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Self-Sampling Assumption — LessWrong

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The self-sampling assumption (SSA), one of the two major schools of anthropic probability1 (the other being the self-indication assumption (SIA)), states that: SSA: All other things equal, an observer should reason as if they are randomly selected from the set of all actually existent observers (past, present and future) in their reference class. For instance, if there is a coin flip that on heads will create one observer, while on tails they will create two, then we have two possible worlds, the first with one observer, the second with two. These worlds are equi-probable, hence the SSA probability of being the first (and only) observer in the heads world is 1/2, that of being the first observer in the tails world is 1/2 x 1/2 = 1/4, and the probability of being the second observer in the tails world is also 1/4. This is why SSA gives an answer of 1/2 probability of heads in the Sleeping Beauty problem. Notice that unlike SIA, SSA is dependent on the choice of reference class. If the a

x Self-Sampling Assumption — LessWrong Self-Sampling Assumption Edited by Jake Miller , et al. last updated 19th Sep 2020 The self-sampling assumption (SSA), one of the two major schools of anthropic probability 1 (the other being the self-indication assumption (SIA)), states that: SSA : All other things equal, an observer should reason as if they are randomly selected from the set of all actually existent observers (past, present and future) in their reference class. For instance, if there is a coin flip that on heads will create one observer, while on tails they will create two, then we have

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