Computing from projections of random points: a dense hierarchy of subideals of the $K$-trivial degrees
Noam Greenberg, Joseph S. Miller, Andre Nies

TL;DR
This paper introduces a hierarchy of subideals within the $K$-trivial degrees based on computability from parts of random sequences, characterized by cost functions and geometric inequalities, revealing a structured landscape of randomness-based computability.
Contribution
It defines and characterizes a dense hierarchy of subideals of $K$-trivial degrees using projections of random sequences, cost functions, and geometric inequalities, expanding understanding of randomness and computability.
Findings
The collection of sets computable from both halves of a random sequence forms an ideal generated by c.e. sets.
A hierarchy of subideals $B_{k/n}$ is established, properly contained within each other for different rational $k/n$.
The union of $B_p$ for $p<1$ corresponds to sets robustly computable from a random sequence.
Abstract
We study the sets that are computable from both halves of some (Martin-L\"of) random sequence, which we call \emph{-bases}. We show that the collection of such sets forms an ideal in the Turing degrees that is generated by its c.e.\ elements. It is a proper subideal of the -trivial sets. We characterise -bases as the sets computable from both halves of Chaitin's , and as the sets that obey the cost function . Generalising these results yields a dense hierarchy of subideals in the -trivial degrees: For , let be the collection of sets that are below any out of columns of some random sequence. As before, this is an ideal generated by its c.e.\ elements and the random sequence in the definition can always be taken to be . Furthermore, the corresponding cost function characterisation…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · semigroups and automata theory · Mathematical Dynamics and Fractals
