Astrobiological Complexity with Probabilistic Cellular Automata

TL;DR

This paper proposes using probabilistic cellular automata to model the astrobiological evolution of the Milky Way, providing a quantitative framework to analyze the likelihood of extraterrestrial life and guide SETI efforts.

Contribution

It introduces a novel probabilistic cellular automata approach for modeling astrobiological history and offers a method for analyzing large parameter spaces efficiently.

Findings

Clustering analysis of astrobiological histories

Identification of boundary conditions for SETI planning

Framework adaptability to new observational data

Abstract

Search for extraterrestrial life and intelligence constitutes one of the major endeavors in science, but has yet been quantitatively modeled only rarely and in a cursory and superficial fashion. We argue that probabilistic cellular automata (PCA) represent the best quantitative framework for modeling astrobiological history of the Milky Way and its Galactic Habitable Zone. The relevant astrobiological parameters are to be modeled as the elements of the input probability matrix for the PCA kernel. With the underlying simplicity of the cellular automata constructs, this approach enables a quick analysis of large and ambiguous input parameters' space. We perform a simple clustering analysis of typical astrobiological histories and discuss the relevant boundary conditions of practical importance for planning and guiding actual empirical astrobiological and SETI projects. In addition to showing how the present framework is adaptable to more complex situations and updated observational databases from current and near-future space missions, we demonstrate how numerical results could offer a cautious rationale for continuation of practical SETI searches.