2006
DOI: 10.2178/bsl/1154698741
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Calibrating Randomness

Abstract: We report on some recent work centered on attempts to understand when one set is more random than another. We look at various methods of calibration by initial segment complexity, such as those introduced by Solovay [125], Downey, Hirschfeldt, and Nies [39], Downey, Hirschfeldt, and LaForte [36], and Downey [31]; as well as other methods such as lowness notions of Kučera and Terwijn [71], Terwijn and Zambella [133], Nies [101, 100], and Downey, Griffiths, and Reid [34]; higher level randomness notions going ba… Show more

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Cited by 78 publications
(87 citation statements)
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References 119 publications
(221 reference statements)
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“…This is a notion that has been intensively studied recently (see [DHNT06]). Clearly every computable sequence is H-trivial, but the converse does not hold [Zam90,Sol75].…”
Section: Remarkmentioning
confidence: 99%
“…This is a notion that has been intensively studied recently (see [DHNT06]). Clearly every computable sequence is H-trivial, but the converse does not hold [Zam90,Sol75].…”
Section: Remarkmentioning
confidence: 99%
“…A short survey of it is also given in Ambos-Spies and Kučera [1]. Much deeper insight into the subject of algorithmic randomness can be found in a forthcoming book of Downey and Hirschfeldt [4]; a good survey is also in Downey, Hirschfeldt, Nies and Terwijn [5].…”
Section: Definitions and Notationmentioning
confidence: 99%
“…All of these notions express feebleness of an oracle with respect to some notion of algorithmic randomness: for example, a real A is low for Martin-Löf randomness if every real which is Martin-Löf random remains Martin-Löf random relative to A; in other words, A cannot detect patterns in Martin-Löf random sets. We refer the reader to [7,9,23,24,25] for details of such results.…”
Section: Introductionmentioning
confidence: 99%