When the crisis came, the serious limitations of existing economic and financial  models immediately became apparent. There is also a strong belief, which I  share, that bad, or oversimplistic and overconfident economics help create the  crisis. Now, you've probably all heard of similar criticism coming from people  who are skeptical of capitalism, but this is different. This is coming from the  heart of finance. The first quote is from Jean Claude Trichet when he was  governor of the European Central Bank. The second quote is from the head of  the UK Financial Services Authority. Are these people implying that we don't  understand the economic systems that drive our modern societies? It gets  worse. We spend billions of dollars trying to understand the origins of the  universe, while we still don't understand the conditions for a stable society, a  functioning economy, or peace. What's happening here? How can this be  possible? Do we really understand more about the fabric of reality than we do  about the fabric which emerges from our human interactions? Unfortunately, the  answer is yes, but there is an intriguing solution which is coming from what is  known as the science of complexity. To explain what this means and what this  thing is, please let me quickly take a couple of steps back. I ended up in physics by accident. It was a random encounter when I was young, and since then I've  often wondered about the amazing success of physics in describing the reality  we wake up in every day. In a nutshell, you can think of physics as follows: so  you take a chunk of reality you want to understand, and you translate it into  mathematics. You encode it into equations. Then predictions can be made and  tested. We're actually really lucky that this works, because no one really knows  why the thoughts in our heads should actually relate to the fundamental  workings of the universe. Despite the success, physics has its limits, as Derek  Helbing pointed out in the last quote. We don't really understand the complexity  that relates to us, that surrounds us. This paradox is what got me interested in  complex systems. So these are systems which are made up of many  interconnected or interacting parts, swarms of birds or fish, ant colonies,  ecosystems, brains, financial markets-these are just a few examples.  Interestingly, complex systems are very hard to map into mathematical  equations. So, the usual physics approach doesn't really work here. So, what do we know about complex systems? Well, it turns out that what looks like complex  behavior from the outside is actually the result of a few simple rules of  interaction. This means you can forget about the equations, and just start to  understand the system by looking at the interactions. So you can actually forget  about the equations, and you just start to look at the interactions. And it gets  even better because most complex systems have this amazing property called  emergence. So this means that the system as a whole suddenly starts to show a behavior which cannot be understood or predicted by looking at the components of the system. So the whole is literally more than the sum of its parts. And all of  this also means that you can forget about the individual parts of the system, how

complex they are. So if it's a cell or a termite or a bird, you just focus on the  rules of interaction. As a result, networks are ideal representations of complex  systems. The nodes in the network are the system's components, and the links  are given by the interactions. So, what equations are for physics? Complex  networks are for the study of complex systems. This approach has been very  successfully applied to many complex systems in physics, biology. Computer  science, the social sciences, but what about economics? Where are economic  networks? This is a surprising and prominent gap in the literature. The study we  published last year, called "The Network of Global Corporate Control, was the  first extensive analysis of economic networks. The study went viral on the  internet, and it attracted a lot of attention from the international media. This is  quite remarkable because, again, why did no one look at this before? Similar  data has been around for quite some time. What we looked at in detail was  ownership networks, so here the nodes are companies, people, governments,  foundations, etc. and the links represent the shareholding relation. So  shareholder A has X percent of the shares in company B, and we also assign a  value to the company given by the operating revenue. So ownership networks  reveal the patterns of shareholding relations. In this little example, you can see a few financial institutions with some of the many links highlighted. Now you may  think that no one's looked at this before because ownership networks are like  really really boring to study. Well, as ownership is related to control, as I shall  explain later, looking at ownership networks actually can give you answers to  questions like: Who are the key players? How are they organized? Are they  isolated? Are they interconnected, and what is the overall distribution of control? In other words, who controls the world? I think this is an interesting question,  and it has implications for systemic risk. This is a measure of how vulnerable a  system is overall. A high degree of interconnectivity can be bad for stability,  because then distress can spread through the system like an epidemic.  Scientists have sometimes criticized economists who believe ideas and  concepts are more important than empirical data, because a foundational  guideline in science is let the data speak. Okay, let's do that. So we started with  a database containing 13 million ownership relations from 2007. This is a lot of  data, and because we wanted to find out who rules the world, we decided to  focus on transnational corporations, or TNCs for short. These are companies  that operate in more than one country, and we found 43,000. In the next step,  we built the network around these companies. So we took all the TNCs'  shareholders and the shareholders' shareholders, etc. all the way upstream, and we did the same downstream, and ended up with a network containing 600,000  nodes and 1 million links. This is a TNC network which we analyzed, and it turns out to be structured as follows: so you have a periphery and a center, which  contains about 75% of all the players, and in the center there's this tiny but  dominant core, which is made up of highly interconnected companies. To give 

you a better picture, think about a metropolitan area. So you have the suburbs  and the periphery. You have a center like a financial district. Then the core would be something like the tallest high-rise building in the center, and we already see  signs of organization going on here. 36% of the TNCs are in the core only, but  they make up 95% of the total operating revenue of all TNCs. Okay, so now we  analyzed the structure. So, how does this relate to the control? Well, ownership  gives voting rights to shareholders. This is the normal notion of control, and  there are different models which allow you to compute the control you get from  ownership. If you have more than 50% of the shares in a company, you get  control. But usually, it depends on the relative distribution of shares, and the  network really matters. About 10 years ago, Mr. Tronchetti Provera had  ownership and control in a small company, which had ownership and control in a bigger company, you get the idea. This ended up giving him control in Telecom  Italia with a leverage of 26. So this means that with each euro he invested, he  was able to move 26 euros of market value through the chain of. Ownership  relations. Now, what we actually computed in our study was the control over the  TNC's value. This allowed us to assign a degree of influence to each  shareholder. This is very much in the sense of Max Weber's idea of potential  power, which is the probability of imposing one's own will despite the opposition  of others. If you want to compute the flow in an ownership network, this is what  you have to do. It's actually not that hard to understand. Let me explain by giving you this analogy. So think about water flowing in pipes where the pipes have  different thickness. So similarly, the control is flowing in the ownership networks, and is accumulating at the nodes. So what did we find after computing all this  network control? Well, it turns out that the 737 top shareholders have the  potential to collectively control 80% of the TNC's value. Now, remember, we  started out with 600,000 nodes, so these 737 top players make up a bit more  than 0.1% They're mostly financial institutions in the U.S. and the U.K. And it  gets even more extreme. There are 146 top players in the core, and they  together have the potential to collectively control 40% of the TNC's value. What  should you take home from all of this? Well, the high degree of control you saw  is very extreme by any standard. The high degree of interconnectivity of the top  players in the core could pose significant systemic risk to the global economy,  and we could easily reproduce the TNC network with a few simple rules. This  means that its structure is probably the result of self-organization. It's an  emergent property which depends on the rules of interaction in the system, so  it's probably not the result of a top-down approach like a global conspiracy. Our  study is an impression of the moon's surface. It's not a street map, so you  should take the exact numbers in our study with a grain of salt. Yet, it gave us a  tantalizing glimpse of a brave new world Of finance, we hope to have opened  the door for more such research in this direction. So, the remaining unknown  terrain will be charted in future, and this is slowly starting. We're seeing the 

emergence of long-term and highly funded programs, which aim at  understanding our networked world from a complexity point of view, but this  journey has only just begun, so we will have to wait before we see the first  results. Now, there is still a big problem, in my opinion. Ideas relating to finance,  economics, Politics, society, are very often tainted by people's personal  ideologies. I really hope that this complexity perspective allows for some  common ground to be found. It would be really great if it has the power to help  end the gridlock created by conflicting ideas, which appears to be paralyzing our globalized world. Reality is so complex; we need to move away from dogma. But this is just my own personal ideology. Thank you. 



Остання зміна: понеділок 21 вересня 2026 13:48 PM