A Monte Carlo Renormalization Group Approach to the Bak-Sneppen model

TL;DR

This paper introduces a Monte Carlo renormalization group method for the Bak-Sneppen model, enhancing computational efficiency and accuracy in analyzing self-organized criticality.

Contribution

It presents a novel Monte Carlo RG approach with a self-consistency condition, significantly improving speed and reliability over previous methods.

Findings

Faster RG technique for Bak-Sneppen model

Validated and refined previous results

Demonstrated effectiveness of importance sampling in this context

Abstract

A recent renormalization group approach to a modified Bak-Sneppen model is discussed. We propose a self-consistency condition for the blocking scheme to be essential for a successful RG-method applied to self-organized criticality. A new method realizing the RG-approach to the Bak-Sneppen model is presented. It is based on the Monte-Carlo importance sampling idea. The new technique performs much faster than the original proposal. Using this technique we cross-check and improve previous results.