doughnut 7 growth

GET SAVVY WITH SYSTEMS

from mechanical equilibrium to dynamic complexity

Newton’s apple has a lot to answer for. In 1666 as the brilliant young scientist sat in his mother’s Lincolnshire garden, he marvelled – it is said – at how an apple fell: why never sideways or up, but always down ? The answer prompted his famous insight into gravity and the laws of motion, which went on to revolutionise science. But, two centuries later, those same laws also gave rise to physics envy, misplaced metaphors, and painfully narrow thinking in economics. If only – just before that apple fell – young Isaac had also marvelled at how it grew: in a fascinating, ever-evolving interplay of trees and bees, sun and leaves, roots and rain, blossom and seeds. It might have led him to equally revolutionary insights into the nature of complex systems, thus transforming the history of science. It would have changed the course of economics, too, inspiring his economic admirers with a far more fruitful metaphor. Today we would be talking not of the market mechanism but of the market organism – and we’d be so much the wiser for it.
So much for that fantasy. It was the apple as it fell that grabbed Isaac’s attention and led to his groundbreaking discoveries. Craving the authority of science, economists then mimicked Newton’s laws of motion in their theories, describing the economy as if it were a stable, mechanical system. But we now know it is far better understood as a complex adaptive system, made up of interdependent humans in a dynamic living world. So if we are to have half a chance of bringing ourselves into the Doughnut, then it is essential to shift the economist’s attention from the apple as it falls to the apple as it grows, from linear mechanics to complex dynamics. Bid farewell to the market as mechanism and discard the engineer’s hard hat: it’s time to don a pair of gardening gloves instead.
Overcoming our inheritance

Thanks to the last 100,000 years of evolution that fine-tuned Homo sapiens , we humans don’t find it so easy to think in terms of complex systems. For millennia, people lived relatively short lives in small groups, learned from quick feedback (put your hand in the fire: it gets burned) and had little impact on their wider surroundings. Hence our brains evolved to cope with the near, the short term and the responsive, while expecting incremental, linear change. Add to that our evident desire for equilibrium and resolution: we promise it in our stories, with their happily-ever-after endings, and seek it in our music with harmonic melodies that resolve. But these traits leave us ill equipped when the world turns out to be dynamic, unstable and unpredictable.
Of course we know that counter-intuitive things do happen, so we warn ourselves with folk sayings. It was the straw that broke the camel’s back (incremental change can lead to sudden collapse). Don’t put all your eggs in one basket (a lack of diversity makes you vulnerable). A stitch in time saves nine (beware of escalating effects). What goes around comes around (everything is connected). Wise advice, but it still doesn’t make it easy for us to anticipate and interpret the complex world as it comes at us.
If our understanding of complexity has been hampered by 100,000 years of evolution, then it has been topped off by 150 years of economic theory that has reinforced our biases with mechanistic models and metaphors. In the late nineteenth century a handful of mathematically minded economists set out to make economics a science as reputable as physics. And they turned to differential calculus – which could so elegantly describe the trajectory of falling apples and orbiting moons – to describe the economy with a set of axioms and equations. Just as Newton had uncovered the physical laws of motion that explained the world from the scale of a single atom to the movement of the planets, they sought to uncover the economic laws of motion that explained the market, starting with a single consumer and scaling up to national output.
The British economist William Stanley Jevons set the metaphorical ball rolling in the 1870s when he claimed that ‘the Theory of Economy … presents a close analogy to the Science of Statical Mechanics, and the Laws of Exchange are found to resemble the Laws of Equilibrium of a lever’. 1 Over in Switzerland, the engineer-turned-economist Léon Walras had a similar vision, declaring that ‘the pure theory of economics … is a science which resembles the physio-mathematical sciences in every respect’ and – as if to prove it – he started referring to market exchange as ‘the mechanism of competition’. 2 They and others likened the role played by gravity in pulling a pendulum to rest to the role played by prices in pulling markets into equilibrium. As Jevons put it:
Just as we measure gravity by its effects in the motion of a pendulum, so we may estimate the equality or inequality of feelings by the decisions of the human mind. The will is our pendulum, and its oscillations are minutely registered in the price lists of the markets. I know not when we shall have a perfect system of statistics, but the want of it is the only insuperable obstacle in the way of making Economics an exact science. 3
Such mechanical metaphors – from the lever to the pendulum – must have seemed cutting edge in their day. No wonder these economists put them at the heart of their theories of how individuals and firms behave, thus founding a field that came to be known as microeconomics. But in order to make this new theory echo Newton’s laws and conform to the rigours of differential calculus, Jevons, Walras and their fellow mathematical pioneers had to make some heroically simplifying assumptions about how markets and people work. Crucially, the nascent theory hinged on assuming that, for any given mix of preferences that consumers might have, there was just one price at which everyone who wanted to buy and everyone who wanted to sell would be satisfied, having bought or sold all that they wanted for that price. In other words, each market had to have one single, stable point of equilibrium, just as a pendulum has only one point of rest. And for that condition to hold, the market’s buyers and sellers all had to be ‘price-takers’ – no single actor being big enough to have sway over prices – and they had to be following the law of diminishing returns. Together these assumptions underpin the most widely recognised diagram in all of microeconomic theory, and the first one that must be mastered by every novice student: the diagram of supply and demand.
Supply and demand: the point at which price matches supply with demand is the point of market equilibrium.
What lies behind this iconic pair of crossing lines? Think of a good, any good (let’s say pineapples) and here’s how it works. The demand curve shows how many pineapples customers will want to buy at each price, given their aim of maximising their utility, or satisfaction. The curve slopes downwards because the more pineapples a customer buys, the less utility they are likely to gain from buying yet one more – an assumption known as the diminishing marginal utility of consumption – and so they will be willing to pay a little less for each successive one. The supply curve, in contrast, shows how many pineapples the sellers will be prepared to supply for any given price, given their aim of maximising their profits. Why does the curve slope upwards? Because – the theory goes – if each pineapple farmer has a fixed plot of land, then the cost of growing yet more pineapples on it will start to rise – that’s the law of diminishing marginal returns – and so they will require a higher price for supplying each successive piece of fruit.
Alfred Marshall, who drew the definitive version of this diagram in the 1870s, likened the criss-crossing of its lines to a pair of scissors – yet another mechanical analogy – to explain the mystery of how market prices are set. Just as a pair of scissors does not cut paper with its upper blade or lower blade alone but precisely where the two blades cross, so, he argued, market price is set not by suppliers’ costs nor consumers’ utility alone, but precisely where costs and utility meet – and there lies the point of market equilibrium.
Walras had an ambitious agenda for these scissors: he was convinced it was possible to scale the analysis up from a single commodity to all commodities, so creating a model of the whole market economy. And, he reasoned, if those markets were comprised of fully informed, small-scale competitive sellers and buyers, then the economy would reach a point of equilibrium that maximised total utility. In other words – in a neat echo of Smith’s invisible hand – it would, for any given income distribution, produce the best possible outcome for society as a whole. The mathematical techniques did not yet exist for Walras to prove his hunch but his agenda was later picked up by Kenneth Arrow and Gerard Debreu, who set out its equations in their 1954 model of general equilibrium. It appeared to be a landmark proof, giving microeconomic underpinning to macroeconomic analysis, launching a seemingly unified economic theory and laying the foundations of what has been known ever since as ‘modern macro’. 4
The theory looks complete, sounds impressively like physics, and is set out in authoritative equations. But it is deeply flawed. Thanks to the interdependence of markets within an economy, it is just not possible to add up all individuals’ demand curves to get a reliable downward-sloping demand curve for the economy as a whole. And without that, there is no promise of equilibrium. This is not news to economists, or at least it shouldn’t be: in the 1970s several smart theorists realised (to their own horror) that the foundations of equilibrium theory didn’t hold up. But the implications of their insight (catchily known as the Sonnenschein–Mantel–Debreu conditions) were so devastating for the rest of the theory that the disproof seems to have been hidden, ignored or brushed aside in the textbooks and the teaching, leaving students ever since unaware that anything was fundamentally out of whack with the equilibrating pulleys and pendulum of the market mechanism. 5
As a result, general equilibrium theories dominated macroeconomic analysis through the second half of the twentieth century, and all the way up to the 2008 financial crash. The ‘New Classical’ variants of equilibrium theory – which assume that markets adjust instantly to shocks – jostled for attention with so-called ‘New Keynesian’ variants that assume there will be adjustment delays due to ‘sticky’ wages and prices. Both variants failed to see the crash coming because – being built on the presumption of equilibrium, while simultaneously overlooking the role of the financial sector – they had little capacity to predict, let alone respond to, boom, bust and depression.
With such ill-fitting models dominating macroeconomic analysis, some big-name insiders began to critique the very theories that they had helped to legitimise. Robert Solow, known as the father of neoclassical economic growth theory and long-time collaborator of Paul Samuelson, became an outspoken critic, first in his 2003 speech bluntly entitled ‘Dumb and Dumber in Macroeconomics’, then in analyses that mocked the theory’s stringent assumptions. 6 The general equilibrium model, he pointed out, in fact depends upon there being just one single, immortal consumer-worker-owner maximising their utility into an infinite future, with perfect foresight and rational expectations, all the while served by perfectly competitive firms. How on earth did such absurd models come to be so dominant? In 2008, Solow gave his view:
I am left with a puzzle, or even a challenge. What accounts for the ability of ‘modern macro’ to win hearts and minds among bright and enterprising academic economists? … There has always been a purist streak in economics that wants everything to follow neatly from greed, rationality, and equilibrium, with no ifs, ands, or buts … The theory is neat, learnable, not terribly difficult, but just technical enough to feel like ‘science.’ Moreover it is practically guaranteed to give laissez-faire-type advice, which happens to fit nicely with the general turn to the political right that began in the 1970s and may or may not be coming to an end. 7
One thing that is clearly coming to an end is the credibility of general equilibrium economics. Its metaphors and models were devised to mimic Newtonian mechanics, but the pendulum of prices, the market mechanism, and the reliable return to rest are simply not suited to understanding the economy’s behaviour. Why not? It’s just the wrong kind of science.
No one made this point more powerfully than Warren Weaver, the director of natural sciences at the Rockefeller Foundation, in his 1948 article, ‘Science and Complexity’. Looking back over the last three hundred years of scientific progress, while simultaneously looking forward at the challenges facing the world, Weaver clustered together three kinds of problems that science can help us to understand. At one extreme lie problems of simplicity , involving just one or two variables in linear causality – a rolling billiard ball, a falling apple, an orbiting planet – and Newton’s laws of classical mechanics do a great job of explaining these. At the other extreme, he wrote, are problems of disordered complexity involving the random movement of billions of variables – such as the motion of molecules in a gas – and these are best analysed using statistics and probability theory.
In between these two branches of science, however, lies a vast and fascinating realm: problems of organised complexity , which involve a sizeable number of variables that are ‘interrelated in an organic whole’ to create a complex but organised system. Weaver’s examples came close to asking the very questions that Newton’s apple failed to prompt. ‘What makes an evening primrose open when it does? Why does salt water fail to satisfy thirst? … Is a virus a living organism?’ He noted that economic questions came into this realm, too. ‘On what does the price of wheat depend? … To what extent is it safe to depend on the free interplay of such economic forces as supply and demand? … To what extent must systems of economic control be employed to prevent the wide swings from prosperity to depression?’ Indeed, Weaver recognised that most of humanity’s biological, ecological, economic, social and political challenges were questions of organised complexity, the realm that was least understood. ‘These new problems, and the future of the world depends on many of them, require science to make a third great advance,’ he concluded. 8
That third great advance got under way in the 1970s when complexity science – which studies how relationships between the many parts of a system shape the behaviour of the whole – began to take off. It has since transformed many fields of research, from the study of ecosystems and computer networks to weather patterns and the spread of disease. And although it is all about complexity, its core concepts are actually quite simple to grasp – meaning that, despite our instincts, we can all learn, through training and experience, to be better ‘systems thinkers’.
A growing number of economists are thinking in systems too, making complexity economics, network theory, and evolutionary economics among the most dynamic fields of economic research. But, thanks to the lasting influence of Jevons and Walras, most economics teaching and textbooks still introduce the essence of the economic world as linear, mechanical and predictable, summed up by the market’s equilibrating mechanism. It’s a mindset that will leave future economists deeply ill-equipped to handle the complexity of the contemporary world.
In a playful ‘look back from 2050’ the economist David Colander recounts that, by 2020, the majority of scientists – from physicists to biologists – had already realised that complexity thinking was essential for understanding much of the world. Economists, however, were a little slower on the uptake and it was not until 2030 that ‘most economic researchers believed that the economy was a complex system that belonged within complexity science’. 9 If his history of the future should turn out to be right, it may well be too late. Why wait until 2030 when we can ditch the ill-chosen metaphors of Newtonian physics and get savvy with systems now?
The dance of complexity

At the heart of systems thinking lie three deceptively simple concepts: stocks and flows, feedback loops, and delay. They sound straightforward enough but the mind-boggling business begins when they start to interact. Out of their interplay emerge many of the surprising, extraordinary and unpredictable events in the world. If you have ever been mesmerised by the sight of thousands of starlings flocking at sunset – in a spectacle poetically known as a murmuration – then you’ll know just how extraordinary such ‘emergent properties’ can be. Each bird twists and turns in flight, using phenomenal agility to stay a mere wingspan apart from its neighbours, while tilting as they tilt. But as tens of thousands of birds gather together, all following these same simple rules, the flock as a whole becomes an astonishing swooping, pulsing mass against the evening sky.
So what is a system? Simply a set of things that are interconnected in ways that produce distinct patterns of behaviour – be they cells in an organism, protestors in a crowd, birds in a flock, members of a family, or banks in a financial network. And it is the relationships between the individual parts – shaped by their stocks and flows, feedbacks, and delay – that give rise to their emergent behaviour.
Stocks and flows are the basic elements of any system: things that can get built up or run down – just like water in a bath, fish in the sea, people on the planet, trust in a community, or money in the bank. A stock’s levels change over time due to the balance between its inflows and outflows. A bathtub fills or empties depending on how fast water pours in from the tap versus how fast it drains out of the plughole. A flock of chickens grows or shrinks depending on the rate of chicks born versus chickens dying. A piggy bank fills up if more coins are added than are taken away.
If stocks and flows are a system’s core elements, then feedback loops are their interconnections, and in every system there are two kinds: reinforcing (or ‘positive’) feedback loops and balancing (or ‘negative’) ones. With reinforcing feedback loops, the more you have, the more you get. They amplify what is happening, creating vicious or virtuous circles that will, if unchecked, lead either to explosive growth or to collapse. Chickens lay eggs, which hatch into chickens, and so the poultry population grows and grows. Likewise, in the vengeful tit-for-tat of playground fights, a single rough shove can soon escalate into a full-blown bust-up. Interest earned on savings adds to those savings, increasing future interest payments, and so wealth accumulates. But reinforcing feedback can lead to collapse too: the less you have, the less you get. If people lose confidence in their bank and withdraw their savings, for example, it will start to run out of cash, deepening the loss of confidence and leading to a run on the bank.
If reinforcing feedbacks are what make a system move, then balancing feedbacks are what stop it from exploding or imploding. They counter and offset what is happening, and so tend to regulate systems. Our bodies use balancing feedbacks to maintain a healthy temperature: get too hot and your skin will start sweating in order to cool you down; get too cold and your body will start shivering in an attempt to warm itself up. A household’s thermostat works in a similar way to stabilise room temperature. And in a playground scuffle, someone is likely to step in and try to break it up. In effect, balancing feedbacks bring stability to a system.
Complexity emerges from the way that reinforcing and balancing feedback loops interact with one another: out of their dance emerges the system’s behaviour as a whole, and it can often be unpredictable. The simplest depiction of the ideas at the heart of systems thinking is a pair of feedback loops, and the one shown here tells a simple story of chickens, eggs, and crossing the road. 10
Each arrow shows the direction of causation and comes with a plus or minus sign. A plus sign indicates that the effect is positively related to the cause (more chickens result in more attempted road crossings, for example) while a minus sign stands for the reverse (more attempted road crossings result in fewer chickens). Each pair of arrows creates a loop, labelled R if it is reinforcing and B if it is balancing. On the left, more chickens lay more eggs that hatch into more chickens: a reinforcing loop. On the right, more chickens make more attempted road crossings, which results in fewer chickens: a balancing loop. When both feedback loops are in play in a highly simplified system like this one (assuming there is at least one rooster in the flock and no shortage of grain), what might happen to the size of the poultry population over time? Depending on the relative strength of the two loops – the rate at which chickens produce chicks versus the rate at which chickens get hit – the flock might grow exponentially, collapse, or even come to oscillate continuously around a stable size if there is a significant delay between chicks hatching and their attempting to cross the road.
Feedback loops: the fundamentals of complex systems. Reinforcing feedback (R) amplifies what is happening, while balancing feedback (B) counters it. Their interaction creates complexity.
Delays such as this – between inflows and outflows – are common in systems and can have big effects. Sometimes they bring useful stability to a system, allowing stocks to build up and act as buffers or shock absorbers: think energy stored in a battery, food in the cupboard, or savings in the bank. But stock–flow delays can produce system stubbornness too: no matter how much effort gets put in, it takes time to, say, reforest a hillside, build trust in a community, or improve a school’s exam grades. And delay can generate big oscillations when systems are slow to respond – as anyone knows who has been scalded then frozen then scalded again while trying to master the taps on an unfamiliar shower.
It is out of these interactions of stocks, flows, feedbacks and delays that complex adaptive systems arise: complex due to their unpredictable emergent behaviour, and adaptive because they keep evolving over time. Beyond the realm of starlings and chickens, bathtubs and showers, it soon becomes clear just how powerful systems thinking can be for understanding our ever-evolving world, from the rise of corporate empires to the collapse of ecosystems. Many events that first appear to be sudden and external – what mainstream economists often describe as ‘exogenous shocks’ – are far better understood as arising from endogenous change. In the words of the political economist Orit Gal, ‘complexity theory teaches us that major events are the manifestation of maturing and converging underlying trends: they reflect change that has already occurred within the system’. 11
From this perspective, the 1989 fall of the Berlin Wall, the 2008 collapse of Lehman Brothers and the imminent collapse of the Greenland ice sheet have much in common. All three are reported in the news as sudden events but are actually visible tipping points that result from slowly accumulated pressure in the system – be it the gradual build-up of political protest in Eastern Europe, the build-up of sub-prime mortgages in a bank’s asset portfolio, or the build-up of greenhouse gases in the atmosphere. As Donella Meadows, one of the early champions of systems thinking, put it, ‘Let’s face it, the universe is messy. It is nonlinear, turbulent, and chaotic. It is dynamic. It spends its time in transient behaviour on its way to somewhere else, not in mathematically neat equilibria. It self-organises and evolves. It creates diversity, not uniformity. That’s what makes the world interesting, that’s what makes it beautiful, and that’s what makes it work.’ 12
Complexity in economics

The realisation that economics needs to embrace dynamic analysis is by no means a recent one. Over the past 150 years, economists of all stripes tried to break away from imitating Newtonian physics, but their efforts were all too often steam-rolled by the dominance of equilibrium theory and its satisfyingly neat equations. Jevons himself had a hunch that economic analysis should be dynamic but, lacking the mathematics to do it, he settled for comparative statics, which compares snapshots of two points in time: it was an unfortunate compromise because it led him away from, rather than towards, the insight he ultimately sought. 13 In the 1860s, Karl Marx described how the relative income shares of workers and capitalists would continually rise and fall, due to self-perpetuating cycles of output and employment. 14 By the end of the nineteenth century, Thorstein Veblen was criticising economics for being ‘helplessly behind the times in not being evolutionary’ and therefore unable to explain change or development, 15 while Alfred Marshall argued against mechanical metaphors and, instead, for seeing economics as ‘a branch of biology, broadly interpreted’. 16
Twentieth-century attempts to recognise the economy’s inherent dynamism were likewise made by deeply opposing schools of thought but even they couldn’t dislodge equilibrium thinking. In the 1920s John Maynard Keynes critiqued the use of comparative statics, pointing out that it is precisely what happens in between those snapshots of economic events that is of greatest interest. ‘Economists set themselves too easy, too useless a task,’ he wrote, ‘if in tempestuous seasons they can only tell us that when the storm is long past the ocean is flat again.’ 17 In the 1940s, Joseph Schumpeter drew on Marx’s insights into dynamism to describe how capitalism’s inherent process of ‘creative destruction’, through continual waves of innovation and decline, gave rise to business cycles. 18 In the 1950s, Bill Phillips created his MONIAC precisely with the aim of replacing comparative statics with system dynamics, complete with the time lags and fluctuations that can be observed as water flows into and out of tanks. In the 1960s Joan Robinson lambasted equilibrium economic thinking, insisting that, ‘a model applicable to actual history has to be capable of getting out of equilibrium; indeed it must normally not be in it’. 19 And in the 1970s, the father of neoliberalism, Friedrich Hayek, decried the economist’s ‘propensity to imitate as closely as possible the procedures of the brilliantly successful physical sciences – an attempt which in our field may lead to outright error’. 20
So let’s finally heed their collective advice, push equilibrium thinking to one side, and start to think in systems instead. Imagine pulling the iconic supply and demand curves out of their rigid criss-cross and twisting them into a pair of feedback loops. At the same time, drop the economist’s beloved notion of ‘externalities’, those incidental effects felt by people who were not involved in the transactions that produced them – like toxic effluent that affects communities living downstream of a river-polluting factory, or the exhaust fumes inhaled by cyclists biking through city traffic. Such negative externalities, remarks the ecological economist Herman Daly, are those things that ‘we classify as “external” costs for no better reason than because we have made no provision for them in our economic theories’. 21 The systems dynamics expert John Sterman concurs. ‘There are no side effects – just effects ,’ he says, pointing out that the very notion of side effects is just ‘a sign that the boundaries of our mental models are too narrow, our time horizons too short’. 22 Due to the scale and interconnectedness of the global economy, many economic effects that were treated as ‘externalities’ in twentieth-century theory have turned into defining social and ecological crises in the twenty-first century. Far from remaining a peripheral concern ‘outside’ of economic activity, addressing these effects is of critical concern for creating an economy that enables us all to thrive.
From this vantage point – counter-intuitive though it may sound – equilibrium economics actually turns out to be a form of systems analysis, just an extremely limited one. It get the results it seeks by imposing severely restrictive assumptions about how market systems behave – assumptions including perfect competition, diminishing returns, full information, and rational actors – so that no errant effects get in the way of the price mechanism’s ability to act as the balancing feedback loop that restores market equilibrium. Think of it in terms of starlings: what restrictions would you have to impose on a large flock of these birds if you wanted to make sure that they all stayed still? You could place each bird in its own narrow little coop and shut them all away in a dark, quiet room: that should encourage them to stay put. But don’t expect the flock to behave like that once you remove these unnatural confines and release them into the air. They will twist and turn, putting on an extraordinary aerial display of a complex system in action. So it is with economic actors trapped in the narrow confines of an equilibrium model: when all the restrictive assumptions are in place, they will indeed behave as required. But remove those assumptions – enter the real world – and all havoc could break loose. It often does, of course, in the boom-to-bust of financial crash, in the rise of the 1%, and in the tipping points of climate change.
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.
Avoiding collapse

A systems perspective makes clear that the prevailing direction of global economic development is caught in the twin dynamics of growing social inequality and deepening ecological degradation. To put it bluntly, these trends echo the conditions under which earlier civilisations – from the Easter Islanders to the Greenland Norse – have collapsed. When a society starts to destroy the resource base on which it depends, argues the environmental historian Jared Diamond, it is going to be far less adept at changing its ways if it is also stratified, with a small elite that is quite separate from the masses. And when the short-term interests of that decision-making elite diverge from the long-term interests of society as a whole it is, he warns, ‘a blueprint for trouble’. 42 Instances of collapse are sometimes assumed to be rare aberrations along the path of human progress, but they have been surprisingly common. Indeed, the breakdown of civilisations ranging from the Roman Empire and China’s Han Dynasty to the Mayan civilisation of Mesoamerica makes clear that even complex and inventive civilisations are vulnerable to downfall. 43 So can systems thinking help us to discover whether it might happen again?
That question was most famously explored in the 1972 study Limits to Growth , whose team of authors based at MIT created one of the first dynamic computer models of the global economy, known as World 3. The team’s aim was to explore a range of economic scenarios up to 2100, taking account of five factors that they saw as determining – and ultimately limiting – output growth: population, agricultural production, natural resources, industrial production, and pollution. According to their projections for the business-as-usual scenario, as global population and output expanded, non-renewable resources like oil, minerals and metals would be depleted, leading to a drop in industrial output and food production, ultimately resulting in famine, a large fall in the human population, and greatly reduced living standards for all. When launched, their analysis simultaneously raised the alarm about the state of the world, introduced systems thinking widely into policy debates, and caused an uproar amongst those committed to the goal of growth. 44
Mainstream economists were quick to deride the model’s design on the basis that it underplayed the balancing feedback of the price mechanism in markets. If non-renewable resources became scarce, they argued, their prices would rise, triggering greater efficiency in their use, the wider use of substitutes, and exploration for new sources. But in dismissing World 3 and its implied limits to growth, they too quickly dismissed the role and effects of what the 1970s model simply called ‘pollution’, which – unlike metals, minerals and fossil fuels – typically carries no price and so generates no direct market feedback. World 3’s modelling of pollution turned out to be prescient: today we can name it in far more specific terms as the many forms of ecological degradation that put pressure on planetary boundaries, from climate change and chemical pollution to ocean acidification and biodiversity loss. What’s more, recent data comparisons with the 1972 model find that the global economy appears to be closely tracking its business-as-usual scenario – and that doesn’t end well. 45
This should set the alarm bells ringing: in the early twenty-first century, we have transgressed at least four planetary boundaries, billions of people still face extreme deprivation, and the richest 1% own half of the world’s financial wealth. These are ideal conditions for driving ourselves towards collapse. If we are to avoid such a fate for our global civilisation, we clearly need a transformation and it can be summed up like this:
Today’s economy is divisive and degenerative by default.
Tomorrow’s economy must be distributive and regenerative by design.
An economy that is distributive by design is one whose dynamics tend to disperse and circulate value as it is created, rather than concentrating it in ever-fewer hands. An economy that is regenerative by design is one in which people become full participants in regenerating Earth’s life-giving cycles so that we thrive within planetary boundaries. This is our generational design challenge, and its possibilities are explored in Chapters 5 and 6 . But what kind of systems-thinking economist can help to make it happen?
Goodbye spanner, hello secateurs

Thinking in systems transforms the way we view the economy and invites economists to drop off their old metaphorical baggage. Say farewell to economy-as-machine and embrace economy-as-organism. Let go of the imaginary controls that promised to pull markets into equilibrium and, instead, get a feel for the pulse of the feedback loops that keep them continually evolving. It is time for economists to make a metaphorical career change, too: discard the engineer’s hard hat and spanner, and pick up some gardening gloves and secateurs instead.
It’s a vocational shift that has been a long time coming: back in the 1970s, Friedrich Hayek himself suggested that economists should aim to be less like craftsmen shaping their handiwork and more like gardeners tending their plants. Yes, the metaphor may have come from a thinker with extreme laissez-faire leanings but, if anything, it suggests that Hayek never did a hard day’s work in the garden: as any true plantsman knows, gardening is far from laissez-faire. In their book The Gardens of Democracy , Eric Liu and Nick Hanauer argue that moving from ‘machinebrain’ to ‘gardenbrain’ thinking calls for a simultaneous shift away from believing that things will self-regulate to realising that things need stewarding. ‘To be a gardener is not to let nature take its course; it is to tend ,’ they write, ‘Gardeners don’t make plants grow but they do create conditions where plants can thrive and they do make judgments about what should and shouldn’t be in the garden.’ 46 That is why economic gardeners must get stuck in, nurturing, selecting, repotting, grafting, pruning and weeding the plants as they grow and mature.
Economists need a metaphorical career change: from engineer to gardener (as demonstrated by Charlie Chaplin and Josephine Baker).
One approach to economic gardening is to embrace evolution. Rather than aiming to predict and control the economy’s behaviour, says Eric Beinhocker, a leading thinker in this field, economists should ‘think of policy as an adapting portfolio of experiments that helps to shape the evolution of the economy and society over time’. It’s an approach that aims to mimic the process of natural selection, often summed up as ‘diversify–select–amplify’. Set up small-scale policy experiments to test out a variety of interventions, put a stop to the ones that don’t work well, and scale up those that do. 47 This kind of adaptive policymaking is crucial in the face of today’s ecological and social challenges because, as Elinor Ostrom put it, ‘We have never had to deal with problems of the scale facing today’s globally interconnected society. No one knows for sure what will work, so it is important to build a system that can evolve and adapt rapidly.’ 48
This has empowering implications: if complex systems evolve through their innovations and deviations then that gives added importance to novel initiatives, from new business models to complementary currencies and open-source design. Far from being mere fringe activities, these experiments are at the cutting edge – or rather, the evolving edge – of economic transformation towards the distributive and regenerative dynamics that we need.
If the economy is constantly evolving, how best can we steward its process? Learn to find the ‘leverage points’, said Donella Meadows – those places in a complex system where making a small change in one thing can lead to a big change in everything. She believed that most economists spend too much time tweaking low leverage points such as adjusting prices (which merely alters the rate of flow), when they could have far greater leverage through rebalancing the economy’s feedback loops, or even by changing its goal (she had little time, remember, for that cuckoo goal of GDP growth). In addition, instead of jumping straight in with plans for change, she advised, be humble and try to get the beat of the system, even if it is an ailing economy, a dying forest, or a broken community. Watch and understand how it currently works and learn its history. It’s obvious to ask what’s wrong, so also ask: how did we get here, where are we headed, and what is still working well? ‘Don’t be an unthinking intervenor and destroy the system’s own self-maintenance capacities,’ she warned. ‘Before you charge in to make things better, pay attention to the value of what’s already there.’ 49
Meadows was a skilled economic gardener in this sense, having spent much of her life watching the dance of social-ecological systems in action, and observing the value of what was already there. In fact, she noted, effective systems tend to have three properties – healthy hierarchy, self-organisation and resilience – and so should be stewarded to enable these characteristics to emerge.
First, healthy hierarchy is achieved when nested systems serve the greater whole of which they are a part. Liver cells serve the liver, which in turn serves the human body; if those cells start to multiply rapidly, they become a cancer, no longer serving but destroying the body on which they depend. In economic terms, healthy hierarchy means, for example, ensuring that the financial sector is in service to the productive economy, which in turn is in service to life. 50
Second, self-organisation is born out of a system’s capacity to make its own structures more complex, like a dividing cell, a growing social movement, or an expanding city. In the economy much self-organising goes on in the marketplace through the price mechanism – that was Adam Smith’s insight – but it also takes place in the commons and in the household too – the insight of Elinor Ostrom and generations of feminist economists. All three of these realms of provisioning can self-organise effectively to meet people’s wants and needs, and the state should support all three in doing so.
Lastly, resilience emerges out of a system’s ability to endure and bounce back from stress, like a jelly that wobbles on a plate without losing its form, or a spider’s web that survives a storm. Equilibrium economics became fixated on maximising efficiency and so overlooked the vulnerability that it can bring, as we will see in the next chapter. Building diversity and redundancy into economic structures enhances the economy’s resilience, making it far more effective in adapting to future shocks and pressures.
Getting ethical

There is one further important consequence of recognising the economy’s inherent complexity and it concerns the ethics of economic policymaking. Ethics are at the core of other professions, such as medicine, that combine the uncertainty inherent in intervening in a complex system (like the human body) with having responsibility for significant impacts upon other people’s lives. Hippocrates, the father of medicine, inspired a set of ethical principles, summed up in the modern Hippocratic Oath, that still guide doctors today, including: first do no harm; prioritise the patient; treat the whole person, not just the symptom; obtain prior informed consent; and call on the expertise of others when needed.
Xenophon, the father of economics, conceived of household management as a domestic affair and so suggested no ethics to guide it (since he believed he already knew how to manage women and slaves). But economics now guides the management of nations and of our planetary household, profoundly influencing the lives of us all. Is it, then, time for economists to get serious about ethics? George DeMartino, economist and ethicist at the University of Denver, certainly thinks so. ‘When a profession seeks influence over others, it necessarily takes on ethical obligations – whether it recognizes them or not,’ he argues, bluntly adding, ‘I’m aware of no other profession that has been so cavalier regarding its responsibilities.’ 51
DeMartino believes that economic policy advisers too often follow what he calls the ‘maxi-max’ rule: when considering all possible policy options, recommend the one that would work best if it worked – without fully assessing whether it is likely to work. ‘Maxi-max has been the primary decision rule in the most important economic interventions over the past 30 years,’ he argues, pointing to the damage wrought by the shock policies of privatisation and market liberalisation implemented in Latin America, sub-Saharan Africa, and the former Soviet Union during the 1980s and 1990s. 52
Economics is more than two thousand years behind medicine in honing the ethics of its own profession. That’s quite some catching up to do, so to get the ball rolling – and with inspiration from DeMartino – here are four ethical principles for the twenty-first-century economist to consider. First, act in service to human prosperity in a flourishing web of life, recognising all that it depends upon. Second, respect autonomy in the communities that you serve by ensuring their engagement and consent, while remaining ever aware of the inequalities and differences that may lie within them. Third, be prudential in policymaking, seeking to minimise the risk of harm – especially to the most vulnerable – in the face of uncertainty. Lastly, work with humility , by making transparent the assumptions and shortcomings of your models, and by recognising alternative economic perspectives and tools. Principles such as these may one day be included in an Economist’s Oath, to be recited by aspiring professionals upon graduation. But with or without the ceremony, what matters most is to bring such ethical principles to life in every economics student’s training and every policymaker’s practice.

‘The future can’t be predicted,’ wrote Donella Meadows, ‘but it can be envisioned and brought lovingly into being. Systems can’t be controlled, but they can be designed and redesigned … We can listen to what the system tells us, and discover how its properties and our values can work together to bring forth something much better than can ever be produced by our will alone.’ 53
If the global economy’s current dynamics continue – with their divisive and degenerative effects – then we face the very real risk of heading towards collapse. This overriding generational challenge calls on the twenty-first-century economist to embrace complexity and draw on its insights in order to transform economies – local to global – to make them distributive and regenerative by design, as the following chapters explore. If he were alive today I bet that Newton, apple in hand, would be up for the task.