We are complex systems
Complex systems are a core issue of our daily work. When you think about complexity, probably your first thought is that it may be something difficult and not at all in relative proximity to what it is your experience on a daily basis. But the truth is that we are both expressions of it as well as a part of it. With its organs, cells and biological processes our body allows us to live, which is an example of a complex system as are social and business systems. And a lot of infrastructures we build, such as the Internet and the highways, too.
All of them are composed of a high number of single and simpler parts, that interact among themselves. The feature that marks the units' conduct is that these interactions and their results aren't totally predictable: consequently, nobody knows exactly how the system will change. For instance, our society is a complex system: marketing experts and political campaigners know how hard it can be to persuade people to buy a product or to vote for a candidate and in spite of the effort put forward by these people the exact opposite could happen instead. In this context, the network science theory is one of the most powerful tools in analyzing these structures: this formalism identifies each part of the system as a node and the relationships as edges, giving a model that allows the user to understand the system and its properties.
But why would it matter to be able to grasp complex systems? We could say it's because complexity goes with potential: indeed, they are a cross-disciplinary key to the reading of a phenomenon and enables us to interpret its behavior better. There is a clear difference between studying a company analyzing only what it produces, how it makes it and how much it spends, and instead relating these processes to the fact that the firm operates in a system made up of banks, clients, suppliers and competitors. The outcome of this approach is surely richer and can highlight previously unexpected results.
To give another example, it is interesting to talk about DebtRank, a study that applied a methodology regarding the field of complex networks, which has given a different insight on the risk of default of the financial system. This application, created amongst others by Stefano Battiston, shows that the FED loans program launched in the USA in 2008-2010 to help manage the crises went mostly to 22 banks alone and that they received the biggest slice of the 1.2 trillion allocation. According to the findings, those institutions were not only “too big to fail”, but mostly were “too central to fail” because they were all apart of the same hub inside the US financial system. The strength of their mutual connections was so high that even a small loss could affect other nodes causing the infamous “systemic default”.
Tykli's holistic approach
We often need to explain a lot of similar issues: thus, our challenge is to make complex systems more accessible, regardless of whether or not we are talking about media or library archives, human resources or the database of an e-commerce. The holistic approach of our technology, based on algorithms of complex network analysis, makes detecting the properties of any system we investigate simple and immediate.