Coarse computability, the density metric, Hausdorff distances between Turing degrees, perfect trees, and reverse mathematics
Denis R. Hirschfeldt, Carl G. Jockusch, Jr., and Paul E. Schupp

TL;DR
This paper explores the structure of coarse similarity classes and Turing degrees using a new metric based on density and Hausdorff distance, revealing their topological, computability, and reverse mathematical properties.
Contribution
It introduces a novel metric space for coarse similarity classes and Turing degrees, analyzing their geometric, measure-theoretic, and logical characteristics.
Findings
Between any two points, there are continuum many geodesic paths.
Distances between Turing degrees are exactly 0, 1/2, or 1.
Distribution of attractive and dispersive degrees is studied.
Abstract
The coarse similarity class of is the set of all whose symmetric difference with has asymptotic density 0. There is a natural metric on the space of coarse similarity classes defined by letting be the upper density of the symmetric difference of and . We study the resulting metric space, showing in particular that between any two distinct points there are continuum many geodesic paths. We also study subspaces of the form where is closed under Turing equivalence, and show that there is a tight connection between topological properties of such a space and computability-theoretic properties of . We then define a distance between Turing degrees based on Hausdorff distance in this metric space. We adapt a proof of Monin to show that the distances between degrees that…
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Taxonomy
TopicsAdvanced Topology and Set Theory · Computability, Logic, AI Algorithms · Topological and Geometric Data Analysis
