by Kate Raworth
Bubble, boom, and bust: the dynamics of finance
If financial traders were birds, their antics would indeed resemble those of a flock of starlings cavorting in the sky (the obvious difference being that starlings never crash). Those financial antics are due to what the speculator George Soros has called ‘the reflexivity of markets’: the pattern of feedbacks that kick in when market participants’ views influence the course of events, and the course of events, in turn, influences participants’ views.23 Whether we are financial traders or teenagers (or indeed both), our emerging self-portrait reveals that we are not isolated individuals driven by fixed preferences: we are deeply influenced by what goes on around us – and we often have fun being part of it. Trends are launched when a product’s popularity boosts its desirability to others, further raising its popularity, generating this season’s must-have toy, the hottest me-too gadget, and the latest viral dance craze (who can forget ‘Gangnam Style’?).
Less fun but almost as frequent are asset bubbles in which the price of a stock builds higher and higher before it ultimately bursts. The name of that phenomenon originated with the South Sea Bubble of 1720, an event that the great Sir Isaac Newton forbade to be mentioned in his presence ever after. In March of that year, the price of shares in the South Sea Company – which had been granted a British monopoly on trading with South American colonies – began to rise fast as false rumours of its successes abroad started to spread. Newton had already bought a few shares in the company and so in April he cashed them in for a large profit. But the South Sea stock price kept on rising fast and so, swept along by the nation’s enthusiasm, Isaac couldn’t resist the market’s lure. He jumped back in at a much higher price in June – just two months before the bubble finally peaked and burst. Newton lost his life savings as a result. ‘I can calculate the movement of stars, but not the madness of men,’ he famously said in the bubble’s aftermath.24 The master of mechanics had been confounded by complexity.
Like Newton, we all pay a high price when we don’t understand the dynamic systems on which our lives and livelihoods depend. That certainly became clear in the wake of the 2008 financial crash, which famously prompted the Queen to ask, ‘Why did no one see it coming?’ Before it happened, the equilibrium-thinking underpinning mainstream economic theory had lulled the vast majority of economic analysts into paying scant attention to the banking sector – both its structure and its behaviour. Incredible though it now seems, many major financial institutions – from the Bank of England and the European Central Bank to the US Federal Reserve – were using macroeconomic models in which private banks played no role at all: an omission that turned out to be a fatal error. As economist Steve Keen – one of the few who did see a crash coming – pithily put it, ‘Trying to analyse capitalism while leaving out banks, debt, and money is like trying to analyse birds while ignoring that they have wings. Good luck.’25
Thanks to the dominance of equilibrium thinking, most economic policymakers eschewed the idea that instability could arise from the dynamics at play within the economy itself. In the decade running up to the crash, and oblivious to the build-up of systemic risk, the UK’s chancellor, Gordon Brown, hailed the end of boom and bust,26 while Ben Bernanke, Governor of the Federal Reserve Board welcomed what he called ‘the Great Moderation’.27 After 2008, when the boom went very bust, many started to search for insights in the long-ignored work of the economist Hyman Minsky, especially his 1975 financial-instability hypothesis, which put dynamic analysis at the heart of macroeconomics.
Minsky had realised that – counter-intuitive though it sounds – when it comes to finance, stability breeds instability. Why? Because of reinforcing feedback loops, of course. During good economic times, banks, firms and borrowers all gain in confidence and start to take on greater risks, which pushes up the price of housing and other assets. This asset price rise, in turn, reinforces borrowers’ and lenders’ confidence along with their expectations that asset values will keep on rising. In Minsky’s own words, ‘The tendency to transform doing well into a speculative investment boom is the basic instability in a capitalist economy.’28 When prices eventually don’t keep pace with expectations, as will inevitably happen, mortgage defaults kick in, assets fall further in value, and – in what has been dubbed a ‘Minsky moment’ – finance goes off the cliff of insolvency, bringing on a crash. Guess what happens post-crash? Confidence gradually rebuilds and the process begins all over again in a rolling cycle of dynamic disequilibrium. There’s still a lot to learn from the chicken that crossed the road.
In 2008 the fallout from this inherent market instability was compounded by the financial regulators’ failure to understand the inherent dynamics of banking networks. Before the crash, those regulators worked on the assumption that networks always serve to disperse risk, and so the regulations that they devised only monitored the nodes in the network – individual banks – rather than overseeing the nature of their interconnections. But the crash made clear that a network’s structure can be robust-yet-fragile: usually behaving as a robust shock-absorber, but then – as the character of the network evolves – switching to becoming a fragile shock-amplifier. That switch is more likely to be triggered, discovered the Bank of England’s Andy Haldane, when networks have a few super-nodes acting as key hubs, too many connections between the nodes, and the small-world trait of creating short-cut connections between otherwise distant nodes. Between 1985 and 2005, the global financial network evolved to feature all three of these trigger traits but, lacking a systems perspective, regulators did not pick up on them.29 As Gordon Brown later admitted, ‘we created a monitoring system that was looking at individual institutions. That was the big mistake. We didn’t understand how risk was spread across the system, we didn’t understand the entanglements of different institutions with each other, and we didn’t understand – even though we talked about it – just how global things were.’30
Prompted by the 2008 crash, new dynamic models of financial markets are being built. Steve Keen has teamed up with computer programmer Russell Standish to develop the first systems-dynamics computer program – aptly named Minsky – which is a disequilibrium model of the economy that takes the feedbacks of banks, debt and money seriously. As Keen told me in his characteristic style, ‘Minsky finally gives wings to the economic bird, so at last we’ll have a chance of understanding how it flies.’31 Theirs is one among several promising complexity approaches to understanding the effects of financial markets on the macroeconomy.
Success to the successful: the dynamics of inequality
Inequality features only as a peripheral concern in the world of equilibrium economics. Given that markets are efficient at rewarding people, goes the theory, then those with broadly similar talents, preferences, and initial endowments will end up equally rewarded: any remaining differences must be due to differences in effort, and that provides a spur for innovation and hard work. But in the disequilibrium world that we inhabit – where powerful reinforcing feedbacks are in play – virtuous cycles of wealth and vicious cycles of poverty can send otherwise similar people spiralling to opposite ends of the income-distribution spectrum. It’s due to what systems experts have come to call the ‘Success to the Successful’ trap, which kicks off when the winners in one round of a game reap rewards that raise their chances of winning again in the next.
Equilibrium theory acknowledges that reinforcing feedbacks might sometimes prevail in business, resulting in oligopoly – the rule of the few – but it presents these cases as exceptions to the rule. As early as the 1920s, however, the Italian economist Piero Sraffa argued the opposite: when it comes to firms’ supply curves, increasing returns – not the so-called law of diminishing returns – are often likely to be the norm. As Sraffa pointed out, everyday experience shows that firms in many industries face falling unit costs as they expand their production, and so those industries tend towards oligopoly or even monopoly, rather than perfect competition.32 That perspective certainly
resonates with the corporate landscape we know today. In the food sector alone, four agribusiness giants known as the ABCD group (ADM, Bunge, Cargill, and Louis Dreyfus) control over 75% of the global grain trade. Another four account for over 50% of global seed sales, and just six agrochemical firms control 75% of the world’s fertiliser and pesticide market.33 In 2011, just four Wall Street banks – JPMorgan Chase, Citigroup, Bank of America, and Goldman Sachs – accounted for 95% of the financial industry’s derivatives trading in the US.34 It is a pattern of concentration that prevails in many other industries too, from media and computing to telecoms and supermarkets.
Anyone who has played the board game Monopoly is well versed in the dynamics of Success to the Successful: players who are lucky enough to land on expensive properties early in the game can buy them up, build hotels, and reap vast rents from their fellow players, thus accumulating a winning fortune as they bankrupt the rest. Fascinatingly, however, the game was originally called ‘The Landlord’s Game’ and was designed precisely to reveal the injustice arising out of such concentrated property ownership, not to celebrate it.
The game’s inventor Elizabeth Magie was an outspoken supporter of Henry George’s ideas and when she first created her game in 1903 she gave it two very different sets of rules to be played in turn. Under the ‘Prosperity’ set of rules, every player gained each time someone acquired a new property (echoing George’s call for a land value tax), and the game was won (by all) when the player who had started out with the least money had doubled it. Under the second, ‘Monopolist’ set of rules, players gained by charging rent to those who were unfortunate enough to land on their properties – and whoever managed to bankrupt the rest was the sole winner. The purpose of the dual sets of rules, said Magie, was for players to experience a ‘practical demonstration of the present system of land grabbing with all its usual outcomes and consequences’ and so understand how different approaches to property ownership can lead to vastly different social outcomes. ‘It might well have been called “The Game of Life”,’ remarked Magie, ‘as it contains all the elements of success and failure in the real world.’ But when the games manufacturer Parker Brothers bought the patent for The Landlord’s Game from Magie in the 1930s, they relaunched it simply as Monopoly, and provided the eager public with just one set of rules: those that celebrate the triumph of one over all.35
Distributional dynamics that play out in board games show up in computer simulations of the economy too. It was Robert Solow, the outspoken critic of modern macro, who ridiculed equilibrium economic models by demonstrating that, far from modelling markets of many players, they were actually made up of a single ‘representative agent’ – reducing the economy to just one typical consumer-worker-owner who responds predictably to ‘external’ shocks. Since the 1980s, complexity economists have been developing alternative approaches including ‘agent-based’ modelling which starts out with a diverse array of agents all following a simple set of rules as they continually respond and adapt to their surroundings. Once the computer model is set up, the programmers essentially press ‘go’, launching those agents into action, then sit back to watch and learn from the dynamic patterns that emerge from their interplay. And there is a lot to learn.
In a 1992 landmark computer simulation known as Sugarscape, modellers Joshua Epstein and Robert Axtell created a miniature virtual society to see how wealth would be distributed over time. Sugarscape consists of a 50-by-50 grid-based landscape – like a giant chessboard – featuring two large sugar mountains that are separated by sugar-sparse plains.36 Scattered across that landscape are many sugar-hungry agents, some able to move faster than others, some seeing further, and some burning sugar faster, as they all scan the grid, competing to move on to the squares piled high with the sugary fuel that will sustain them. At the outset, sugar stocks are randomly distributed between the agents: a few have more, a few less, but most have a middling share. As the simulation gets under way, however, it doesn’t take long for these sweet-toothed agents to find themselves deeply divided into a small elite of sugar super-rich and the vast mass of sugar-poor. Yes, their varying attributes of speed, eyesight, metabolism and starting point can explain some of this divergence, but – importantly – these attributes alone cannot account for the striking extremes of inequality that arise.
That inequality emerges, in fact, largely from the dynamics inherent in Sugarscape society: sugar is wealth, and having more helps in getting more, a classic case of Success to the Successful at work. Most striking, however, is that even small chance differences between the agents – like having an early lucky break or making a first false move in the search for sugar – can rapidly amplify into big differences, propelling them to vastly different fates in their starkly split saccharine society.37 The computer world of Sugarscape is of course not reality but its familiar dynamics further debunk the claim that income inequalities mostly reflect talent and merit in society.
The Success to the Successful dynamic was spotted long before Monopoly and Sugarscape came along. Two thousand years ago, the notion that ‘the rich get richer and the poor get poorer’ was noted in the Bible and hence came to be known as ‘the Matthew Effect’. Its tell-tale pattern of accumulative advantage, coupled with spiralling disadvantage, can be seen in children’s educational outcomes, in adults’ employment opportunities, and of course in terms of income and wealth. And that financial dynamic is certainly alive today. Between 1988 and 2008, the majority of countries worldwide saw rising inequality within their borders, resulting in a hollowing out of their middle classes. Over those same 20 years, global inequality fell slightly overall (mostly thanks to falling poverty rates in China) but it increased significantly at the extremes. More than 50% of the total increase in global income over that period was captured by just the richest 5% of the world’s population, while the poorest 50% of people gained only 11% of it.38 Getting into the Doughnut requires reversing these widening gaps of income and wealth, so finding ways to offset and weaken the Success to the Successful feedback loop will be key, and we will explore some of them in Chapter 5.
Water in the tub: the dynamics of climate change
Economic externalities are framed – thanks to their very name – as a peripheral concern in mainstream theory. But when we recast them as effects and recognise that the economy is embedded within the biosphere – as we did in Chapter 2 – it quickly becomes clear that those effects could build up as feedback and disrupt the economic system that first generated them. That is certainly the case with so-called environmental externalities, like the build-up of greenhouse gases in the atmosphere, which risk triggering catastrophic effects of climate change. No wonder systems thinkers like John Sterman, director of MIT’s systems dynamics group, are intent on finding ways to overcome policymakers’ blind spots when it comes to tackling climate change because, unlike in banking crises, there is no chance of a last-minute bail-out.
Understanding the build-up of pressure in the climate system depends upon understanding a basic relationship between the flow of carbon dioxide emissions and their stock, or concentration, in the atmosphere. To his alarm, Sterman discovered that even his top students at MIT had a surprisingly poor intuitive grasp of how such stock–flow dynamics work: most thought that simply stopping global CO2 emissions from rising would be enough to halt the increase of CO2 in the atmosphere. So he turned to a classic analogy and drew the atmosphere as a giant bathtub with an open tap and open plughole: the tub fills as new emissions pour in and empties as carbon dioxide is both taken up by plant photosynthesis and dissolved in the oceans. The metaphor’s message? Just as a bathtub will only start to empty if water pours in from the tap more slowly than it drains out of the plughole, so carbon dioxide concentration in the atmosphere will only fall if new emissions flow in more slowly than CO2 is drawn out. When Sterman first drew the carbon bathtub in 2009, global annual inflows of CO2 were 9 billion tons, compared to outflows of just 5 billion tons: it meant that annual emissions had to
fall by half merely to start reducing atmospheric concentrations. If MIT students found that hard to grasp, he realised, then no doubt policymakers did as well and, ‘that means they think it’s easier to stabilize greenhouse gases and stop warming than it is’, he warned.39
Following in the footsteps of Elizabeth Magie, Sterman and his colleagues set out to create a game that would teach climate dynamics to its players through experience. They came up with a user-friendly computer simulation, known as C-ROADS (short for Climate Rapid Overview and Decision Support) to help governments see the impacts of their policy plans. C-ROADS instantly adds up all nations’ greenhouse gas reduction pledges to show their combined long-term implications for global emissions, atmospheric concentrations, temperature change, and sea-level rise. It has been used by negotiating teams in the US, China, the EU and beyond, transforming their understanding of the speed and scale of cuts needed worldwide. ‘Without tools like these,’ explains Sterman, ‘there is no hope for developing the systems-thinking capabilities or understanding of the climate among any of the constituent stakeholder groups.’40
C-ROADS has been highly valuable for running role-plays of international climate negotiations over the past decade, often with real policymakers. Seeking to recreate the power dynamics at play, the C-ROADS team offer those representing powerful countries a literal seat at the table which is loaded with plentiful snacks, whilst leaving least developed country representatives to sit on the floor. So when the President of Micronesia took part in a role-play in 2009, he duly insisted on taking his place on the floor. As the mock negotiations got under way and the major powers made their usual inadequate pledges, the simulated sea level rose by one metre. So the C-ROADS team duly covered up all those on the floor – including the Micronesian President – with a big blue sheet. ‘He was thrilled,’ recounted Sterman, ‘because for the first time people saw what the implications of sea level rise would be.’41 Without understanding or experiencing the effects of stock and flow dynamics, we have little chance of recognising the speed and scale of energy transformation required to bring ourselves back within the planetary boundary for climate change.