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Nose Pressed Up Against the Glass

nose against the glass

You’re on the outside looking in. What you want is only a window pane away, but it might as well be on Mars. Novelist Maria E. Andreu captures the feeling:

wuthering heights“There is a wonderful scene in the 1939 film version of Wuthering Heights… in which Heathcliff and Catherine sneak on to the grounds of the Linton house at night. The Lintons, the rich neighbors, are having a grand party. Heathcliff and Catherine watch through the window, unseen. It’s exactly what’s meant by ‘nose pressed up against the glass,’ watching but not being able to participate.

“You can see a lot in their faces as they watch the others dance. Catherine, the daughter of a landed ‘gentleman,’ gets a look that lets you know that she’s intrigued, beginning to want to let go of her wild childhood and take her place in the Lintons’ world. Healthcliff, the servant who adores Catherine, knows that even if he could stop being poor, he would never belong there. He will always be watching from outside the glass.”

Nose pressed up against the glass — it’s an enduring image in literature and in life. Ms. Andreu continues:

“I’ve thought about this scene a lot. I’ve used the image in my writing. It illustrates how I’ve felt sometimes, able to see ‘the good life’ but not able to live it. Most of my life, the Heathcliff in me has weighed heavy inside my heart.”

But then one day the magic happened, and suddenly she found herself transported to the other side of the window pane:

“Yesterday, I got a rave review for my novel that comes out in a month and a half. In my email, I got an invitation to a launch party for another author’s book. I packed to go to a book signing and remembered I needed an extra outfit for an industry cocktail party and the ‘members only’ dinner afterwards with people from my publishing house.

“If that’s not being inside the party, I don’t know what is.

“Someone has opened the door of the party for some fresh air, seen me lurking, and extended a hand of friendship to let me in. It is an unbelievable feeling. I live a life of impossible splendor, of magical beauty, of infinite luck. And I am so deeply grateful.”

We’d feel the same way, if we ever got so lucky. (Assuming we’ve been working hard enough to get lucky — here’s The Quote Investigator on where that saying came from.)

hard work luck

In economic terms, the distance between Heathcliff and the Lintons is a matter of social capital. Ryan Avent, author of The Wealth of Humans, distinguishes between human capital and social capital. Human capital, he saredys, is a particularly focused and useful form of knowledge that an individual gains through education, hard work, experience, on-the-job training, etc. It’s the hard work part of the formula. Social capital, on the other hand, is the opportunity part, and it’s not just personal, it’s cultural. Avent says it’s “like human capital… but is only valuable in particular contexts, within which a critical mass of others share the same social capital.”

red velvet rope 2

For those not already in the social capital club, converting human capital into social capital requires upward mobility. Ms. Andreu’s upward mobility moment was getting her “members only” invitation — official permission to duck under the red velvet rope and join an exclusive gathering where she could schmooze the “others [who] share the same social capital.” Heathcliff, on the other hand, never got his upward mobility moment. As a result, there wasn’t just a glass window pane between him and the Lintons, there was a glass ceiling.

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Nose pressed against the glass… glass ceiling… we’ve heard those expressions before. Nowadays, another glass metaphor has entered the economic lexicon:   the “glass floor, which protects the upper middle class against the risk of downward mobility.” (My emphasis. The quote is from Dream Hoarders:  How the American Upper Middle Class is Leaving Everyone Else in the Dust, Why That is a Problem, and What to Do About It by Richard V. Reeves.)

Hoping to move up? Afraid of moving down? These days, it’s hard to do either. And if you’re hoping to move up, there’s one additional, elusive element required for membership in the red velvet club:  the notion of identity — the need to be the kind of person who belongs there. In this short video (click the image below), Michael Port, author of the bestseller Book Yourself Solid, asks, “What makes [red velvet rope people] who they are?” He answers that it’s “their quality, their characteristics, their personality — things that are innate, are part of who they are as people, not necessarily their circumstances.”

red velvet rope

We’ll be looking lots more at upward mobility and social capital in the weeks to come.

The End of the Firm

industrial revolution factory

 “The official line is that we all have rights and live in a democracy. Other unfortunates who aren’t free like we are have to live in police states. These victims obey orders or else, no matter how arbitrary. The authorities keep them under regular surveillance. State bureaucrats control even the smallest details of everyday life. The officials who push them around are answerable only to higher-ups. Informers report regularly to the authorities. All this is supposed to be a very bad thing — and so it is, although it is nothing but a description of the modern workplace.”

Bob Black, The Abolition of Work and Other Essays (1985)

Peter Drucker’s famous dictum  “If you can’t measure it, you can’t manage it” established math and management as the indisputable co-sovereigns of the modern workplace. As it turns out, Drucker apparently never actually said that[1], but the concept has dominated the workplace since the advent of factories and railroads, telegraphs and electricity. Consider, for example, what it’s like to work at Amazon.

amazon 2

But, while math and management prospered together under the Industrial Revolution’s mechanistic worldview, today’s digitally-driven marketplace demands a freshly-nuanced management style, or in some cases, no management at all. Either idea challenges an even more foundational historical assumption:  that commerce is best conducted by a firm that must be managed. Eliminate the firm and you eliminate the need to manage it. Get rid of both, and you have an unimaginably different “description of the modern workplace” than Bob Black wrote about 33 years ago.

Last time, we looked at an article by science writer and artificial intelligence engineer George Zarkadakis called “The Economy Is More A Messy, Fractal Living Thing Than A Machine.” In it, he says this about the firm:

Ever since the invention of the assembly line, corporations have been like medieval cities: building walls around themselves and then trading with other ‘cities’ and consumers. Companies exist because of the need to protect production from volatile market fluctuations, and because it’s generally more efficient to consolidate the costs of getting goods and services to market by putting them together under one roof.  So said the British economist Ronald Coase in his paper ‘The Nature of the Firm’ (1937).

“Why do firms exist?” asks Ryan Avent in his book The Wealth of Humans:  Work, Power, and Status in the Twenty-First Century (2016). He provides the same answer as Zarkadakis:

According to a 1937 paper by Nobel Prize- winning economist Ronald Coase, it’s to bring all the necessary people, processes, and information under one roof, instead of contracting it all out. In exchange for the convenience of one-stop shopping, one-size-fits-all,  employees trade their independence and the possibility of greater personal market returns for the firm’ management structure and financial capital, which — as long as they conform to the company culture –  the way we do things around here — promises to keep them on task and to deliver a paycheck in return.

Today, however, the new “gig economy” is fast making that unimaginable the new normal — and that’s only the beginning, says Zarkadakis:

Now, in an era of Ubers-for-everything, companies are changing into platforms that enable, rather than enact, core business processes. The cost of reaching customers has dropped dramatically thanks to the ubiquity of digital networks, and production is being pushed outside the company wall, on to freelancers and self-employed contractors. Market and price fluctuations have been defanged as machine learning and predictive analytics help companies manage such ructions, and on-demand services for labour, office space and infrastructure allow them to be more responsive to changing conditions. Coase’s theory is nearing its expiry date.

The so-called ‘gig economy’ is only the beginning of a profound economic, social and political transformation. For the moment, these new ways of working are still controlled by old-style businesses models – platforms that essentially sell ‘trust’ via reviews and verification, or by plugging into existing financial and legal systems. Airbnb, eBay and Uber succeed in making money out of other people’s work and assets because they provide guarantees for good seller-buyer behaviour, while connecting to the ‘old world’ of banks, courts and government. But this hybrid model of doing digital business is about to change.

Avent concurs, and describes two key dynamics of the new anti-firm business model:  operating culture and rent: — how a business gets things done, and whether it owns the kinds of assets it can let others use, for a price:

Current workplace trends are bidding fair to tear down the firm model of operating. If you take employees out from under the firm umbrella — make them mostly freelancers, outsource jobs to countries on the make — then what’s left of value is mostly the company’s way of getting things done and the assets for which it can charge rent, in the economic sense of billing a premium for scarce assets. How assets become scarce becomes an essential policy-making function. These become essential “intangible” or “social” capital, replacing “human” capital.]

We’ll be talking more about social capital, rent, and other changing dynamics of the workplace.

[1] According to the Drucker Institute, Drucker never actually said that. And see this Forbes article for a rousing condemnation of the idea.

Who Controls the World?

swarm of Starling birds (2)

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 — see this YouTube video for an example — 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

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.

Rolling the Rock:  Lessons From Sisyphus on Work, Working Out, and Life

sisyphus

Here’s a link to my latest LinkedIn Pulse article:  Rolling the Rock:  Lessons From Sisyphus on Work, Working Out, and Life.  In it, I talk about a key psycho-neurological function known as “the pleasure of being the cause.”  As I say in the article, “The conversation is going to get philosophical, but it will be worth it. So get yourself a cup, close the door, turn off the ringer, take a breath. This won’t be spin. It’s based on good ideas from smart people.”

Enjoy!

 

 

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.

Economics + Math = Science?

mathematical equation

The human brain is wired to recognize patterns, which it then organizes into higher level models and theories and beliefs, which in turn it uses to explain the past and present, and to predict the future. Models offer the consolation of rationality and understanding, which provide a sense of control. All of this is foundational to classical economic theory, which assumes we approach commerce equipped with an internal rational scale that weighs supply and demand, cost and benefit, and that we then act according to our assessment of what we give for what we get back. This assumption of an internal calculus has caused mathematical modeling to reign supreme in the practice of economics.

The trouble is, humans aren’t as innately calculating as classical economics would like to believe — so says David Graeber, professor of anthropology at the London School of Economics, in his new book Bullshit Jobs: :

“According to classical economic theory, homo oeconomicus, or “economic man” — that is, the model human being that lies behind every predication made by the discipline — is assumed to be motivated by a calculus of costs and benefits.

“All the mathematical equations by which economists bedazzle their clients, or the public, are founded on one simple assumption:  that everyone, left to his own devices, will choose the course of action that provides the most of what he wants for the least expenditure of resources and effort.

 “It is the simplicity of the formula that makes the equations possible: if one were to admit that humans have complicated emotions, there would be too many factors to take into account, it would be impossible to weigh them, and predictions would not be made.

“Therefore, while an economist will say that while of course everyone is aware that human beings are not really selfish, calculating machine, assuming they are makes it possible to explain

“This is a reasonable statement as far as it goes. The problem is there are many dimensions of human life where the assumption clearly doesn’t hold. — and some of them are precisely in the domain of what we like to call the economy.”

Economics’ reliance on mathematics has been a topic of lively debate for a long time:

“The trouble… is that measurement and mathematics do not guarantee the status of science – they guarantee only the semblance of science. When the presumptions or conclusions of a scientific theory are absurd or simply false, the theory ought to be questioned and, eventually, rejected. The discipline of economics, however, is presently so blinkered by the talismanic authority of mathematics that theories go overvalued and unchecked.

“In 1886, an article in Science accused economics of misusing the language of the physical sciences to conceal ‘emptiness behind a breastwork of mathematical formulas’. More recently, Deirdre N McCloskey’s The Rhetoric of Economics(1998) and Robert H Nelson’s Economics as Religion (2001) both argued that mathematics in economic theory serves, in McCloskey’s words, primarily to deliver the message ‘Look at how very scientific I am.’

“After the Great Recession, the failure of economic science to protect our economy was once again impossible to ignore. In 2009, the Nobel Laureate Paul Krugman tried to explain it in The New York Times with a version of the mathiness diagnosis. ‘As I see it,’ he wrote, ‘the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.’ Krugman named economists’ ‘desire… to show off their mathematical prowess’ as the ‘central cause of the profession’s failure’.

“The result is people… who trust the mathematical exactitude of theories without considering their performance – that is, who confuse math with science, rationality with reality.

“There is no longer any excuse for making the same mistake with economic theory. For more than a century, the public has been warned, and the way forward is clear. It’s time to stop wasting our money and recognise the high priests for what they really are: gifted social scientists who excel at producing mathematical explanations of economies, but who fail, like astrologers before them, at prophecy.”

The New Astrology:  By fetishising mathematical models, economists turned economics into a highly paid pseudoscience,” Aeon Magazine

Economists may bristle at being compared to astrologers, but as we have seen, their skill at prediction seems about comparable.

In the coming weeks we’ll look at other models emerging from the digital revolution, consider what they can tell us that classical economic theory can’t, and how they are affecting the world of work.