Related Experiment Video
Updated: Aug 9, 2025

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese
Published on: April 1, 2016
Dollar-Yuan Battle in the World Trade Network.
Célestin Coquidé1, José Lages1, Dima L Shepelyansky2
1Équipe Physique Théorique et Astrophysique, Institut UTINAM, Université Bourgogne Franche-Comté, CNRS, 25000 Besançon, France.
The US dollar
Area of Science:
- Economics
- International Trade
- Network Science
Background:
- The US dollar has dominated global trade since the Bretton Woods agreement.
- The Chinese yuan is emerging as a significant trade currency due to China's economic growth.
Purpose of the Study:
- To mathematically analyze international trade flow structures and their influence on currency preference.
- To model how global trade network dynamics favor either the US dollar or Chinese yuan.
Main Methods:
- Utilized the Ising model to represent a country's trade currency preference as a binary variable.
- Constructed a world trade network using UN Comtrade data from 2010-2020.
- Calculated currency preference based on trade volume with partners and partners' global trade weight.
Main Results:
- Analysis revealed a transition in currency preference from 2010 to the present.
- The structure of the global trade network indicates a majority preference for the Chinese yuan.
Conclusions:
- The global trade network structure suggests a shift towards the Chinese yuan.
- Network dynamics, not just economic size, are crucial in determining international trade currency.
More Related Videos
08:15Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model
Published on: June 6, 2025
11:06Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
Published on: April 7, 2023
Related Concept Videos
Dynamic Equilibrium
Equivalent Resistance
Central Tendency: Analysis
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...