How to Do Decision Theory (Extended)
This rather long post is a collection from earlier posts I’ve written on Evidential, Causal and Functional Decision Theory. I have “played around” with different publications, and finally concluded How to Build an ASI has the format I want to continue using, and so I’ve gathered writings from my publication Parfit’s Town to be published here as well.
The first part of this post is about Evidential Decision Theory. We’ll discuss Bayesian reasoning, utility and of course Evidential Decision Theory itself. The second chapter is on Causal Decision Theory, discussing causality and the theory itself. Finally, Chapter III aims to explain Functional Decision Theory.
Chapter I: How to Do Evidential Decision Theory
1.1: An introduction to Bayesian reasoning
Imagine we have a big box with 400 items inside of it. Of those 400, 100 are cubes, while 300 are balls. Furthermore, 50 of the cubes are red, while 100 of the balls are:
Let’s take an item out of our box at random. What is the probability it is a cube?
