Self-Sampling Assumption - SSA
Updated 2026-08-01
INTRODUCTION
English translation pending.
CORE DEFINITION
An anthropic principle formalized by John Leslie and Nick Bostrom. The Self-Sampling Assumption holds that you should regard yourself as a random sample from all observers in your reference class. If a hypothesis predicts very few observers and you are one of them, that hypothesis loses credibility. The assumption underpins the Doomsday Argument and simulation arguments, and its conclusions depend heavily on how the reference class is defined.
SCAFFOLDING EFFECT
Reduce cognitive load
- Calibrate position: assume you are a typical member of a group rather than an exceptional one - Read markets: treat your own reaction as one draw from the user base, not as the user base - Test predictions: ask what an average observer would see if the hypothesis were true
Anchor fast decisions
If you are a random draw from all observers, your rank among them is uniformly distributed. A hypothesis implying that you are an extremely early observer within an enormous total population makes your observed position improbable. Multiplying the prior by this likelihood shifts probability toward hypotheses under which your position is unremarkable, which is how the argument generates conclusions about human longevity.
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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