Who Controls the World?

murmuration

One fine afternoon autumn day in Cincinnati I watched transfixed as a gigantic flock of migratory birds swarmed over the woods across the street. I didn’t know it then, but I was watching a “complex, self-organizing system” in action. Schools of fish, ant colonies, human brains… and even the financial industry… all exhibit this behavior. And so does “the economy.”

who controls the world TED talk

James B. Glattfelder holds a Ph.D. in complex systems from the Swiss Federal Institute of Technology. He began as a physicist, became a researcher at a Swiss hedge fund. and now does quantitative research at Olsen Ltd in Zurich, a foreign exchange investment manager. He begins his TED Talk with two quotes about the Great Recession of 2007-2008:

“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 over simplistic and overconfident economics helped create the crisis.”

Then he tells us where they came from:

“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?

That’s a rhetorical question, of course:  yes they are, and no we don’t. As a result, nobody saw the Great Recession coming, with its layoffs carnage and near-collapse of the global economy, or its “too big to fail” bailouts and generous bonuses paid to its key players.

Glattfelder tackles what that was about, from a complex systems perspective. First, he dismisses two approaches we’ve already seen discredited:

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.  Ideas relating to finance, economics, politics, society, are very often tainted by people’s personal ideologies.  Reality is so complex, we need to move away from dogma.”

Mathematics:  “You can think of physics as follows. 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. But despite the success, physics has its limits. Complex systems are very hard to map into mathematical equations, so the usual physics approach doesn’t really work here”

Then he lays out a couple key features of complex, self-organizing systems:

“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.

“And it gets even better, because most complex systems have this amazing property called emergence. 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. The whole is literally more than the sum of its parts.”

Applying this to the financial industry, he describes how his firm studied the Great Recession by analyzing a database of controlling shareholder interests in 43,000 transnational corporations (TNC’s). That analysis netted over 600,000 “nodes” of ownership, and over a million connections among them. Then came the revelation:

“It turns out that the 737 top shareholders have the potential to collectively control 80 percent of the TNCs’ value. Now remember, we started out with 600,000 nodes, so these 737 top players make up a bit more than 0.1 percent. They’re mostly financial institutions in the US and the UK. And it gets even more extreme. There are 146 top players in the core, and they together have the potential to collectively control 40 percent of the TNCs’ value.”

737 or 146 shareholders — “mostly financial institutions in the U.S. and the U.K.” — had the power to control 80% or 40% of the value of 43,000 multinational corporations. And those few hundreds — for their own accounts and through the entities they controlled — bought securitized sub-prime mortgages until the market imploded and nearly brought down the global economy valued in the tens of trillions dollars — giving a whole new meaning to the concept of financial leverage. In what might be the economic understatement of the 21st Century, Glattfelder concludes:

“This high level of concentrated ownership means these elite owners possess an enormous amount of leverage over financial risk worldwide. 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 a significant systemic risk to the global economy.”

It took a lot of brute number-crunching computer power and some slick machine intelligence to generate all of that, but in the end there’s an innate simplicity to it all. He concludes:

[The TNC network of ownership is] “an emergent property which depends on the rules of interaction in the system. We could easily reproduce [it] with a few simple rules.”

The same is true of the mesmerizing flock of birds I watched that day:  here’s a YouTube explanation of the three simple rules that explain it[i].

[i] What I saw was a “murmuration” of birds, which is explained by a form of complex system analysis  known as “swarm behavior.”

Reframing “The Economy”

We’ve seen that conventional thinking about “the economy” struggles to accommodate technologies such as machine learning, robotics, and artificial intelligence–which means it’s ripe for a big dose of reframing. Reframing is a problem-solving strategy that flips our usual ways of thinking so that blind spots are revealed, conundrums resolved, polarities synthesized, and barriers transformed into logistics.

The Santa Fe Institute is on the reframing case:  Rolling Stone called the Institute “a sort of Justice League of renegade geeks, where teams of scientists from disparate fields study the Big Questions.” W. Brian Arthur is one of those geeks. He’s also onboard with PARC — a Xerox company in “the business of breakthroughs” — and has written two seminal books on complexity economics:  Complexity and the Economy (2014) and The Nature of Technology: What it Is and How it Evolves (2009). Here’s his pitch for reframing “the economy”:

The standard way to define the economy — whether in dictionaries or economics textbooks — is as a “system of production and distribution and consumption” of goods and services. And we picture this system, “the economy,” as something that exists in itself, as a backdrop to the events and adjustments that occur within it. Seen this way, the economy becomes something like a gigantic container…, a huge machine with many modules or parts.

I want to look at the economy in a different way. The shift in thinking I am putting forward here is ,,, like seeing the mind not as a container for its concepts and habitual thought processes but as something that emerges from these. Or seeing an ecology not as containing a collection of biological species, but as forming from its collection of species. So it is with the economy.

The economy is a set of activities and behaviors and flows of goods and services mediated by — draped over — its technologies:  the of arrangements and activities by which a society satisfies its needs. They include hospitals and surgical procedures. And markets and pricing systems. And trading arrangements, distribution systems, organizations, and businesses. And financial systems, banks, regulatory systems, and legal systems. All these are arrangements by which we fulfill our needs, all are means to fulfill human purposes.

fractal 3

George Zarkadakis is another Big Questions geek. He’s an artificial intelligence Ph.D. and engineer, and the author of In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence  (2016). He describes his complexity economics reframe in a recent article “The Economy Is More A Messy, Fractal Living Thing Than A Machine”:

Mainstream economics is built on the premise that the economy is a machine-like system operating at equilibrium. According to this idea, individual actors – such as companies, government departments and consumers – behave in a rational way. The system might experience shocks, but the result of all these minute decisions is that the economy eventually works its way back to a stable state.

Unfortunately, this naive approach prevents us from coming to terms with the profound consequences of machine learning, robotics and artificial intelligence.

Both political camps accept a version of the elegant premise of economic equilibrium, which inclines them to a deterministic, linear way of thinking. But why not look at the economy in terms of the messy complexity of natural systems, such as the fractal growth of living organisms or the frantic jive of atoms?

These frameworks are bigger than the sum of their parts, in that you can’t predict the behaviour of the whole by studying the step-by-step movement of each individual bit. The underlying rules might be simple, but what emerges is inherently dynamic, chaotic and somehow self-organising.

Complexity economics takes its cue from these systems, and creates computational models of artificial worlds in which the actors display a more symbiotic and changeable relationship to their environments. Seen in this light, the economy becomes a pattern of continuous motion, emerging from numerous interactions. The shape of the pattern influences the behaviour of the agents within it, which in turn influences the shape of the pattern, and so on.

There’s a stark contrast between the classical notion of equilibrium and the complex-systems perspective. The former assumes rational agents with near-perfect knowledge, while the latter recognises that agents are limited in various ways, and that their behaviour is contingent on the outcomes of their previous actions. Most significantly, complexity economics recognises that the system itself constantly changes and evolves – including when new technologies upend the rules of the game.

That’s all pretty heady stuff, but what we’d really like to know is what complexity economics can tell us that conventional economics can’t. We’ll look at that next time.

What is “The Economy” Anyway?

Throughout this series, we’ve heard from numerous commentators who believe that conventional economic thinking isn’t keeping pace with the technological revolution, and that polarized ideological posturing is preventing the kind of open-minded discourse we need to reframe our thinking.

In this short TED talk, the author[1] of Americana:  A Four Hundred Year History of American Capitalism suggests that we unplug the ideological debate and instead adopt a less combative and more digital-friendly metaphor for how we talk about the economy:

“Capitalism… is this either celebrated term or condemned term. It’s either revered or it’s reviled. And I’m here to argue that this is because capitalism, in the modern iteration, is largely misunderstood.

“In my view, capitalism should not be thought of as an ideology, but instead should be thought of as an operating system.

“When you think about it as an operating system, it devolves the language of ideology away from what traditional defenders of capitalism think.”

The operating system metaphor shifts policy agendas away from ideology and instead invites us to consider the economy as something that needs to be continually updated:

“As you have advances in hardware, you have advances in software. And the operating system needs to keep up. It needs to be patched, it needs to be updated, new releases have to happen. And all of these things have to happen symbiotically. The operating system needs to keep getting more and more advanced to keep up with innovation.”

brain tilt

But what if the operating system has gotten too complex for the human mind to comprehend?  This recent article from the Silicon Flatirons Center at the University of Colorado[2] observes that “Human ingenuity has created a world that the mind cannot master,” then asks, “Have we finally reached our limits?” The question telegraphs its answer:  in many respects, yes we have. Consider, for example, the air Traffic Alert and Collision Avoidance System (TCAS) that’s responsible for keeping us safe when we fly:

“TCAS alerts pilots to potential hazards, and tells them how to respond by using a series of complicated rules. In fact, this set of rules — developed over decades — is so complex, perhaps only a handful of individuals alive even understand it anymore.

“While the problem of avoiding collisions is itself a complex question, the system we’ve built to handle this problem has essentially become too complicated for us to understand, and even experts sometimes react with surprise to its behaviour. This escalating complexity points to a larger phenomenon in modern life. When the systems designed to save our lives are hard to grasp, we have reached a technological threshold that bears examining.

“It’s one thing to recognise that technology continues to grow more complex, making the task of the experts who build and maintain our systems more complicated still, but it’s quite another to recognise that many of these systems are actually no longer completely understandable.”

The article cites numerous other impossibly complex systems, including the law:

“Even our legal systems have grown irreconcilably messy. The US Code, itself a kind of technology, is more than 22 million words long and contains more than 80,000 links within it, between one section and another. This vast legal network is profoundly complicated, the functionality of which no person could understand in its entirety.”

Steven Pinker, author of the recent optimistic bestseller Enlightenment Now (check back a couple posts in this series) suggests in an earlier book[3] that the human brain just isn’t equipped for the complexity of modern life:

“Maybe philosophical problems are hard not because they are divine or irreducible or workaday science, but because the mind of Homo Sapiens lacks the cognitive equipment to solve them. We are organisms, not angels, and our minds are organs, not pipelines to the truth. Our minds evolved by natural selection to solve problems that were life-and-death matters to our ancestors, not to commune with correctness or to answer any question we are capable of asking.”

In other words, we have our limits.

Imagine that.

So then… where do we turn for appropriately complex economic thinking? According to “complexity economics,” we turn to the source:  the economy itself, understood not by reference to historical theory or newly updated metaphor, but on its own data-rich and machine-intelligent terms.

We’ll go there next time.

[1] According to his TED bio, Bhu Srinivasan “researches the intersection of capitalism and technological progress.”

[2] Samuel Arbesman is the author. The Center’s mission is to “propel the future of technology policy and innovation.”

[3] How The Brain Works, which Pinker wrote in 1997 when he was a professor of psychology and director of The Center for Cognitive Neuroscience at MIT.