Self-Indication Assumption - SIA
Updated 2026-08-01
INTRODUCTION
English translation pending.
CORE DEFINITION
A principle in anthropic reasoning, formalized in debates involving Nick Bostrom during the 2000s. The Self-Indication Assumption states that given that you exist, you should favor hypotheses under which more observers exist, because a randomly selected observer is more likely to be you in a crowded world. It is applied to cosmology and to simulation arguments, and it is frequently contrasted with the Self-Sampling Assumption, which samples observers within a world rather than across worlds.
SCAFFOLDING EFFECT
Reduce cognitive load
- Weight scenarios: when comparing rival hypotheses, multiply each one by the number of observers it creates - Test intuitions: check whether a position that feels special is actually the common, unremarkable case - Read simulations: apply it wherever simulated minds vastly outnumber real ones in the predicted population
Anchor fast decisions
The principle follows from treating your existence as data. If observers are sampled uniformly from all observers that exist, the probability of finding yourself in a given world is proportional to how many observers that world contains. Worlds with more observers therefore receive higher posterior probability, which is why the assumption systematically favors large or simulated populations over sparse ones.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Anthropic_Biasverified
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