Chain stability in trading networks

Hatfield, John William; Kominers, Scott Duke; Nichifor, Alexandru; Ostrovsky, Michael; Westkamp, Alexander

doi:10.3982/te3839

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…The use of complex networks in the trading system academic research many (Amador and Cabral, 2017;Garcia-Perez et al, 2016;Ge, et al, 2016;Hatfield, et al, 2018) for the study of international trade networks. Starting from the unweighted network, ignoring the impact of trade direction, and the amount on the relationship between the two, only consider the existence of import and export relationships between trading countries or regions from the perspective of the number of relationships to analyze the topological nature of the global trade network, and the position of each economy in the trade network (WANG, et al, 2009).…”

Section: Research On Trade Networkmentioning

confidence: 99%

The carbon effects of the evolution of node status in the world trade network

Tang

Kong²,

Wang

et al. 2022

Front. Environ. Sci.

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Trade has contributed to economic development and has brought countries or regions of the world closer together, but it has also had a significant impact on the global environment, particularly in terms of carbon dioxide emissions. Does an increase in a country’s trade necessarily contribute to an increase in its carbon emissions? This paper examines the impact of changes in the position of major countries in the world trade network on their carbon emission levels. In terms of research methodology, this paper achieves an innovation by adopting a complex network approach to analyze the structural characteristics of the trade relationship networks of major countries or regions in the world, providing a new perspective for the study of the relationship between trade development and carbon emissions. The results of the study show that: trade relations among countries are relatively stable from 2000 to 2020, trade ties among members of regional integration organizations are increasing, the top ten countries in terms of importance are mainly developed countries, and China has very close trade relations with most countries. Based on the analysis of structural characteristics, the impact of changes in the network status of each country on its ocarbon emissions is analyzed, using indicators such as the degree centrality of each node as the independent variable and its domestic carbon emission level as the dependent variable. It is found that developed countries have a significant positive impact on in-going degree centrality, and insignificant impact on out-going degree centrality and betweenness centrality. In contrast, developing countries have a significant positive impact on out-going degree centrality and a negative impact on carbon emissions by betweenness centrality, and this conclusion tells us that not all export growth will contribute to higher levels of carbon emissions in the country. Eigenvector centrality has a negative effect on carbon emissions for both developing and developed countries, and closeness to centrality has no effect on carbon emissions while closeness centrality has no effect on carbon emissions. The results of this study again show that the influence of developed countries is greater than that of developing countries on carbon emissions; therefore, the role of developed countries can be taken into account in subsequent studies on carbon emission reduction.

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Section: Research On Trade Networkmentioning

confidence: 99%

The carbon effects of the evolution of node status in the world trade network

Tang

Kong²,

Wang

et al. 2022

Front. Environ. Sci.

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Complexity of stability in trading networks

et al. 2023

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Efficient computability is an important property of solution concepts. We consider the computational complexity of finding and verifying various solution concepts in trading networks—multi-sided matching markets with bilateral contracts and without transferable utility—under the assumption of full substitutability of agents’ preferences. It is known that outcomes that satisfy trail stability always exist and can be found in linear time. However, we show that the existence of stable outcomes—immune to deviations by arbitrary sets of agents—is an $${{\textsf{N}}}{{\textsf{P}}}$$ N P -hard problem in trading networks. We also show that even verifying whether a given outcome is stable is $${{\textsf{N}}}{{\textsf{P}}}$$ N P -hard in trading networks.

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