Enhancing countries' fitness with recommender systems on the international trade network

Abstract

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries' data of the international trading network. A recommendation system is then used to identify the products corresponding to the production capacity of each individual country, but are somehow overlook by the country. Then, we simulate the evolution of the country's fitness if it would have followed the recommendations. The result of this work is the combination combine these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries.

References in corpus (16)

• The Building Blocks of Economic Complexity
• The Product Space Conditions the Development of Nations
• Link Prediction in Complex Networks: A Survey
• How to project a bipartite network?
• Solving the apparent diversity-accuracy dilemma of recommender systems
• Robust dynamic classes revealed by measuring the response function of a social system
• Topology of the World Trade Web
• A network analysis of countries' export flows: firm grounds for the building blocks of the economy
• The Scientific Competitiveness of Nations
• Network-based recommendation algorithms: A review
• Measuring economic complexity of countries and products: which metric to use?
• On the convergence of the Fitness-Complexity Algorithm
• Complex economies have a lateral escape from the poverty trap
• Prediction in complex systems: the case of the international trade network
• The essential role of time in network-based recommendation
• Economic Complexity: "Buttarla in caciara" vs a constructive approach