The 20-60-20 Rule

TL;DR

This paper explores the empirical 20-60-20 rule, demonstrating its mathematical basis and implications for population balance and management, especially in multivariate normal distributions.

Contribution

It provides a mathematical justification for the 20-60-20 rule and shows its relevance in achieving equilibrium in populations modeled by multivariate normal vectors.

Findings

The 20-60-20 ratio leads to a global equilibrium in population models.

The rule is justified mathematically for multivariate normal distributions.

It implies practical benefits for management and control strategies.

Abstract

In this paper we discuss an empirical phenomena known as the 20-60-20 rule. It says that if we split the population into three groups, according to some arbitrary benchmark criterion, then this particular ratio implies some sort of balance. From practical point of view, this feature often leads to efficient management or control. We provide a mathematical illustration, justifying the occurrence of this rule in many real world situations. We show that for any population, which could be described using multivariate normal vector, this fixed ratio leads to a global equilibrium state, when dispersion and linear dependance measurement is considered.