English

Martin-L\"of reducibility and cost functions

Logic 2022-02-11 v3

Abstract

Martin-L\"of (ML)-reducibility compares KK-trivial sets by examining the Martin-L\"of random sequences that compute them. We show that every KK-trivial set is computable from a c.e.\ set of the same ML-degree. We investigate the interplay between ML-reducibility and cost functions, which are used to both measure the number of changes in a computable approximation, and the type of null sets used to capture ML-random sequences. We show that for every cost function there is a c.e.\ set ML-above the sets obeying it (called an ML-complete set for the cost function). We characterise the KK-trivial sets computable from a fragment of the left-c.e.\ random real~Ω\Omega. This leads to a new characterisation of strong jump-traceability.

Keywords

Cite

@article{arxiv.1707.00258,
  title  = {Martin-L\"of reducibility and cost functions},
  author = {Noam Greenberg and Joseph S. Miller and Andre Nies and Daniel Turetsky},
  journal= {arXiv preprint arXiv:1707.00258},
  year   = {2022}
}