Logistic modelling of economic dynamics

TL;DR

This paper shows that logistic functions effectively model the evolution of economic systems, specifically GDP, trade, revenue, and human resources, revealing power-law dynamics in their coupled variables.

Contribution

It introduces a logistic modelling approach to describe economic dynamics and demonstrates its effectiveness on real-world data from the USA and IBM.

Findings

Logistic functions fit the economic data well.

Coupled variables follow power-law behavior.

Models accurately capture growth patterns.

Abstract

We demonstrate the effectiveness of the logistic function to model the evolution of two economic systems. The first is the GDP and trade growth of the USA, and the second is the revenue and human resource growth of IBM. Our modelling is based on the World Bank data in the case of the USA, and on the company data in the case of IBM. The coupled dynamics of the two relevant variables in both systems - GDP and trade for the USA, and revenue and human resource for IBM - follows a power-law behaviour.