Probabilistic Thinking

The next mental model I’m digging into is the idea of Probabilistic Thinking. This is the effort to try to determine the likelihood of any particular outcome ahead of time. With most things in life there are an infinite set of factors to consider, but you can focus on the big ones and get a fairly good idea of what the odds look like for any situation.

According to the Farnam Street blog, there are three good tools you can use to help estimate outcomes.

Bayesian thinking

Given that we have limited but useful information about the world, and are constantly encountering new information, we should probably take into account what we already know when we learn something new. As much of it as possible. Bayesian thinking allows us to use all relevant prior information in making decisions. Statisticians might call it a base rate, taking in outside information about past situations like the one you’re in.

Fat-tailed curves

A “fat-tailed curve” is similar to the normal bell curves that you’ve seen before, but it’s more flattened out, meaning a wider variety of possibilities exist. Farnam explains it like this:

In a bell curve type of situation, like displaying the distribution of height or weight in a human population, there are outliers on the spectrum of possibility, but the outliers have a fairly well defined scope. You’ll never meet a man who is ten times the size of an average man. But in a curve with fat tails, like wealth, the central tendency does not work the same way. You may regularly meet people who are ten, 100, or 10,000 times wealthier than the average person. That is a very different type of world.

Understanding the parameters of what you’re looking at will help guide your thoughts.

Asymmetries

This is a natural tendency that we have, which we should fight to overcome. In many cases, we’ll give too much weight to one side of a situation, generally the over-optimistic side, when it really should be more balanced.

Farnam gives two examples: One is related to projected investor returns, which almost never hit their goals (some of the stocks do, some don’t, and you meet in the middle). The other is how we calculate travel time to meeting:

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? Exactly. Your estimation errors are asymmetric, skewing in a single direction. This is often the case with probabilistic decision-making.

This is a fascinating and complex topic, but becoming good at it can generate huge advantages. I encourage you to read the full post on the Farnam blog for more examples and details.