Hyper-Rationality: Bayesian Thinking
How to consistently improve your beliefs
TL;DR
Consistently using new information to update existing beliefs leads to better predictions.
Whenever we encounter a new fact about the world, this impacts what we think is likely true or false. However, new knowledge should not exclusively govern what we believe, but it ought to update our existing beliefs prior to this new information.
Generally put, our estimation of what is or what will be should adapt to new information in the light of what we have known already before.
A simple way of putting this:
I know that A is very unlikely.
I also know that A is somehow related to B.
Now I observe B.
This should impact how I think about the likelihood of A (how much exactly depends on probabilities of A and B).
In Bayesian Thinking, so-called conditional probabilities determine how we should think about uncertainty and how to make predictions (whether in everyday personal life situations or on a larger scale in a business or political contest).
Simply put, conditional probabilities measure how likely something is, given that something else has already occurred or is true (i.e., how likely it is that A will occur given that it is related to B and B has already occurred).
For example, we might know that the chance of rain on any given day is 10%. We also know that whenever dark clouds appear in the sky, it is pretty likely to rain soon. Hence, if we observe dark clouds, our estimation of whether it will rain should go up from 10%.
Bayes' Theorem is the mathematical underpinning of the concept of Bayesian Thinking and inverts such conditional probabilities to arrive at concrete probability estimates of something happening given that we have observed something related to it already.
Bayesian Thinking is being applied in science to make predictions and forecasts, as well as in business and politics to navigate uncertainty and new information. A general understanding of it can also be very useful for decision-making in our personal lives.
A lack of Bayesian Thinking often leads to reasoning errors and false assumptions about the state of the world and the likelihood of future events.
To Remember
“Under Bayes' theorem, no theory is perfect. Rather, it is a work in progress, always subject to further refinement and testing.”
— Nate Silver
Explore
➞ This Crashcourse video gives a great introduction to everything you need to know.
➞ In this short video, Julia Galef wonderfully explains what Bayesian Thinking means and how it can be applied in everyday life.
➞ This video shows how real-life mysteries have been solved by applying Bayesian Thinking and explains the concept in the process.
➞ This article gives a more visual explanation of Bayes' Theorem (the basis for Bayesian Thinking).
➞ With this video, you will get a deeper insight into the mathematical underpinnings of Bayes' Theorem.
➞ For a slightly more technical explanation of Bayes' Theorem, read through this brief blog post.
➞ If you want to understand the hardcore math behind Bayesian Thinking, check out this Wikipedia article.
Resources
For a structured list of fascinating books, blogs, podcasts, and Youtube channels related to cognition and self-perception, visit mindvault.co/vault/bayesian.