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In several classes of countable structures it is known that every hyperarithmetic structure has a computable presentation up to bi-embeddability. In this article we investigate the complexity of embeddings between bi-embeddable structures in two such classes, the classes of linear orders and Boolean algebras. We show that if $mathcal L$ is a computable linear order of Hausdorff rank $n$, then for every bi-embeddable copy of it there is an embedding computable in $2n-1$ jumps from the atomic diagrams. We furthermore show that this is the best one can do: Let $mathcal L$ be a computable linear order of Hausdorff rank $ngeq 1$, then $mathbf 0^{(2n-2)}$ does not compute embeddings between it and all its computable bi-embeddable copies. We obtain that for Boolean algebras which are not superatomic, there is no hyperarithmetic degree computing embeddings between all its computable bi-embeddable copies. On the other hand, if a computable Boolean algebra is superatomic, then there is a least computable ordinal $alpha$ such that $mathbf 0^{(alpha)}$ computes embeddings between all its computable bi-embeddable copies. The main technique used in this proof is a new variation of Ash and Knights pairs of structures theorem.
We study the algorithmic complexity of embeddings between bi-embeddable equivalence structures. We define the notions of computable bi-embeddable categoricity, (relative) $Delta^0_alpha$ bi-embeddable categoricity, and degrees of bi-embeddable catego
We investigate the complexity of embeddings between bi-embeddable structures. In analogy with categoricity spectra, we define the bi-embeddable categoricity spectrum of a structure $mathcal A$ as the family of Turing degrees that compute embeddings b
In previous work, we have combined computable structure theory and algorithmic learning theory to study which families of algebraic structures are learnable in the limit (up to isomorphism). In this paper, we measure the computational power that is n
We combine computable structure theory and algorithmic learning theory to study learning of families of algebraic structures. Our main result is a model-theoretic characterization of the class $mathbf{InfEx}_{cong}$, consisting of the structures whos
A computable structure $mathcal{A}$ has degree of categoricity $mathbf{d}$ if $mathbf{d}$ is exactly the degree of difficulty of computing isomorphisms between isomorphic computable copies of $mathcal{A}$. Fokina, Kalimullin, and Miller showed that e