Understanding scaling through history-dependent processes with collapsing sample space

TL;DR

This paper introduces sample-space reducing (SSR) processes that naturally produce Zipf's law and power-law distributions, explaining scaling phenomena in complex systems without relying on traditional multiplicative or self-organized critical models.

Contribution

The authors formalize SSR processes, demonstrate their ability to generate power-law distributions with adjustable exponents, and connect these processes to various phenomena like word frequencies and network diffusion.

Findings

SSR processes lead to Zipf's law in outcome distributions.

Adding noise to SSR processes results in power-law distributions with exponents determined by the noise level.

SSR processes can explain a wide range of scaling exponents in complex systems.

Abstract

History-dependent processes are ubiquitous in natural and social systems. Many such stochastic processes, especially those that are associated with complex systems, become more constrained as they unfold, meaning that their sample-space, or their set of possible outcomes, reduces as they age. We demonstrate that these sample-space reducing (SSR) processes necessarily lead to Zipf's law in the rank distributions of their outcomes. We show that by adding noise to SSR processes the corresponding rank distributions remain exact power-laws, $p(x)\sim x^{-\lambda}$, where the exponent directly corresponds to the mixing ratio of the SSR process and noise. This allows us to give a precise meaning to the scaling exponent in terms of the degree to how much a given process reduces its sample-space as it unfolds. Noisy SSR processes further allow us to explain a wide range of scaling exponents in frequency distributions ranging from $\alpha = 2$ to $\infty$. We discuss several applications showing how SSR processes can be used to understand Zipf's law in word frequencies, and how they are related to diffusion processes in directed networks, or ageing processes such as in fragmentation processes. SSR processes provide a new alternative to understand the origin of scaling in complex systems without the recourse to multiplicative, preferential, or self-organised critical processes.