What is the self indication assumption?

The Self Indication Assumption (SIA) is a way of thinking about observational effects and their implications. It is often contrasted with the Self Sampling Assumption (SSA).

Here's a simplified explanation from a discussion between Dwarkesh Patel and Joseph Carlsmith 1:

• Self Indication Assumption (SIA): SIA posits that it's more likely to find yourself in a world where there are more observers like you. For example, if you wake up in a white room and a message states that a coin was flipped, creating one person if heads and a million people if tails, SIA would suggest that the probability of the coin being tails is extremely high (1 in a million). This is because in worlds where tails was the result, there are many more people experiencing your situation.

• Self Sampling Assumption (SSA): On the other hand, SSA asserts that you should consider yourself as a random sample from a 'reference class' of all possible observers. This means that rather than favoring worlds with more observers, SSA leans towards scenarios where you are a typical member of your reference class, leading to different probability estimations.

The SIA generally leads to the conclusion that you are more likely to exist in scenarios with a higher number of observers with your current evidence, while SSA often results in the doomsday argument and other probabilistic outcomes based on the sampling from a predefined class of observers.