JournalsjemsVol. 18, No. 4pp. 773–812

Coherent randomness tests and computing the KK-trivial sets

  • Laurent Bienvenu

    Université Paris Diderot - Paris 7, France
  • Noam Greenberg

    Victoria University of Wellington, New Zealand
  • Antonín Kučera

    Charles University, Prague, Czech Republic
  • André Nies

    University of Auckland, New Zealand
  • Dan Turetsky

    Universität Wien, Austria
Coherent randomness tests and computing the $K$-trivial sets cover
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We introduce Oberwolfach randomness, a notion within Demuth's framework of statistical tests with moving components; here the components' movement has to be coherent across levels. We show that a ML-random set computes all KK-trivial sets if and only if it is not Oberwolfach random, and indeed that there is a KK-trivial set which is not computable from any Oberwolfach random set. We show that Oberwolfach random sets satisfy e ffective versions of almost-everywhere theorems of analysis, such as the Lebesgue density theorem and Doob's martingale convergence theorem. We also show that random sets which are not Oberwolfach random satisfy highness properties (such as LR-hardness) which mean they are close to computing the halting problem.

A consequence of these results is that a ML-random set failing the e ffective version of Lebesgue's density theorem for closed sets must compute all KK-trivial sets. Combined with a recent result by Day and Miller, this gives a positive solution to the ML-covering problem of algorithmic randomness. On the other hand these results settle stronger variants of the covering problem in the negative: no low ML-random set computes all KK-trivial sets, and not every KK-trivial set is computable from both halves of a random set.

Cite this article

Laurent Bienvenu, Noam Greenberg, Antonín Kučera, André Nies, Dan Turetsky, Coherent randomness tests and computing the KK-trivial sets. J. Eur. Math. Soc. 18 (2016), no. 4, pp. 773–812

DOI 10.4171/JEMS/602