Reconsidering economic leverage and vulnerability: Trade ties, sanction threats, and the success of economic coercion

2018 ◽  
Vol 37 (4) ◽  
pp. 409-429 ◽  
Author(s):  
Timothy M Peterson
Keyword(s):  
Centrality Measures ◽  
Global Trade ◽  
Network Centrality ◽  
Trade Network ◽  
Economic Coercion ◽  
Trade Partners ◽  
Sanction Threats

I contend that a state’s position in the global trade network affects the initiation and outcome of sanction threats. A state is vulnerable, and thus more likely to acquiesce, when its trade has low value to trade partners that are well connected to the global trade network. Conversely, a state has leverage that could motivate the use of sanction threats when its trade has high value to trade partners that are otherwise not well connected. Capturing leverage/vulnerability with an interaction between two network centrality measures, results indicate that vulnerability is associated with acquiescence to sanctions, while leverage is associated with threat initiation.

China's Geostrategic Approach to Trade in Pursuit of Global Centrality

2021 ◽  
pp. 148-175
Author(s):  
Guy-Maurille Massamba
Keyword(s):  
International Trade ◽  
National Character ◽  
Global Trade ◽  
Trade Expansion ◽  
Trade Network ◽  
Central Node ◽  
Global Power ◽  
Trade Partners ◽  
International Trade Network

The geostrategic approach refers to China's method to rise as global power through worldwide trade expansion and the development of its military and naval capabilities. It creates clusters of countries interlinked as China's trade partners, thus being assets to its global ascent. China's importance in global trade is a function of its partners' behavior embracing its trade mechanism. The edges connecting nodes are multidirectional, implying that countries are as much interested in their China-induced interlinkages as they are in their partnership with China. This results in China's centrality, a quality gained from being dominant in trade partnerships in terms of numbers and significance. This chapter examines the approach, process, and historical, geographic, and behavioral components that China uses in its ascent as central node in the international trade network. It explores how underlying dimensions making China's national character conjointly devise its behavior in global trade.

scholarly journals A network analysis of global cephalopod trade

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Andres Ospina-Alvarez ◽  
Silvia de Juan ◽  
Pablo Pita ◽  
Gillian Barbara Ainsworth ◽  
Fábio L. Matos ◽  
...  
Keyword(s):  
Network Analysis ◽  
Global Market ◽  
Centrality Measures ◽  
Global Trade ◽  
Emergent Properties ◽  
Trade Network ◽  
Environmentally Sustainable ◽  
The Usa ◽  
Weak Points ◽  
First Time

AbstractThe global trade in cephalopods is a multi-billion dollar business involving the fishing and production of more than ten commercially valuable species. It also contributes, in whole or in part, to the subsistence and economic livelihoods of thousands of coastal communities around the world. The importance of cephalopods as a major cultural, social, economic, and ecological resource has been widely recognised, but research efforts to describe the extent and scope of the global cephalopod trade are limited. So far, there are no specific regulatory and monitoring systems in place to analyse the traceability of the global trade in cephalopods at the international level. To understand who are the main global players in cephalopod seafood markets, this paper provides, for the first time, a global overview of the legal trade in cephalopods. Twenty years of records compiled in the UN COMTRADE database were analysed. The database contained 115,108 records for squid and cuttlefish and 71,659 records for octopus, including commodity flows between traders (territories or countries) weighted by monetary value (USD) and volume (kg). A theoretical network analysis was used to identify the emergent properties of this large trade network by analysing centrality measures that revealed key insights into the role of traders. The results illustrate that three countries (China, Spain, and Japan) led the majority of global market movements between 2000 and 2019. Based on volume and value, as well as the number of transactions, 11 groups of traders were identified. The leading cluster consisted of only eight traders, who dominated the cephalopod market in Asia (China, India, South Korea, Thailand, and Vietnam), Europe (the Netherlands, and Spain), and the USA. This paper identifies the countries and territories that acted as major importers or exporters, the best-connected traders, the hubs or accumulators, the modulators, the main flow routes, and the weak points of the global cephalopod trade network over the last 20 years. This knowledge of the network is crucial to move towards an environmentally sustainable, transparent, and food-secure global cephalopod trade.

scholarly journals Predicting Crisis in Global Trade Network: An Enhanced Decision Tree Based Methods

2019 ◽  
Vol 9 (1S3) ◽  
pp. 314-318
Keyword(s):  
Global Economy ◽  
Sampling Technique ◽  
Global Network ◽  
Centrality Measures ◽  
Global Trade ◽  
Trade Network ◽  
Trade Relations ◽  
Accuracy And Precision ◽  
Machine Learning Model ◽  
Efficient Machine

International Trade Relations represent a natural Social Information Network that has been extensively analyzed for various purposes like monitoring the global economy. The aim is to use the Global Trade Network to predict the occurrence of natural disasters or financial crisis based on the fact that the trade relations tax a hit in their patterns. The Global Network compromises of Export-Import Relations between the countries in the form of a Weighted Social Network. Predicting Trade relations help us effectively predict any future crisis and prepare for the same. An analysis of the Global Trade Network would discuss the centrality measures and Degree strengths. Using a list of crises which has occurred in the past and with the help of an efficient Machine Learning Model and Sampling Technique the aim is to improve the accuracy and precision of our prediction and discuss the implications on the network.

Comparison and Application of Metrics That Define the Components Modularity in Complex Products

2008 ◽  
Author(s):  
Qi D. Van Eikema Hommes
Keyword(s):  
Social Network ◽  
Centrality Measures ◽  
Software Systems ◽  
Network Centrality ◽  
Good Pair ◽  
Centrality Metrics ◽  
The Social ◽  
Social Network Centrality ◽  
Sample Test

As the content and variety of technology increases in automobiles, the complexity of the system increases as well. Decomposing systems into modules is one of the ways to manage and reduce system complexity. This paper surveys and compares a number of state-of-art components modularity metrics, using 8 sample test systems. The metrics include Whitney Index (WI), Change Cost (CC), Singular value Modularity Index (SMI), Visibility-Dependency (VD) plot, and social network centrality measures (degree, distance, bridging). The investigation reveals that WI and CC form a good pair of metrics that can be used to assess component modularity of a system. The social network centrality metrics are useful in identifying areas of architecture improvements for a system. These metrics were further applied to two actual vehicle embedded software systems. The first system is going through an architecture transformation. The metrics from the old system revealed the need for the improvements. The second system was recently architected, and the metrics values showed the quality of the architecture as well as areas for further improvements.

scholarly journals Identifying key papers within a journal via network centrality measures

2016 ◽  
Vol 107 (3) ◽  
pp. 1005-1020 ◽  
Author(s):  
Saikou Y. Diallo ◽  
Christopher J. Lynch ◽  
Ross Gore ◽  
Jose J. Padilla
Keyword(s):  
Centrality Measures ◽  
Network Centrality

scholarly journals Machine Learning in Network Centrality Measures

2019 ◽  
Vol 51 (5) ◽  
pp. 1-32 ◽  
Author(s):  
Felipe Grando ◽  
Lisandro Z. Granville ◽  
Luis C. Lamb
Keyword(s):  
Machine Learning ◽  
Centrality Measures ◽  
Network Centrality

scholarly journals Predicting dengue importation into Europe, using machine learning and model-agnostic methods

2019 ◽  
Author(s):  
Donald Salami ◽  
Carla Alexandra Sousa ◽  
Maria do Rosário Oliveira Martins ◽  
César Capinha
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Transport Network ◽  
Air Transport ◽  
Health Concern ◽  
Centrality Measures ◽  
Network Centrality

ABSTRACTThe geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation.Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale.Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions.We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.

scholarly journals Growth dynamics and complexity of national economies in the global trade network

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Gianluca Teza ◽  
Michele Caraglio ◽  
Attilio L. Stella
Keyword(s):  
Growth Dynamics ◽  
Global Trade ◽  
Trade Network ◽  
National Economies
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