Optimizing Economic Markets through Monte Carlo Simulations and Magnetism-Inspired Modeling

TL;DR

This paper models economic agents using physics-inspired spin states and employs Monte Carlo simulations to optimize market surplus, highlighting the role of temperature and magnetic fields in economic dynamics.

Contribution

It introduces a novel physics-based framework for economic modeling using Ising-like spin states and Monte Carlo methods to analyze market optimization.

Findings

Monte Carlo simulations effectively optimize market surplus.

Temperature regulation influences economic activity levels.

Magnetic field analogies help understand policy impacts.

Abstract

This study presents a novel approach to modelling economic agents as analogous to spin states in physics, particularly the Ising model. By associating economic activity with spin orientations (up for inactivity, down for activity), the study delves into optimizing market dynamics using concepts from statistical mechanics. Utilizing Monte Carlo simulations, the aim is to maximize surplus by allowing the market to evolve freely toward equilibrium. The introduction of temperature represents the frequency of economic activities, which is crucial for optimizing consumer and producer surplus. The government's role as a temperature regulator (raising temperature to stimulate economic activity) is explored. Results from simulations and policy interventions, such as introducing a "magnetic field," are discussed, showcasing complexities in optimizing economic systems while avoiding undue control that may destabilize markets. The study provides insights into bridging concepts from physics and economics, paving the way for a deeper understanding of economic dynamics and policy interventions.