Ruby | v1.9.0Sep 22nd 2020 | |||

Paul Crowley | v1.8.0Sep 6th 2014 | (+50/-50) Fix references to SIA, SSA | ||

AlexMennen | v1.7.0Aug 8th 2013 | |||

Glacian | v1.6.0Jul 6th 2013 | (+7/-8) | ||

Kaj_Sotala | v1.5.0Oct 22nd 2012 | (+343/-96) | ||

Kaj_Sotala | v1.4.0Jun 28th 2012 | (+19/-20) | ||

Kaj_Sotala | v1.3.0Jun 28th 2012 | (+85/-69) | ||

steven0461 | v1.2.0Jun 26th 2012 | (+461/-17) | ||

steven0461 | v1.1.0Jun 26th 2012 | (+6/-6) /* See also */ | ||

steven0461 | v1.0.0Jun 26th 2012 | (+1476) only moderately confident that this is all correct |

- The
~~Self-Sampling Assumption~~self-sampling assumption, which performs a Bayesian update on the fact that you were randomly chosen out of the set of all observers. - The
~~Self-Indication Assumption~~self-indication assumption, which favors theories in proportion to the number of observers they predict, because with more observers, you're more likely to exist at all. (Sometimes, people take this to include the Self-Sampling Assumption, using "SIA" as shorthand for "SSA+SIA".) - Full Non-indexical Conditioning, which performs a Bayesian update on the fact that there exists someone with your exact experiences.

For example, if intelligence hadn't evolved, we wouldn't exist, and couldn't evaluate the probability of intelligence evolving. In a big enough universe, intelligence could evolve somewhere even if the probability of it happening was arbitrarily low. Therefore we cannot just infer that because intelligence ~~involved~~evolved here, evolution is common, or that designing intelligence is easy.

~~A~~In statistics, a selection effect exists when some property of a thing is correlated with its being sampled. The classic example is a phone poll, which necessarily only reaches people who have phones. If only wealthy people have phones, then being wealthy is correlated with ever being polled in the ~~views~~first place. Thus the opinions of wealthy people will be overrepresented.

For example, if intelligence hadn't evolved, we wouldn't ~~exist. So it's~~exist, and couldn't evaluate the probability of intelligence evolving. In a big enough universe, intelligence could evolve somewhere even if the probability of it happening was arbitrarily low. Therefore we ~~not obvious~~cannot just infer that ~~we can start from the observation that~~because intelligence ~~evolved~~involved here,~~ and infer that such~~ evolution is common, or that designing intelligence is easy.

A selection effect exists when some property of a thing is correlated with its being sampled. The classic example is a phone poll, which necessarily only reaches people who have phones. If only wealthy people have phones, the views of wealthy people will ~~get too much weight.~~be overrepresented.

A selection effect exists when some property of a thing is correlated with its being sampled. The classic example is a phone ~~poll sampling~~poll, which necessarily only ~~those~~reaches people who have phones. ~~Where~~If only wealthy people have phones, ~~such a poll would overestimate average wealth.~~the views of wealthy people will get too much weight.

A selection effect exists when some property of a thing is correlated with its being sampled. The classic example is a phone poll sampling only those people who have phones. Where only wealthy people have phones, such a poll would overestimate average wealth.

An **observation selection effect** exists when some property of a thing is correlated with the observer existing in the first place. The study of such effects is sometimes called "anthropic reasoning" or "anthropics", after the anthropic principle. For example, if intelligence hadn't evolved, we wouldn't exist. So it's not obvious that we can start from the observation that intelligence evolved here, and infer that such evolution is common, or that designing intelligence is easy.

Recent approaches to such effects have focused less on "anthropic principles" and more on ~~possible~~candidate assumptions such as:

- The Self-Sampling Assumption, which performs a Bayesian update on the fact that you were randomly chosen out of the set of all observers.
- The Self-Indication Assumption, which favors theories in proportion to the number of observers they predict, because with more observers, you're more likely to exist at all. (Sometimes, people take this to include the Self-Sampling Assumption, using "SIA" as shorthand for "SSA+SIA".)
- Full Non-
~~Indexical~~indexical Conditioning, which performs a Bayesian update on the fact that there exists someone with your exact experiences.

Such assumptions are needed to determine how we choose between theories predicting different sets of observers.

One approach to anthropic reasoning that has sometimes been attempted is to derive the right principles from decision theory.

A selection effect exists when some property of a thing is correlated with its being sampled. The classic example is a phone poll sampling only those people who have phones.

An **observation selection effect** exists when some property of a thing is correlated with the observer existing in the first place. The study of such effects is sometimes called "anthropic reasoning" or "anthropics", after the anthropic principle.

Recent approaches to such effects have focused less on "anthropic principles" and more on possible assumptions such as:

- The Self-Sampling Assumption, which performs a Bayesian update on the fact that you were randomly chosen out of the set of all observers.
- The Self-Indication Assumption, which favors theories in proportion to the number of observers they predict, because with more observers, you're more likely to exist at all. (Sometimes, people take this to include the Self-Sampling Assumption, using "SIA" as shorthand for "SSA+SIA".)
- Full Non-Indexical Conditioning, which performs a Bayesian update on the fact that there exists someone with your exact experiences.

One approach to anthropic reasoning that has sometimesbeenattempted is to derive principles from decision theory.

- Nick Bostrom's book
*Anthropic Bias* - A primer on the anthropic principle
- Anthropic reasoning in the great filter