. But we often have trouble disentangling luck from cause-and-effect.
For instance, Daniel Kahneman noticed an odd feature of the feedback given to people training in the Israeli Air Force—those who were praised subsequently performed worse, while those who were scolded showed an improvement.
Naturally, the officers grew in favour of negative feedback, but they failed to see the role of chance and randomness: both the best and worst performances were outliers and were likely to move back towards the norm with or without the feedback.
“The instructor had attached a causal interpretation to the inevitable fluctuations of a random process.” —Daniel Kahneman
Ideally, we’ll have large sample sizes and control groups to help identify what’s real and what’s random. But that’s not always possible. Be careful drawing too many conclusions from a few extreme data points.
5. Occam’s Razor
Attributed to William of Ockham, the principle suggests that when faced with competing explanations, it’s the simplest one that’s usually correct.
Used appropriately, Occam’s Razor has us keeping watch for details and complexities that aren’t strictly necessary, and searching for the minimal number of claims and devices needed to explain something.
“Other things being equal, we may assume the superiority of the demonstration which derives from fewer postulates or hypotheses.” —Aristotle
Complex and detail-rich theories are more difficult to both understand and assess. Good theories are simple enough that they’re easier to test, the best theories are those that withstand said tests.
It’s easy to make the simple complicated, but not to make the complicated simple. This doesn’t just apply to science and math but to how we think and structure our lives. Focus on the essentials and reduce the clutter.
Know When to Use Them
Remember, these are tools meant to help you be more efficient with your mental energies. They are not meant to be perfectly accurate in all situations, they don’t remove doubt or the chance of failure.
What they do is give us a new method for investigating problems and making efficient decisions. Rather than perfection, they help us avoid the pervasive biases and fallacies present in our old models. Used correctly, they are ‘good enough.’