# Optimization of the post-crisis recovery plans in scale-free networks

## TL;DR

This paper investigates optimal stimulation strategies in scale-free economic networks using the Ising model, revealing that targeting high-degree nodes generally reduces costs and that network features like assortativity influence stimulation efficiency.

## Contribution

It introduces a novel application of the Ising model to economic network stimulation, analyzing how network topology affects optimal intervention strategies.

## Key findings

- Stimulating high-degree nodes reduces overall stimulation costs.
- Network assortativity influences the effectiveness of stimulation.
- Application of Ising model provides insights into socio-economic system interventions.

## Abstract

General Motors or a local business, which one is better to be stimulated in post-crisis recessions, where government stimulation is meant to overcome recessions? Due to the budget constraints, it is quite relevant to ask how one can increase the chance of economic recovery. One of the key elements to answer this question is to understand metastable features of the economic networks. Ising model has been suggested for studying such features in the literature. In the homogenous networks one needs at least a minimum activation, forcing an Ising network to switch its local equilibria, where such minimum is independent of the nodes characteristics. In the scale free networks however, when one aims to push the network to switch its vacuum, she faces the question of which nodes are better to be stimulated to minimize the cost. In the paper it has been shown that stimulation of the high degree nodes costs less in general. Despite regular networks, in the scale free networks, the stimulation cost depends on the networks features such as assortativity. Though we have utilized the Ising model to tackle a problem in economics, our analysis shed lights on many other problems concerning stimulations of socio-economic systems.