Multiple Recurrence and Algorithmic Randomness

This work contributes to the programme of studying effective versions of "almost everywhere" theorems in analysis and ergodic theory via algorithmic randomness. We determine the level of randomness needed for a point in a Cantor space $ \{0,1\}^{\NN}$ with the uniform measure and the usual shift so that effective versions of the multiple recurrence theorem of Furstenberg holds for iterations starting at the point. We consider recurrence into closed sets that possess various degrees of effectiveness: clopen, $\PPI$ with computable measure, and $\PPI$. The notions of Kurtz, Schnorr, and \ML\ randomness, respectively, turn out to be sufficient. We obtain similar results for multiple recurrence with respect to the $k$ commuting shift operators on $\{0,1\}^{\NN^{\normalsize k}}$.

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