Nick Bostrom

Anthropic Bias (eBook, PDF)

Observation Selection Effects in Science and Philosophy

• Format: PDF

Produktbeschreibung

Anthropic Bias explores how to reason when you suspect that your evidence is biased by "observation selection effects"--that is, evidence that has been filtered by the precondition that there be some suitably positioned observer to "have" the evidence. This conundrum--sometimes alluded to as "the anthropic principle," "self-locating belief," or "indexical information"--turns out to be a surprisingly perplexing and intellectually stimulating challenge, one abounding with important implications for many areas in science and philosophy.

There are the philosophical thought experiments and paradoxes: the Doomsday Argument; Sleeping Beauty; the Presumptuous Philosopher; Adam & Eve; the Absent-Minded Driver; the Shooting Room.

And there are the applications in contemporary science: cosmology ("How many universes are there?", "Why does the universe appear fine-tuned for life?"); evolutionary theory ("How improbable was the evolution of intelligent life on our planet?"); the problem of time's arrow ("Can it be given a thermodynamic explanation?"); quantum physics ("How can the many-worlds theory be tested?"); game-theory problems with imperfect recall ("How to model them?"); even traffic analysis ("Why is the 'next lane' faster?").

Anthropic Bias argues that the same principles are at work across all these domains. And it offers a synthesis: a mathematically explicit theory of observation selection effects that attempts to meet scientific needs while steering clear of philosophical paradox.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 240
• Erscheinungstermin: 11. Oktober 2013
• Englisch
• ISBN-13: 9781136711008
• Artikelnr.: 39703870

Autorenporträt

Nick Bostrom

Inhaltsangabe

1. Introduction
2. Fine-Tuning in Cosmology
3. Anthropic Principles
4. Thought Experiments Supporting the Self-Sampling Assumption
5. The Self-Sampling Assumption in Science
6. The Doomsday Argument
7. ::
8. ::
9. ::
ch0010 Observation Selection Theory
11. Observation Selection Theory Applied