Two objects are independent if they do not affect each other. Independence is wellunderstood in classical information theory, but less in algorithmic information theory. Working in the framework of algorithmic information theory, the paper proposes two types of independence for arbitrary infinite binary sequences and studies their properties. Our two proposed notions of independence have some of the intuitive properties that one naturally expects. For example, for every sequence x, the set of sequences that are independent (in the weaker of the two senses) with x has measure one. For both notions of independence we investigate to what extent pairs of independent sequences, can be effectively constructed via Turing reductions (from one or more input sequences). In this respect, we prove several impossibility results. For example, it is shown that there is no effective way of producing from an arbitrary sequence with positive constructive Hausdorff dimension two sequences that are independent (even in the weaker type of independence) and have super-logarithmic complexity. Finally, a few conjectures and open questions are discussed.

Let d be a positive integer. Let G be the additive monoid N d or the additive group Z d. Let A be a finite set of symbols. The shift action of G on A G is given by S g (x)(h) = x(g + h) for all g,h ∈ G and all x ∈ A G. A G-subshift is defined to be a nonempty closed set X ⊆ A G such that S g (x) ∈ X for all g ∈ G and all x ∈ X. Given a G-subshift X, the topological entropy ent(X) is defined as usual [31]. The standard metric on A G is defined by ρ(x,y) = 2 −|Fn | where n is as large as possible such that x↾Fn = y↾Fn. Here Fn = {0,1,...,n} d if G = N d, and Fn = {−n,...,−1,0,1,...,n} d if G = Z d. For any X ⊆ A G the Hausdorff dimension dim(X) and the effective Hausdorff dimension effdim(X) are defined as usual [14, 26, 27] with respect to the standard metric. It is well known that effdim(X) = sup x∈X liminfnK(x↾Fn)/|Fn | where K denotes Kolmogorov complexity [9]. If X is a G-subshift, we prove that ent(X) = dim(X) = effdim(X), and ent(X) ≥ limsup n K(x↾Fn)/|Fn | for all x ∈ X, and ent(X) = limnK(x↾Fn)/|Fn | for some x ∈ X.

Let f be a computable function from finite sequences of 0’s and 1’s to real numbers. We prove that strong f-randomness implies strong f-randomness relative to a PA-degree. We also prove: if X is strongly f-random and Turing reducible to Y where Y is Martin-Löf random relative to Z, then X is strongly f-random relative to Z. In addition, we prove analogous propagation results for other notions of partial randomness, including non-K-triviality and autocomplexity. We prove that f-randomness relative to a PA-degree implies strong f-randomness, but f-randomness does not imply f-randomness relative to a PA-degree. Keywords: partial randomness, effective Hausdorff dimension, Martin-Löf randomness, Kolmogorov complexity, models of arithmetic.