Random gluing of metric spaces

TL;DR

This paper constructs random metric spaces by sequentially gluing scaled, independent blocks with probabilistic attachment, and computes the Hausdorff dimension of the set of points created during this process, revealing complex dependencies on parameters.

Contribution

It provides a novel method to analyze the Hausdorff dimension of leaves in randomly glued metric spaces with power-law scaling and weights, extending understanding of fractal dimensions in such constructions.

Findings

Dimension equals  for  large  and  small .

Dimension depends on parameters , , and  in a non-trivial way.

Explicit formula for the Hausdorff dimension in specific parameter regimes.

Abstract

We construct random metric spaces by gluing together an infinite sequence of pointed metric spaces that we call blocks. At each step, we glue the next block to the structure constructed so far by randomly choosing a point on the structure and then identifying it with the distinguished point of the block. The random object that we study is the completion of the structure we obtain after an infinite number of steps. We introduce a sequence $(w_n)_{n\geq1}$ that we call the weights of the blocks. The probability at each step that the next block is glued onto any of the preceding blocks is proportional to its weight. We suppose that the blocks are i.i.d. copies of the same random metric space, scaled by deterministic factors that we call $(\lambda_n)_{n\geq 1}$. We work under some conditions on the distribution of the blocks ensuring that they a.s. have dimension $d$, for some $d>0$. The main contribution of this paper is the computation of the Hausdorff dimension of the set $\mathcal{L}$ of points which appear during the completion procedure, which we call the leaves, when $(\lambda_n)_{n\geq 1}$ and $(w_n)_{n\geq1}$ typically behave like a power of $n$, say $n^{-\alpha}$ for the scaling factors and $n^{-\beta}$ for the weights. For a large domain of $\alpha$ and $\beta$, we have $\mathrm{dim_H}(\mathcal{L})=\alpha^{-1}$. However for $\beta>1$ and $\alpha<1/d$, our results reveal an interesting phenomenon: the dimension has a non-trivial dependence in $\alpha$, $\beta$ and $d$, namely $\mathrm{dim_H}(\mathcal{L})=\alpha^{-1}(2\beta-1-2\sqrt{(\beta-1)(\beta-d\alpha)})$.}