Network theory
Arium's Trade Analysis Tool uses network theory to illustrate relationships diagrammatically. Network theory helps us make sense of 'real life' networks such as food chains, the World Wide Web, the electrical power grid; or even social networks.
Each element in the network - a species, website, power station or person - is a 'node' in a network theory diagram. Nodes are connected by lines indicating links or relationships - who eats whom, for example, or who knows whom. Nodes may link to only one or many nodes.
Background
An 18th century mathematician, Leonhard Euler, proved the first theorem of graph, or network, theory. He was grappling with a problem that intrigued the citizens of Konigsberg: to prove whether one could cross the city's seven bridges and return to one's starting point without crossing the same bridge twice.
Euler solved the problem by viewing the bridges as a graph, land masses were represented nodes, the bridges the links. In proving that it was not possible to cross the seven bridges without repeating a crossing, he created a new branch of mathematics called graph theory, the basis for thinking about networks.
Trading networks
The network maps in Arium's Trade Analysis Tool describe 'who trades with whom', where industries, populated with companies active within them, are the nodes. Trade between industries form the links.
This network helps users easily see the relationships between industries and companies of interest, such as industries in a supply chain, merger and acquisition targets, or key companies in a credit or loan portfolio.
This use of network maps provides a new, visual perspective on the relationships between industries and the companies in them. The theory is that connected industries may have connected risks or opportunities.
While this method is a novel way to examine trading connections, it is by no means all that network theory can contribute. Much work is in progress to understand the apparent commonality of structure in many real life networks, and the stability, robustness and efficiency of the underlying systems. These parameters could highlight systemic risk within networks, the kind of interconnected risks such as those that devastated the financial system in 2008 and 2009.
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