Question:

THE PHYSICS OF GETTING FIRED During my final year at Oxford, the hot career for aspiring young graduates was a job in management consultancy. Entering the university careers service on Banbury Road, it felt like every other shiny brochure was for an elite consulting firm, promising intellectual stimulation, glamorous travel and the opportunity to work with interesting companies. It all sounded great. But I had no idea what management consultants actually did. There’s an old joke that consultants borrow your watch so they can tell you the time – and then charge you for it. After a bit of research, I discovered that management consultants advise companies and other organisations on how to tackle any number of strategic and operational challenges that they themselves don’t have the time, experience or perspective to solve. The marketing brochures explained that the best consultants are exceptionally structured thinkers, able to take a swirl of disconnected information and make sense of it. They are also highly curious, always asking questions and challenging the status quo. In many ways, it felt like the perfect career for a former student of physics. For centuries, physicists have prided themselves on exactly these skills. Physicists stared at the skies and wondered: ‘Why are we here?’ ‘Why is there something and not nothing?’ ‘Why did that just happen?’ And over countless years, they slowly learned how to harness that curiosity and direct it towards understanding the world around us in a structured and systematic way. From the ancient Greeks to Galileo and his telescope, this process became known as the scientific method, a technique for learning about the world in a systematic, replicable way. To this day, the scientific method remains the most fundamental approach used by physicists – by all scientists, in fact. There are six basic steps to the scientific method. Step 1: Make an observation. For example, the sky is blue. Step 2: Ask a question. Why is the sky blue? Step 3: Form a hypothesis. The sky is blue because of the scattering of electromagnetic radiation, such as light, by air molecules. Step 4: Make a prediction based on the hypothesis. If scattering is the reason why the sky is blue, then the colour of the sky should change if we observe it at different altitudes and times of day. Step 5: Test the prediction. When we observe the sky at different altitudes and times of day, we see that the sky appears darker blue at higher altitudes and at sunrise and sunset. Step 6. Iterate. We could observe the sky from different locations on Earth, or we could observe the sky from space. The scientific method provides a structure for revealing the mysteries of the physical world. But I have found that it can also help make sense of the world beyond physics. Consulting companies are fond of setting prospective employees challenging case study interview questions. In one such interview, I remember being asked how I might help a large multinational company that was dealing with a staff retention issue. I knew nothing about the company or their specific issues, but the scientific method provided a useful structure for answering the question. I started with an observation: The human resources team had noticed a recent increase in resignations. Ask a question: Why were so many people leaving? Form a hypothesis: The company’s compensation is not competitive. Make a prediction: If we increase employee bonus and overtime pay structures, then fewer resignations will occur. Now test it: Let’s say that for the next three months, we will track resignations and a third party will conduct exit interviews. And finally iterate: Communicate the findings and re-evaluate compensation and benefits. The real world is of course far more complicated than an interview case study. In the example above, one might need to develop a number of hypotheses and test each. Some predictions will be accurate and some false. The accurate predictions help you narrow down on the right solution and eventual course of action. Nevertheless, I found these six simple steps an invaluable guide. And after several of these tough interviews, I eventually landed a job with a consulting firm in London. Right from the start I was thrown into a gruelling schedule, regularly working late into the night. I was still living with my parents at the time, and my poor mother could never get to sleep until I got home from the office, which was often not until well after midnight. Still, I gave everything to this job. It was my first full-time job in the real world and I felt like I was flying. At my first performance evaluation, a manager told me I had ‘partner potential’. The future felt like mine to seize. Nine months in, I was sent to Italy with my fellow graduate hires for mandatory company training. While there, I got an unexpected call to say that on Monday of the following week, I would need to attend an early morning meeting with a partner back in London to discuss potential ‘reductions in force’. I had never heard this expression before, but I was told that it was essentially corporate speak for lay-offs. The firm was going to have to make a certain proportion of its staff redundant and on Monday morning I would be told whether I would be one of them. Despite being a notorious over-worrier, uncharacteristically, I took the phone call in my stride. Every single one of my colleagues had received a similar call and, at the time, I was convinced it would be one of them that was let go. It surely couldn’t affect me. I’d be fine, precisely because I’d been working so hard, putting in so much effort, and getting such good feedback and such good performance reviews. When Monday morning came, I was called into an office on the first floor of the building, several levels down from where everyone normally worked. Sitting across the table was a very large, very senior American partner with a serious face. On his left was a woman from Human Resources. I’d never spoken to the partner before, but I knew him as the Burger King because he would enjoy a Whopper Meal in his office every day for lunch. He got to the point straight away: ‘I’m sorry, Zahaan, your services are no longer required at the firm.’ I don’t remember exactly what else he or the HR manager said after that. There was a buzzing in my ears. I felt numb, almost like I was going to faint. I do remember him telling me that I shouldn’t even clear my desk. It became apparent that the reason why the meeting had taken place on the first floor was because the company wanted me to take the single flight of stairs down to the ground floor, without seeing or speaking to anyone, then leave and go home. It was as abrupt as that. And that was my first experience of the working world. * * * Getting fired is tough for anyone, but I think for me it was particularly painful because the way events had unfolded was in complete opposition to the deterministic world that I longed to live in as a teenage boy – a world that was orderly, predictable and fair. I had worked hard as a consultant because I expected my maximal effort to translate into optimal outcomes. It was hard to make sense of what had happened. Of course, my employer was not run by soulless automatons who delighted in toying with naive young graduates, only to crush them just as it seemed the world was within their grasp. At least, I hope not. It’s more likely that they were guided by the bottom line, and what had happened to me made perfect business sense. There was a broader context behind why I lost my job. And once the dust had settled and the shock had worn off, I could begin to see it. It was the summer of 2001. The world was yet to change quite as irrevocably as it would in September later that year, but still, all was not well. The dot-com bubble had burst a year earlier, sending financial shock waves around the globe. This came at the end of a period of overconfidence and turbocharged investment in just about any company with a dot-com website – then a new, glittering and sexy concept. But it had failed quite spectacularly to pay off. Both investors and entrepreneurs found out the hard way that a dot-com URL alone was not sufficient to guarantee a company’s performance. It turned out that more fundamental factors – such as a viable business plan, adequate capital, sufficient financial returns to cover costs and, above all, basic competence of the management – still had a part to play. It was not just the dot-com companies themselves that went bust. The shock was passed on to related businesses – companies in the consulting, banking and tech sectors that supported them, and which suddenly found themselves with fewer paying customers. The dot-coms were ground zero for the crisis, but the waves radiated outwards throughout the world of finance. Belts had to be tightened, budgets had to be cut, and workforces had to be trimmed. The harsh HR rule in such circumstances tends to be last in, first out. I was not the first young graduate to be fired abruptly. Nor was I the last. With hindsight, and possibly even some foresight, I should have seen it coming. With a little more detachment I would have been aware of the bigger picture. That, after all, is one reason management consultants are hired in the first place. Detachment, however, is sometimes hard to find. It can be because you, the main character of your story, are simply too immersed in your own narrative. It can be a wilful blindness, or an inability to conceive that things aren’t working out as you would prefer they would. The fact is that, time and time again, crises like that seem to build up unnoticed, until suddenly they burst, to the surprise of everyone. The dot com bust was not the first nor the last time this happened. In 2008, there was the subprime mortgage crisis. In the United States, irresponsible lending on risky mortgages to those who simply couldn’t afford it had led to a housing bubble, which finally and inevitably burst. It began with poor homeowners in a few key states but the shock was felt all around the world. In 2011, several members of the eurozone – those countries that use the euro as their principal currency – found themselves unable to repay their government debt. One government going broke is bad enough; five in quick succession, all from one of the wealthiest areas of the world, is calamitous. The countries had been accumulating debt for a variety of reasons, and the expected inward flow of capital that would let them pay this off had dried up. Each country would have had more latitude to handle the crisis on their own terms if they still had their own individual currency, but being pegged to one shared currency both severely limited their options, and increased the knock-on effect into other countries. In 2013, the US Federal Reserve had been spending $85 billion a month to buy US Treasury bonds. This policy, known as quantitative easing, was initiated after the subprime mortgage crisis with the goal of helping to keep US interest rates low. This prompted investors to seek higher returns beyond the US, especially in emerging markets like India, helping to bolster the strength of the rupee. But when, in May 2013, the then chairman of the Federal Reserve, Ben Bernanke, hinted that the US government would start to taper its bond-buying programme, India suddenly found its inward capital flows drying up. Three months later, the rupee hit an all-time low against the US dollar. In 2015, turbulence on the Chinese stock market was making waves around the world. About 30 per cent was knocked off the price of Chinese shares over a three-week period. So many investors were selling their shares that hundreds of Chinese companies had to suspend their share dealings. Paradoxically, just a few weeks earlier, Chinese stock markets had soared to a seven-year peak as borrowing costs fell and private investors piled in – and therein lay the problem. The stock market high was driven more by momentum than by the actual value of the market and there was nothing to keep it at its peak. Those private investors were borrowing heavily to raise money, and as the value of stocks (inevitably) fell, the lenders were requiring more cash or other collateral in return. Then 2017 saw India’s attempts at financial reform score a significant own goal for the country’s domestic economy, with serious knock-on effects around the globe once again. Still reeling from the after-effects of 2013, the Indian government announced out of the blue that all 500 and 1,000 rupee notes would cease to be legal tender. Put another way, 86 per cent of all currency then in circulation was suddenly declared null and void. The stated goals of this action were sound, and included forcing tax dodging cash hoarders out into the open, eliminating fake banknotes in circulation, and helping the country prepare for digital currency. However, the rural economy, which relied primarily on cash, was wrecked overnight. Prices of staples like onions, tomatoes and potatoes halved compared to what they had been a year earlier. Profits plunged, debt grew and, as we should now be becoming accustomed to, a slump in one sector led to downturns elsewhere. In the first few months of 2017 around 1.5 million people lost their jobs. The point here is not to have fun highlighting how clever financial schemes have backfired, or how experts missed what hindsight shows to have been obvious. The point is rather to show how sometimes just one small failure in one small area can spark off a cascade of failure across something much larger, leading to markets crashing, businesses crumbling and hopeful young graduates losing their jobs. Can physics help make sense of what’s happening? To answer this question we have to go back to the very first physics I learned as a teenager. * * * Isaac Newton’s laws of motion provided the foundations for modern physics. They proved immensely successful in predicting and explaining the behaviour of objects in our everyday world. If force A is applied to object B then we can know precisely the resulting effect C. They represented a deterministic universe that is predictable and certain. However, this certainty was not to last. Physicists began to realise that making such predictions was not always so straightforward. Imagine two objects, say a single planet orbiting a star. Using Newton’s laws, we can precisely model the behaviour of both the planet and the star over time, pretty much indefinitely. Now imagine introducing a third object, say a moon. You might think, no big deal. It’s only three objects; surely the same laws of physics that can launch rockets into space could still predict the motion and behaviour of all three? It turns out they can’t. The addition of this third object leads to immense complexity – so much complexity, in fact, that making predictions over time about the gravitational interactions between the objects becomes exceedingly hard, if not impossible. Imagine all three objects starting in known, fixed positions. After a certain time they will all move and end up in new positions. Now imagine doing the exact same thing again, repositioning the same objects in the same starting positions. After the same time has elapsed, you might expect the objects to end up in the same place as before, but in reality they could be somewhere very different. You could repeat this over and over again, each time starting in the same position, but each time seeing different end positions. During the late 19th and early 20th centuries, French mathematician Henri Poincaré took on this so-called ‘three-body problem’. His groundbreaking work revealed why making predictions was so hard. It’s because even in deterministic systems, the tiniest perturbations in initial conditions can lead to vastly different outcomes over time. It’s the same phenomenon that years later would become known as the ‘butterfly effect’; a butterfly that flaps its wings in China could cause a hurricane in Texas, a metaphor for how a minuscule change here can set off a chain of events that leads to a dramatic impact there. Poincaré’s work represented a turning point for physics. It forced a shift in focus from deterministic, well-ordered systems to those characterised by complexity and chaos. Modern physicists have sought to develop models that illustrate this phenomenon, providing a bridge between Newtonian predictability and the inherent instability of complex systems. One such individual was the Danish physicist Per Bak. In the 1980s, he was employed at Brookhaven National Laboratory, a US Department of Energy facility based in upstate New York. His research focused on the physics of complex systems and how small events can trigger major events. In 1987, he teamed up with two postdoctoral researchers, Chao Tang and Kurt Wiesenfeld, to publish a paper that would revolutionise the thinking about such phenomena. Their idea was called self-organised criticality,1 and their surprising inspiration was grains of sand. To illustrate his ideas, let’s consider two simple experiments involving sand. Imagine going to the beach and collecting a handful of sand. The sand is dry and you’re able to very carefully pick out individual grains with a pair of tweezers and then weigh them, one by one. It would, of course, be a very tedious process, but if you had an adequately sensitive set of scales and were able to individually weigh, say, 500 individual grains of sand, what results would you expect? If you were to plot a graph showing the various weights recorded on the horizontal axis and the frequency of that weight being recorded on the vertical axis, you would almost certainly see a peak in the middle with the curve gradually tapering off on either side. In other words, most grains of sand would have a mass at or around the mean (which happens to be about 150 micrograms) while some would be heavier and some lighter. The heavier or lighter the grain of sand, the less common they will likely become. This shape is known as a bell curve, or a ‘normal distribution’, and it’s incredibly common in society and nature. Whether you’re measuring the average birth weight of babies in a hospital or scores in standardised tests or the average number of people taking the Tube on any given Monday in London or the sizes of leaves on a tree, you will invariably find a bell-curve distribution. As long as the things being measured are independent and not interacting with each other, you will always see the same shape. Now let’s take our grains of sand and do a different experiment. Picture a large table in the centre of a room. Imagine dropping grains of sand onto the centre of the table. One by one the grains fall and a larger and larger pile will slowly build up. Eventually the pile will get so big that there will be a small ‘avalanche’ and some portion of the sand will slide down the pile. There may be several of these small avalanches, but if you keep dropping grains of sand on top of the pile you may eventually see a much larger avalanche where a significant portion of the pile will collapse. If you were to draw another graph, plotting the size of avalanches on one axis and their frequency on another, what shape would you expect? You may expect another bell-curve, but you would be wrong. The actual graph would more closely resemble a children’s slide at a play park. Starting from the left, you would see a high peak representing the small, frequent avalanches. Moving to the right, this peak would rapidly decline, forming a long flat tail extending far to the right. What such a graph tells us is that the vast majority of avalanches would be very small, but on rare occasions, you would see an extremely large one. This curve is known as a ‘long tail’ and this pattern is also common in nature, although in very different circumstances. The severity and frequency, for instance, of forest fires, earthquakes, floods and epidemics all follow similar long-tail patterns. In other words, most are small, but very rarely they can be catastrophically large. Wars also follow a long-tail distribution. Most are short-lived and have relatively few casualties, but very rarely, wars can lead to the deaths of millions. These two shapes – the bell curve and the long tail – are all around us and it’s easy to take them for granted. But they describe two very different things. Scott E. Page, a Professor of Complexity, Social Science and Management at the University of Michigan compares the two by considering the average height of human beings around the world. This currently looks like a bell curve with the mean height of a grown man globally being around 5 foot 9 inches. There are some shorter men (like me) and some taller men, but you very rarely see men shorter than 3 foot or taller than 7 foot. Page imagines what would happen if the mean height of men remained 5 foot 9 inches, but the distribution followed a long tail: 60,000 people would be over 9 foot tall, 10,000 people would be over 17 foot tall, and one person would be over 1,000 foot tall. And to balance out all these tall people, 170 million men would be a Lilliputian 7 inches tall. With such dramatic differences between bell-curve and long-tail graphs, the next logical question is why? Why do we sometimes see a bell curve and sometimes see a long tail? Is there a model that can help explain this? This was exactly the question asked by Bak, Tang and Wiesenfeld. And to find their answer, they delved deeper into the behaviour of sandpiles. Consider the same table as before, with the pile of sand on it, but this time the surface is covered with a large grid, much like the squares on a chessboard. Now imagine dropping individual grains of sand onto the centre of the table. For the purpose of this model, each square on the grid can only hold a limited number of grains of sand, a maximum of just three. Once a fourth is added, the square becomes ‘full’. At this point, there’s a little avalanche and a grain of sand falls into each of the neighbouring squares. One grain goes to the square above, one goes below, one goes to the left and one goes to the right. As the grains of sand continue to fall, more and more squares will get loaded up with grains of sand. At the start, an avalanche might travel only two or three squares and then stop. But when every cell is loaded, one extra tiny grain of sand could send a larger wave, an avalanche, across the whole board. Bak, Tang and Wiesenfeld were not able to carry out this experiment using actual sand. It turns out even dry sand is too sticky. So they had to make do with a computer model that built up a virtual pile, grain by grain, and coloured in the piles for them. Relatively flat and stable areas were coloured green, and the steeper sections were coloured red. As you would expect, the pile was at first shaded green throughout. As it grew, red areas began to emerge. Some of the red piles developed on their own, isolated from others. In other cases, a cluster of red piles developed next to each other. When a grain of sand landed on an isolated pile, the avalanche was likely to be a one-off and the pile would quickly return to green. If, however, there were other red areas nearby, then an additional grain could set off a chain reaction of further avalanches in other red areas. Crucially though, they found that predicting the actual size or precise location of the next avalanche was impossible. The pattern of avalanches was not neat or orderly. Ripples spreading out from the centre of the pile could head off in any direction. Grains of sand seemed to tumble at random. Sometimes nothing happened, sometimes a few other grains tumbled beside it, and sometimes a fresh avalanche would be triggered. Bak, Tang and Wiesenfeld could not say which grain would cause that avalanche, and they could not say how large the avalanche would be. Avalanches had nothing to do with the size of the sandpile or the number of grains. Identical inputs and conditions led to a different outcome every time. Jordan Ellenberg, a professor of mathematics at the University of Wisconsin-Madison, went so far as to describe a sandpile as almost seeming to be alive. The grains of sand seem to have an agency of their own, speeding up and slowing down as if being acted upon by some external force. The only pattern was that there was no pattern. Yet, despite this seeming unpredictability, an order of a sort did emerge. Bak, Tang and Wiesenfeld paid particular attention to the respective sizes of the avalanches. Most were small. Fewer were mid-sized. And a rare number were extreme. In other words, the distribution of avalanche sizes followed the long tail. The power and beauty of Bak, Tang and Wiesenfeld’s sandpile model was that it revealed the underlying processes that led to other long-tail distributions. Whether dealing with earthquakes or forest fires or epidemics, the same thing is happening. A system operates as it should. But eventually, a point known as self-organised criticality is reached. It’s at this moment when just the smallest change – an apparently insignificant event like one extra grain of sand – can lead to an avalanche, the size and consequences of which are unpredictable. The system could absorb the avalanche and return to stability. Or it could lead to a catastrophic cascade of further avalanches. Complex systems self-organise and build up the potential for their own destruction. Dr Ted Lewis, a former executive director of the Center for Homeland Defense and Security, cites the 2008 financial meltdown mentioned earlier as a classic example of this sandpile phenomenon. It was years in the making as homeowners ended up owing more money on their homes than they were worth. In the US housing market, the carrying capacity, or the maximum risk the market could bear, is normally approximately 62 per cent. By the year 2008, this figure was 65 per cent, representing an enormous tension. The critical point was exceeded and the collapse occurred without warning. It just took one small incident – a failure in one savings and loan company in southern California, which normally would have passed unnoticed by almost everyone – to precipitate a major avalanche. Some scientists see sandpiles as a way of explaining complex biological behaviour. For example, if you have ever watched a murmuration of starlings – one of the wonders of nature – then you have seen it in action. A cloud of thousands of birds, wheeling together in perfect formation, twisting and writhing through the sky and forming the most fantastic abstract shapes. Yet, the birds are not communicating in any way. There is nothing calculated or pre-planned, and it would be impossible to predict the flight pattern of any one starling. No one starling would be able to take control and direct the outcome. Every murmuration is unique, but the mathematical rules behind it are very simple. The shifting cloud of birds is like a sandpile avalanche – unpredictable and only occurring when a critical point has been exceeded, followed by a subsequent build-up of tension that once again reaches the critical point and another avalanche (or direction change) occurs. Even political movements are subject to the sandpile effect. For instance, on 4 January 2011 a street vendor in Egypt of whom hardly anyone had ever heard, Tarek el-Tayeb Mohamed Bouazizi, publicly doused himself with petrol on a busy street and set himself alight following a run-in with corrupt authorities. Normally this wouldn’t have left a ripple in world affairs. However, at the time this happened, resentment against the totalitarian behaviour of many authoritarian regimes in North Africa had built up to the point where Bouazizi’s self-immolation led to massive upheaval across the region. This was the Arab Spring. If Bouazizi had taken his own life, say, one year earlier, then probably no one beyond his family and friends would have been affected by his death. The sandpile model reveals that long-tail distributions are a feature of organisations that are interconnected and interdependent. Tension builds up until self-organised criticality is reached and, at this point, the smallest change can lead to much larger cascades. So is there a way to prevent such catastrophes? For all the recently redundant graduates out there – or those with more foresight than me – the sandpile model offers little comfort when trying to protect specific job losses. Individual employees are best compared to the individual grains, buffeted by shifting sandpiles beyond their control. We can never know or predict the size or location of the next avalanche. Companies and markets on the other hand, more akin to the larger piles of sand, do have more agency. They can take preventative measures simply by the way they operate. One might think that these measures would involve somehow working smarter – optimising and improving efficiency to prevent future collapses. But in fact the opposite is true. The best way to prevent catastrophes is to prevent the build-up of risk in the first place. And Bak’s radical idea was that the way to do that was to become less efficient. Consider a power network. If every household in a city drew exactly the same electrical current, all the time, then the electrical system would be much simpler. Demand would always be predictable and there would be very few outages. This, however, is rarely the case. Perhaps everyone switches on the air conditioning in hot weather. Perhaps millions of people switch on the kettle during the World Cup half-time interval. Or perhaps lightning hits a mains box. In any of these cases, and many others besides, there will be a surge of power, and, if the system does not have inbuilt protection, then across the land, fuses will trip. In this case, the power grid may fail due to inadequate transmission capacity, inadequate fuel for generators, or simply a broken connection. Like grains of sand falling onto a pile, an avalanche of collapsing power may spread like an epidemic throughout the entire grid. The Lebanese-American essayist and mathematical statistician Nassim Taleb describes events like these as ‘black swans’. Swans across most of the world are white. The only native population of black swans is in Australia. So, until the precise moment when European explorers reached Australia and saw a black swan, there could have been no inkling outside Australia that such birds existed. The first explorer to see one could have had no way of guessing that a black swan lay just around the corner – until they saw it. In modern parlance, a black swan is something significant that could happen, but we have no idea of knowing where, or when, or how, or even if it actually will. Until it does. Black swan events occur far more widely than just in power systems. Even though these unforeseen events can’t by definition be pinpointed in advance, they can be catered for. And the counter-intuitive answer is to actually reduce efficiency – to optimise by not optimising – leaving enough slack or redundancy within a system to deal with the unexpected event. A power system can be quite easily protected against surges. One way would be to have plenty of extra cabling and fuse protection, which most of the time will sit idle until the surge strikes. Another way would be to run the entire power network at suboptimal performance, always leaving a little extra capacity for the surges, instead of maximising short-term profit by running it as tightly and within as closely defined parameters as possible. Such solutions are obviously easier said than done. We live in a just-in time world. Modern organisations strive to increase their efficiency and optimise their operations by sweating their assets. Reducing efficiency is not a popular solution with politicians, business people or accountants. Shareholders do not like being told that there is spare, unused capacity in their system, or that money has been spent on installing expensive components that often sit unused. As a result, investment in surge capacity is often not prioritised and this can actually make us more vulnerable to catastrophes. And given the catastrophic nature of such black swan events, when the surge capacity is needed, it is really needed. There is another argument against such preventative measures: black swan events may not always be undesirable. It is true that in an area like homeland defence you very much do not want them. However, innovators like Steve Jobs, Bill Gates or Jeff Bezos could very well be seen as individual black swans, mavericks against the consensus norm way of operating in business, and their actions drive the economy. Picking which kind of black swan to defend against is problematic, because by their very nature they are unforeseen and cannot be planned for. Even when plans are made, they cannot always be perfect. In a 2011 interview, Lewis said that he was confident about the American political system because it was already deliberately suboptimal. The Founding Fathers wanted it to be inefficient; they wanted the American government to be immune from the same kind of totalitarian tendencies they discerned in George III. Therefore, they built in a system of division of powers and checks and balances, defined by the US Constitution. And indeed, for 200 years more, the Constitution survived every black swan thrown at it, including a civil war. Then, in January 2021, a mob that refused to accept the result of a US election stormed the Capitol to prevent the ratification of that election – a process laid down in the same Constitution. A new black swan had entered the arena, one we will talk about in a later chapter. * * * So, what advice would I give myself starting my career again? First, consider that anyone who says they know for sure what is coming – even the smartest management consultant – almost certainly does not. We have shown that blithe predictions, or for that matter even extremely well informed decisions, cannot be made in a complex system where the impossibility of accurate prediction is inbuilt. Probability can be assessed, but should not be taken as absolute. There is simply no way to know exactly what markets will do next, or what the reaction will be to whatever they do. So, listen to more than one individual expert and calculate a path that weaves the best way between all their predicted outcomes. Understand that we are all subject to massive, complex systems, and sometimes, despite our best efforts, events far away will have a bigger impact on our immediate lives than our own actions. Therefore, we need to pay attention to the big picture, stay philosophical and remain flexible. Always have a Plan B. Second, many companies are so tightly optimised that they effectively live in dread of the next downturn. But while downturns – falling grains of sand – will always come, most will probably not lead to a crash – an avalanche – simply because most downturns do not. Your company does not have to run in a permanent crisis-management mode, but in order to be resilient, it does need to build in some redundancy. Concentrate on those things that are within your control, like the company culture and employee well-being. Learn to value those things that bring value to your firm without necessarily showing up on the balance sheet. Getting fired hit me like a physical blow; the damage would have been halved if I had at least walked into that room with the possibility at the back of my mind that I might be about to be fired. As it is, part of the impact of sudden redundancy is psychological. Your confidence in yourself and your faith in the goodwill of others take a severe knock, and it can take a long time to pick yourself back up and tackle the job market again. The more surge capacity you have as an individual, the more likely you are to bounce back. Instead of working at 100 per cent of your capacity for your employer, perhaps see if you can do your job equally well at 80 per cent. Use the remaining 20 per cent for emergencies and to look after yourself. Avalanches can bring down a sandpile, but the individual grains survive.explain this chapter in a very easy way