Economic Forecast Quotes

The only function of economic forecasting is to make astrology look respectable.

Value versus Cost Economists tend to focus on cost, and, as economists, we are as guilty of that as anyone. The entire premise of our first book, Prediction Machines, was that AI advances were going to dramatically reduce the cost of prediction, leading to a scale-up of its use. However, while that book suggested that the initial uses of AI would be where prediction was already occurring, either explicitly in, say, forecasting sales or the weather, or implicitly in classifying photos and language, we were mindful that the real opportunity would be the new applications and uses that were enabled when prediction costs fell low enough.

The old rule of forecasting was to make as many forecasts as possible and publicise the ones you got right. The new rule is to forecast so far in the future, no one will know you got it wrong.

Centuries ago human knowledge increased slowly, so politics and economics changed at a leisurely pace too. Today our knowledge is increasing at breakneck speed, and theoretically we should understand the world better and better. But the very opposite is happening. Our new-found knowledge leads to faster economic, social and political changes; in an attempt to understand what is happening, we accelerate the accumulation of knowledge, which leads only to faster and greater upheavals. Consequently we are less and less able to make sense of the present or forecast the future. In 1016 it was relatively easy to predict how Europe would look in 1050. Sure, dynasties might fall, unknown raiders might invade, and natural disasters might strike; yet it was clear that in 1050 Europe would still be ruled by kings and priests, that it would be an agricultural society, that most of its inhabitants would be peasants, and that it would continue to suffer greatly from famines, plagues and wars. In contrast, in 2016 we have no idea how Europe will look in 2050. We cannot say what kind of political system it will have, how its job market will be structured, or even what kind of bodies its inhabitants will possess.

Most gurus and forecasters are willing to give people what they want: exotic reasons to believe that they are in with the smart crowd.

Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics where the data is so noisy.

Economics is a discipline for quiet times. The profession, it turns out, … has no grip on understanding how the abnormal grows out of the normal and what happens next, its practitioners like weather forecasters who don’t understand storms. —Will Hutton, journalist The Observer, London

The only function of economic forecasting is to make astrology look respectable.

We need to incorporate the contagion of narratives into economic theory. Otherwise, we remain blind to a very real, very palpable, very important mechanism for economic change, as well as a crucial element for economic forecasting. If we do not understand the epidemics of popular narratives, we do not fully understand changes in the economy and in economic behavior.

So we should have some sympathy for economic forecasters.50 It’s hard enough to know where the economy is going. But it’s much, much harder if you don’t know where it is to begin with.

It turns out that the economic forecasts produced by the White House, for instance, have historically been among the least accurate of all,69 regardless of whether it’s a Democrat or a Republican in charge.)

But do you know what happened during this period? Where do we begin ... 1.3 million Americans died while fighting nine major wars. Roughly 99.9% of all companies that were created went out of business. Four U.S. presidents were assassinated. 675,000 Americans died in a single year from a flu pandemic. 30 separate natural disasters killed at least 400 Americans each. 33 recessions lasted a cumulative 48 years. The number of forecasters who predicted any of those recessions rounds to zero. The stock market fell more than 10% from a recent high at least 102 times. Stocks lost a third of their value at least 12 times. Annual inflation exceeded 7% in 20 separate years. The words “economic pessimism” appeared in newspapers at least 29,000 times, according to Google.

Assuming that something ugly will stay ugly is an easy forecast to make. And it’s persuasive, because it doesn’t require imagining the world changing. But problems correct and people adapt. Threats incentivize solutions in equal magnitude. That’s a common plot of economic history that is too easily forgotten by pessimists who forecast in straight lines.

The economy depends about as much on economists as the weather does on weather forecasters.

Stabilizing an Unstable Economy,16 Minsky described

one of the arguments of this book is that economics has encouraged ways of thinking that made crises more probable. Economists have brought the problem upon themselves by pretending that they can forecast. No

Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics where the data is so noisy. Statistical inferences are much stronger when backed up by theory or at least some deeper thinking about their root causes.

This book aims to learn from that mistake. One of its goals is to ask whether Minsky’s demand for a theory that generates the possibility of great depressions is reasonable and, if so, how economists should respond. I believe it is quite reasonable. Many mainstream economists react by arguing that crises are impossible to forecast: if they were not, they would either already have happened or been forestalled by rational agents. That is certainly a satisfying doctrine, since few mainstream economists foresaw the crisis, or even the possibility of one. For the dominant school of neoclassical economics, depressions are a result of some external (or, as economists say, ‘exogenous’) shock, not of forces generated within the system.

Basically, Graham breaks the art of investing down into two simple variables – price and value. Value is what a business is worth. Price is what you have to pay to get it. Given the stock market’s manic-depressive behavior, numerous occasions arise where a business’ market price is distinctly out of line with its true business value. In such instances, an investor may be able to purchase a dollar of value for just 50 cents. Note that there is no mention here of interest rates, economic forecasts, technical charts, market cycles, etc. The only issues are price and value. I should also note that Graham emphasizes a large margin of safety. The strategy is not to buy a dollar of value for 97 cents. Rather, the gap should be dramatic so as to absorb the effects of miscalculation and worse-than-average luck.

So these are the possibilities I see with regard to economic forecasts: Most economic forecasts are just extrapolations. Extrapolations are usually correct but not valuable. Unconventional forecasts of significant deviation from trend would be very valuable if they were correct, but usually they aren’t. Thus most forecasts of deviation from trend are incorrect and also not valuable. A few forecasts of significant deviation turn out to be correct and valuable—leading their authors to be lionized for their acumen—but it’s hard to know in advance which will be the few right ones. Since the overall batting average with regard to them is low, unconventional forecasts can’t be valuable on balance. There are forecasters who became famous for a single dramatic correct call, but the majority of their forecasts weren’t worth following.

We produce thirty-year projections of social security deficits and oil prices without realizing that we cannot even predict these for next summer—our cumulative prediction errors for political and economic events are so monstrous that every time I look at the empirical record I have to pinch myself to verify that I am not dreaming. What is surprising is not the magnitude of our forecast errors, but our absence of awareness of it.

If the twenty-first century turns out to be a time of low (demographic and economic) growth and high return on capital (in a context of heightened international competition for capital resources), or at any rate in countries where these conditions hold true, inheritance will therefore probably again be as important as it was in the nineteenth century. An evolution in this direction is already apparent in France and a number of other European countries, where growth has already slowed considerably in recent decades. For the moment it is less prominent in the United States, essentially because demographic growth there is higher than in Europe. But if growth ultimately slows more or less everywhere in the coming century, as the median demographic forecasts by the United Nations (corroborated by other economic forecasts) suggest it will, then inheritance will probably take on increased importance throughout the world.

Today our knowledge is increasing at breakneck speed, and theoretically we should understand the world better and better. But the very opposite is happening. Our new-found knowledge leads to faster economic, social and political changes; in an attempt to understand what is happening, we accelerate the accumulation of knowledge, which leads only to faster and greater upheavals. Consequently we are less and less able to make sense of the present or forecast the future.

If a model did anything too obviously bizarre—flooded the Sahara or tripled interest rates—the programmers would revise the equations to bring the output back in line with expectation. In practice, econometric models proved dismally blind to what the future would bring, but many people who should have known better acted as though they believed in the results. Forecasts of economic growth or unemployment were put forward with an implied precision of two or three decimal places. Governments and financial institutions paid for such predictions and acted on them, perhaps out of necessity or for want of anything better. Presumably they knew that such variables as “consumer optimism” were not as nicely measurable as “humidity” and that the perfect differential equations had not yet been written for the movement of politics and fashion. But few realized how fragile was the very process of modeling flows on computers, even when the data was reasonably trustworthy and the laws were purely physical, as in weather forecasting.

The world has enjoyed a notably long period marked by relative peace and security. Nevertheless, the forecasts of increasing fragile states, mounting conflicts born of natural resource scarcity, and the rising risk in the incidence of terrorism around the world all point to an increasingly politically volatile world, one that is worsened by economic uncertainty. The Horizon 2025: Creative Destruction in the Aid Industry report cautions that within the next decade more than 80 percent of the world’s population will live in fragile states, susceptible to civil wars that could spill into cross-border conflicts.4 The US National Intelligence Council has published a similarly dire forecast of more clashes in decades to come. While this study focuses largely on the prospect of natural resource conflicts, water especially, it underscores the political vulnerability of many economies. A 2016 report by the Institute for Economics and Peace concludes 2014 was the worst year for terrorism in a decade and a half, with attacks in ninety-three countries resulting in 32,765 people killed; 29,376 people died the year before, making 2013 the second worst year.5

Two decades after its first democratic election, South Africa ranks as the most unequal country on Earth.1 A host of policy tools could patch each of South Africa’s ills in piecemeal fashion, yet one force would unquestionably improve them all: economic growth.2 Diminished growth lowers living standards. With 5 percent annual growth, it takes just fourteen years to double a country’s GDP; with 3 percent growth, it takes twenty-four years. In general, emerging economies with a low asset base need to grow faster and accumulate a stock of assets more quickly than more developed economies in which basic living standards are already largely met. Meaningfully increasing per capita income is a critical way to lift people’s living standards and take them out of poverty, thereby truly changing the developmental trajectory of the country. South Africa has managed to push growth above a mere 3 percent only four times since the transition from apartheid, and it has remained all but stalled under 5 percent since 2008. And the forecast for growth in years to come hovers around a paltry 1 percent. Because South Africa’s population has been growing around 1.5 percent per year since 2008, the country’s per capita income has been stagnant over the period.

Truth be known, forecasts aren’t worth very much, and most people who make them don’t make money in the markets. . . . This is because nothing is certain and when one overlays the probabilities of all of the various things that affect the future in order to make a forecast, one gets a wide array of possibilities with varying probabilities, not one highly probable outcome. . . . We believe that market movements reflect economic movements. Economic movements are reflected in economic statistics. By studying the relationships between economic statistics and market movements, we’ve developed precise rules for identifying important shifts in the economic/market environment and in turn our positions. In other words, rather than forecasting changes in the economic environment and shifting positions in anticipation of them, we pick up these changes as they’re occurring and move our money around to keep in those markets which perform best in that environment.

Nuclear deterrence will remain a vital aspect of security. or Nuclear deterrence will have a smaller role in future security. Sources are split in their assessment of the importance of nuclear weapons and the validity of traditional nuclear deterrence in the 2001 - 2015 period. On the one hand are those who see nuclear weapons as decreasingly effective tools in deterring war. On the other are those experts who concede that nuclear weapons may have a different role than at the height of the Cold War, but who argue that they remain the ultimate deterrent, with considerable effect on the actions of even rogue states. Many experts who state a moral opposition to nuclear weapons have translated this into forecasts of a globalized world in which nuclear deterrence no longer makes sense. With greater economic interdependence, this argument runs, even the so-called "rogue states" will be reconciled to the international order, renouncing or reducing their overt or covert nuclear arsenals.

Short-termism also dominates in the marketplace. The market uses a discount rate of 10% per year (or more) when comparing costs now with benefits in the future. This means that a benefit that lies twenty years ahead will be valued at one-tenth of its real value. In other words, a problem twenty years in the future will be worth solving only if the cost of the solution is less than one-tenth of the value saved. It comes as no surprise to those who know economics that it is “cost efficient” to allow the world to collapse from climate damage, as long as the collapse is more than forty years into the future. The net present value of reducing emissions and saving the world is lower than the net present value of business as usual. It is cheaper to push the world over the cliff than to try to save it. The political world is not much better, given the short tenure of political appointments. Politicians can rarely spend time on agendas that yield a positive result only after the next election—which is normally less than four years away.

We have polluted for years, causing much damage to the environment, while the scientists currently making these complicated forecasting models were not sticking their necks out and trying to stop us from building these risks (they resemble those “risk experts” in the economic domain who fight the previous war)—these are the scientists now trying to impose the solutions on us. But the skepticism about models that I propose does not lead to the conclusions endorsed by anti-environmentalists and pro-market fundamentalists. Quite the contrary: we need to be hyper-conservationists ecologically, since we do not know what we are harming with now. That’s the sound policy under conditions of ignorance and epistemic opacity. To those who say “We have no proof that we are harming nature,” a sound response is “We have no proof that we are not harming nature, either;” the burden of the proof is not on the ecological conservationist, but on someone disrupting an old system. Furthermore we should not “try to correct” the harm done, as we may be creating another problem we do not know much about currently.

Centuries ago human knowledge increased slowly, so politics and economics changed at a leisurely pace too. Today our knowledge is increasing at breakneck speed, and theoretically we should understand the world better and better. But the very opposite is happening. Our new-found knowledge leads to faster economic, social and political changes; in an attempt to understand what is happening, we accelerate the accumulation of knowledge, which leads only to faster and greater upheavals. Consequently we are less and less able to make sense of the present or forecast the future. In 1016 it was relatively easy to predict how Europe would look in 1050. Sure, dynasties might fall, unknown raiders might invade, and natural disasters might strike; yet it was clear that in 1050 Europe would still be ruled by kings and priests, that it would be an agricultural society, that most of its inhabitants would be peasants, and that it would continue to suffer greatly from famines, plagues and wars. In contrast, in 2016 we have no idea how Europe will look in 2050. We cannot say what kind of political system it will have, how its job market will be structured, or even what kind of bodies its inhabitants will possess. A

For years the financial services have been making stock-market forecasts without anyone taking this activity very seriously. Like everyone else in the field they are sometimes right and sometimes wrong. Wherever possible they hedge their opinions so as to avoid the risk of being proved completely wrong. (There is a well-developed art of Delphic phrasing that adjusts itself successfully to whatever the future brings.) In our view—perhaps a prejudiced one—this segment of their work has no real significance except for the light it throws on human nature in the securities markets. Nearly everyone interested in common stocks wants to be told by someone else what he thinks the market is going to do. The demand being there, it must be supplied. Their interpretations and forecasts of business conditions, of course, are much more authoritative and informing. These are an important part of the great body of economic intelligence which is spread continuously among buyers and sellers of securities and tends to create fairly rational prices for stocks and bonds under most circumstances. Undoubtedly the material published by the financial services adds to the store of information available and fortifies the investment judgment of their clients.

The cheerleaders of the new data regime rarely acknowledge the impacts of digital decision-making on poor and working-class people. This myopia is not shared by those lower on the economic hierarchy, who often see themselves as targets rather than beneficiaries of these systems. For example, one day in early 2000, I sat talking to a young mother on welfare about her experiences with technology. When our conversation turned to EBT cards, Dorothy Allen said, “They’re great. Except [Social Services] uses them as a tracking device.” I must have looked shocked, because she explained that her caseworker routinely looked at her purchase records. Poor women are the test subjects for surveillance technology, Dorothy told me. Then she added, “You should pay attention to what happens to us. You’re next.” Dorothy’s insight was prescient. The kind of invasive electronic scrutiny she described has become commonplace across the class spectrum today. Digital tracking and decision-making systems have become routine in policing, political forecasting, marketing, credit reporting, criminal sentencing, business management, finance, and the administration of public programs. As these systems developed in sophistication and reach, I started to hear them described as forces for control, manipulation, and punishment

A few years ago my friend Jon Brooks supplied this great illustration of skewed interpretation at work. Here’s how investors react to events when they’re feeling good about life (which usually means the market has been rising): Strong data: economy strengthening—stocks rally Weak data: Fed likely to ease—stocks rally Data as expected: low volatility—stocks rally Banks make $4 billion: business conditions favorable—stocks rally Banks lose $4 billion: bad news out of the way—stocks rally Oil spikes: growing global economy contributing to demand—stocks rally Oil drops: more purchasing power for the consumer—stocks rally Dollar plunges: great for exporters—stocks rally Dollar strengthens: great for companies that buy from abroad—stocks rally Inflation spikes: will cause assets to appreciate—stocks rally Inflation drops: improves quality of earnings—stocks rally Of course, the same behavior also applies in the opposite direction. When psychology is negative and markets have been falling for a while, everything is capable of being interpreted negatively. Strong economic data is seen as likely to make the Fed withdraw stimulus by raising interest rates, and weak data is taken to mean companies will have trouble meeting earnings forecasts. In other words, it’s not the data or events; it’s the interpretation. And that fluctuates with swings in psychology.

The Princeton economist and wine lover Orley Ashenfelter has offered a compelling demonstration of the power of simple statistics to outdo world-renowned experts. Ashenfelter wanted to predict the future value of fine Bordeaux wines from information available in the year they are made. The question is important because fine wines take years to reach their peak quality, and the prices of mature wines from the same vineyard vary dramatically across different vintages; bottles filled only twelve months apart can differ in value by a factor of 10 or more. An ability to forecast future prices is of substantial value, because investors buy wine, like art, in the anticipation that its value will appreciate. It is generally agreed that the effect of vintage can be due only to variations in the weather during the grape-growing season. The best wines are produced when the summer is warm and dry, which makes the Bordeaux wine industry a likely beneficiary of global warming. The industry is also helped by wet springs, which increase quantity without much effect on quality. Ashenfelter converted that conventional knowledge into a statistical formula that predicts the price of a wine—for a particular property and at a particular age—by three features of the weather: the average temperature over the summer growing season, the amount of rain at harvest-time, and the total rainfall during the previous winter. His formula provides accurate price forecasts years and even decades into the future. Indeed, his formula forecasts future prices much more accurately than the current prices of young wines do. This new example of a “Meehl pattern” challenges the abilities of the experts whose opinions help shape the early price. It also challenges economic theory, according to which prices should reflect all the available information, including the weather. Ashenfelter’s formula is extremely accurate—the correlation between his predictions and actual prices is above .90.

Many models are constructed to account for regularly observed phenomena. By design, their direct implications are consistent with reality. But others are built up from first principles, using the profession’s preferred building blocks. They may be mathematically elegant and match up well with the prevailing modeling conventions of the day. However, this does not make them necessarily more useful, especially when their conclusions have a tenuous relationship with reality. Macroeconomists have been particularly prone to this problem. In recent decades they have put considerable effort into developing macro models that require sophisticated mathematical tools, populated by fully rational, infinitely lived individuals solving complicated dynamic optimization problems under uncertainty. These are models that are “microfounded,” in the profession’s parlance: The macro-level implications are derived from the behavior of individuals, rather than simply postulated. This is a good thing, in principle. For example, aggregate saving behavior derives from the optimization problem in which a representative consumer maximizes his consumption while adhering to a lifetime (intertemporal) budget constraint.† Keynesian models, by contrast, take a shortcut, assuming a fixed relationship between saving and national income. However, these models shed limited light on the classical questions of macroeconomics: Why are there economic booms and recessions? What generates unemployment? What roles can fiscal and monetary policy play in stabilizing the economy? In trying to render their models tractable, economists neglected many important aspects of the real world. In particular, they assumed away imperfections and frictions in markets for labor, capital, and goods. The ups and downs of the economy were ascribed to exogenous and vague “shocks” to technology and consumer preferences. The unemployed weren’t looking for jobs they couldn’t find; they represented a worker’s optimal trade-off between leisure and labor. Perhaps unsurprisingly, these models were poor forecasters of major macroeconomic variables such as inflation and growth.8 As long as the economy hummed along at a steady clip and unemployment was low, these shortcomings were not particularly evident. But their failures become more apparent and costly in the aftermath of the financial crisis of 2008–9. These newfangled models simply could not explain the magnitude and duration of the recession that followed. They needed, at the very least, to incorporate more realism about financial-market imperfections. Traditional Keynesian models, despite their lack of microfoundations, could explain how economies can get stuck with high unemployment and seemed more relevant than ever. Yet the advocates of the new models were reluctant to give up on them—not because these models did a better job of tracking reality, but because they were what models were supposed to look like. Their modeling strategy trumped the realism of conclusions. Economists’ attachment to particular modeling conventions—rational, forward-looking individuals, well-functioning markets, and so on—often leads them to overlook obvious conflicts with the world around them.

Following Dreman’s thinking led us to a plausible hypothesis. Suppose that the “P/ E effect” is caused by overreaction: high P/ E stocks (known as growth stocks because they are going to have to grow like crazy to justify their high prices) have gone up “too high” because investors have made overly optimistic forecasts of future growth rates, and low P/ E stocks, or value stocks, have sunk “too low,” because investors are excessively pessimistic. If true, the subsequent high returns to value stocks and low returns to growth stocks represent simple regression toward the mean.

Market economies are self-propelling and self-referential systems strongly driven by perceptions and expectations, and these systems routinely develop explosive amplifying feedbacks.

Metastability appears to be the key to explaining the quant meltdown, for example, and it plays a major role in the bursting of any economic bubble, whether in Internet stocks, mortgages, or foreign investment. It

High-performing companies view planning altogether differently. They want their forecasts to drive the work they actually do. To make this possible, they have to ensure that the assumptions underlying their long-term plans reflect both the real economics of their markets and the performance experience of the company relative to competitors.

Ocean, the future center of global trade. Why should it not prosper? Nobody can predict the future with 100 percent certainty. I’m not convinced it will happen. But I am a possibilist and these facts convince me: it is possible. The destiny instinct makes it difficult for us to accept that Africa can catch up with the West. Africa’s progress, if it is noticed at all, is seen as an improbable stroke of good fortune, a temporary break from its impoverished and war-torn destiny. The same destiny instinct also seems to make us take continuing Western progress for granted, with the West’s current economic stagnation portrayed as a temporary accident from which it will soon recover. For years after the global crash of 2008, the International Monetary Fund continued to forecast 3 percent annual economic growth for countries on Level 4. Each year, for five years, countries on Level 4 failed to meet this forecast. Each year, for five years, the IMF said, “Next year it will get back on track.” Finally, the IMF realized that there was no “normal” to go back to, and it downgraded its future growth expectations to 2 percent. At the same time the IMF acknowledged that the fast growth (above 5 percent) during those years had instead happened in countries on Level 2, like Ghana, Nigeria, Ethiopia, and Kenya in Africa, and Bangladesh in Asia. Why does this matter? One reason is this: the IMF forecasters’ worldview had a strong influence on where your retirement funds were invested. Countries in Europe and North America were expected to experience fast and reliable growth, which made them attractive to investors. When these forecasts turned out to be wrong, and when these countries did not in fact grow fast, the retirement funds did not grow either. Supposedly low-risk/high-return countries turned out to be high-risk/low-return countries. And at the same time African countries with great growth potential were being starved of investment. Another reason it matters, if you work for a company based in the old “West,” is that you are probably missing opportunities in the largest expansion of the middle-income consumer market in history, which is taking place right now in Africa and Asia. Other, local brands are already establishing a foothold, gaining brand recognition, and spreading throughout these continents, while you are still waking up to what is going on. The Western consumer market was just a teaser for what is coming next.

Not only were the best forecasters foxy as individuals, they had qualities that made them particularly effective collaborators—partners in sharing information and discussing predictions. Every team member still had to make individual predictions, but the team was scored by collective performance. On average, forecasters on the small superteams became 50 percent more accurate in their individual predictions. Superteams beat the wisdom of much larger crowds—in which the predictions of a large group of people are averaged—and they also beat prediction markets, where forecasters “trade” the outcomes of future events like stocks, and the market price represents the crowd prediction. It might seem like the complexity of predicting geopolitical and economic events would necessitate a group of narrow specialists, each bringing to the team extreme depth in one area. But it was actually the opposite. As with comic book creators and inventors patenting new technologies, in the face of uncertainty, individual breadth was critical. The foxiest forecasters were impressive alone, but together they exemplified the most lofty ideal of teams: they became more than the sum of their parts. A lot more.

Economic forecasters predict things with precise figures; rarely broad probabilities. The pundit who speaks in unshakable certainties will gain a larger following than the one who says “We can’t know for sure,” and speaks in probabilities.42

A WORLD OF SLOWER GROWTH AND HIGHER INFLATION If triple-digit oil prices are the true culprit behind the recent recession, what happens if oil prices recover to triple-digit levels or even close to them when the economy recovers? Does the economy slip right back into recession again? Everything else being equal—or ceteris paribus, as they say in the economics textbooks—that’s probably as good a forecast as any. Every oil shock has produced a global recession, and the record price increase of the past few years may produce the biggest one of all. But recessions, no matter how severe, are finite events. Ultimately, we face a far more challenging economic verdict from oil. Any way you cut it, a return to triple-digit oil prices means a much slower-growing world economy than before. And not just for a couple of quarters of recession. That’s because virtually every dollar of world GDP requires energy to produce. Not all of that energy, of course, comes from oil, but far too much does for world GDP not to be affected by oil’s growing scarcity. And there is nothing at the end of the day that we can do about depletion. Big tax cuts and big spending increases can mitigate triple-digit oil’s bite, but the deficits they inevitably produce ultimately lead to tax hikes and spending cuts that just make the suffering all the more painful down the road. Taking out a loan to pay your mortgage might defer your problems for a month or so, but in the end, it often makes your difficulties more acute. Borrowing from the future just turns today’s problems into tomorrow’s, and by the time tomorrow comes, they’ve become a lot bigger than if we had dealt with them today. Trillion-dollar-plus deficits, just like a near-zero percent federal funds rate, can mask the impact of high energy prices for a while, but ultimately they can’t protect economies that still run on oil from the impact of higher energy prices and the toll that they take.

In the present chapter, the doctrine of the chosen people serves only as an illustration. Its value as such can be seen from the fact that its chief characteristics are shared by the two most important modern versions of historicism, whose analysis will form the major part of this book—the historical philosophy of racialism or fascism on the one (the right) hand and the Marxian historical philosophy on the other (the left). For the chosen people racialism substitutes the chosen race (of Gobineau’s choice), selected as the instrument of destiny, ultimately to inherit the earth. Marx’s historical philosophy substitutes for it the chosen class, the instrument for the creation of the classless society, and at the same time, the class destined to inherit the earth. Both theories base their historical forecasts on an interpretation of history which leads to the discovery of a law of its development. In the case of racialism, this is thought of as a kind of natural law; the biological superiority of the blood of the chosen race explains the course of history, past, present, and future; it is nothing but the struggle of races for mastery. In the case of Marx’s philosophy of history, the law is economic; all history has to be interpreted as a struggle of classes for economic supremacy.

If one searches newspapers of the twentieth century for contemporary explanations of recessions as they begin, one finds that most talk concerns leading indicators rather than ultimate causes. For example, economists tend to bring up central bank policy, or confidence indexes, or the level of unsold inventories. But if asked what caused the changes in these leading indicators, they are typically silent. It is usually changing narratives that account for these changes, but there is no professional consensus regarding the most impactful narratives through time. Economists are reluctant to bring up popular narratives that they have heard that seem important and relevant to forecasts, since their only source about the narratives is hearsay, friends’ or neighbors’ talk. They usually have no way of knowing whether similar narratives were extant in past economic events. So, in their analyses, they do not mention changing narratives at all, as if they did not exist.

It was the income-determination model, based on the multiplier, together with the consequent development of national income statistics, which made Keynesian economics acceptable to policy-makers, since it offered them a seemingly secure method of forecasting and controlling the movement of such ‘real’ variables as investment, consumption, and employment.

traditional economic approaches fail to examine the role of public beliefs in major economic events—that is, narrative. By incorporating an understanding of popular narratives into their explanations of economic events, economists will become more sensitive to such influences when they forecast the future. In doing so, they will give policymakers better tools for anticipating and dealing with these developments.

The official chronicler of business cycles in the United States, the National Bureau of Economic Research, a not-for-profit group founded in 1920, would declare, though many months later, that a recession had set in that August. But in September, no one was aware of it. There were the odd signs of economic slowdown, especially in some of the more interest-rate-sensitive sectors - automobile sales had peaked and construction had been down all year, but most short-term indicators, for example, steel production or railroad freight car loadings, remained exceptionally strong. By the middle of the month, the market was back at its highs and Babson's forecast of a crash had been thoroughly discredited.

There is a prudent maxim of the economic forecaster's trade that is too often ignored: pick a number or pick a date, but never both.

On January 7, 1973, the New York Times featured an interview with one of the nation’s top financial forecasters, who urged investors to buy stocks without hesitation: “It’s very rare that you can be as unqualifiedly bullish as you can now.” That forecaster was named Alan Greenspan, and it’s very rare that anyone has ever been so unqualifiedly wrong as the future Federal Reserve chairman was that day: 1973 and 1974 turned out to be the worst years for economic growth and the stock market since the Great Depression.

There’s a phrase that critics of economic forecasting like to use: Give an economist a result you want, and he’ll find the numbers to justify it. This entire city is filled with number crunchers who look at the exact same data and interpret it in widely disparate ways on everything from the federal budget deficit to the Social Security surplus.” “Meaning that data can be manipulated.” “Of course it can, depending on who’s paying the meter and whose political agenda is being furthered,

Since the proper test of a theory is not the realism of its assumptions but the acceptability of its implications, and since these assumptions imply equilibrium conditions which form a major part of classical financial doctrine, it is far from clear that this formulation should be rejected—especially in view of the dearth of alternative models leading to similar results.

Europe’s lingering economic malaise is not just a slow recovery. Mainstream forecasts predict that hundreds of millions of Europeans will miss out on the opportunities that past generations took for granted. The crisis-burden falls hardest on Europe’s youth whose lifetime earning-profiles have already suffered. Money, however, is not the main issue. This is no longer just an economic crisis. The economic hardship has fuelled populism and political extremism. In a setting that is more unstable than any time since the 1930s, nationalistic, anti-European rhetoric is becoming mainstream. Political parties argue for breaking up the Eurozone and the EU. It is not inconceivable that far-right or far-left populist parties could soon hold or share power in several EU nations. Many influential observers recognise the bind in which Europe finds itself. A broad gamut of useful solutions have been suggested. Yet existing rules, institutions and political bargains prevent effective action. Policymakers seem to have painted themselves into a corner.

as the Russians press on the Poles from the east, the Germans won’t have an appetite for a third war with Russia. The United States, however, will back Poland, providing it with massive economic and technical support.

THE OLD RULE OF FORECASTING was to make as many forecasts as possible and publicize the ones you got right.

The new rule is to forecast so far into the future that no one will know you got it wrong.

no one can forecast the next century with any credibility and, more important, be held accountable for it.

People have been worried about overpopulation ever since Malthus and his “Essay on the Principle of Population,” which was published in 1798 (and in which he said, amid an outpouring of dire forecasts: “I happen to have a very bad fit of the tooth-ache at the time I am writing this”). Thomas Malthus died in 1834 worried about the world population of 1 billion people doubling every three hundred years. Currently at 7 billion and doubling every forty-seven years, we are now well beyond the situation that worried him. But instead of the global starvation and misery he envisioned, we have seen rises in wealth, standard of living, health, personal hygiene, and life expectancy. There is a reason for this: as economist Julian Simon once explained, “Resources come out of people’s minds more than out of the ground or air. Minds matter economically as much as or more than hands or mouths. Human beings create more than they use, on average.

Larry Kudlow hosted a business talk show on CNBC and is a widely published pundit, but he got his start as an economist in the Reagan administration and later worked with Art Laffer, the economist whose theories were the cornerstone of Ronald Reagan’s economic policies. Kudlow’s one Big Idea is supply-side economics. When President George W. Bush followed the supply-side prescription by enacting substantial tax cuts, Kudlow was certain an economic boom of equal magnitude would follow. He dubbed it “the Bush boom.” Reality fell short: growth and job creation were positive but somewhat disappointing relative to the long-term average and particularly in comparison to that of the Clinton era, which began with a substantial tax hike. But Kudlow stuck to his guns and insisted, year after year, that the “Bush boom” was happening as forecast, even if commentators hadn’t noticed. He called it “the biggest story never told.” In December 2007, months after the first rumblings of the financial crisis had been felt, the economy looked shaky, and many observers worried a recession was coming, or had even arrived, Kudlow was optimistic. “There is no recession,” he wrote. “In fact, we are about to enter the seventh consecutive year of the Bush boom.”19 The National Bureau of Economic Research later designated December 2007 as the official start of the Great Recession of 2007–9. As the months passed, the economy weakened and worries grew, but Kudlow did not budge. There is no recession and there will be no recession, he insisted. When the White House said the same in April 2008, Kudlow wrote, “President George W. Bush may turn out to be the top economic forecaster in the country.”20 Through the spring and into summer, the economy worsened but Kudlow denied it. “We are in a mental recession, not an actual recession,”21 he wrote, a theme he kept repeating until September 15, when Lehman Brothers filed for bankruptcy, Wall Street was thrown into chaos, the global financial system froze, and people the world over felt like passengers in a plunging jet, eyes wide, fingers digging into armrests. How could Kudlow be so consistently wrong? Like all of us, hedgehog forecasters first see things from the tip-of-your-nose perspective. That’s natural enough. But the hedgehog also “knows one big thing,” the Big Idea he uses over and over when trying to figure out what will happen next. Think of that Big Idea like a pair of glasses that the hedgehog never takes off. The hedgehog sees everything through those glasses. And they aren’t ordinary glasses. They’re green-tinted glasses—like the glasses that visitors to the Emerald City were required to wear in L. Frank Baum’s The Wonderful Wizard of Oz. Now, wearing green-tinted glasses may sometimes be helpful, in that they accentuate something real that might otherwise be overlooked. Maybe there is just a trace of green in a tablecloth that a naked eye might miss, or a subtle shade of green in running water. But far more often, green-tinted glasses distort reality. Everywhere you look, you see green, whether it’s there or not. And very often, it’s not. The Emerald City wasn’t even emerald in the fable. People only thought it was because they were forced to wear green-tinted glasses! So the hedgehog’s one Big Idea doesn’t improve his foresight. It distorts it. And more information doesn’t help because it’s all seen through the same tinted glasses. It may increase the hedgehog’s confidence, but not his accuracy. That’s a bad combination.

By the time World War I broke out, Babson was a leading market guru. Babson

many on business conditions for the Saturday Evening Post.80 He developed a close working relationship with the Post’s editor, George Lorimer, and in article after article predicted the future of America’s industries:

Babson advertised heavily for subscribers to his newsletter, describing his company in 1910 as the largest statistical organization in the United States.

Babson became a sought-after public speaker, and the newspapers reported his predictions as newsworthy events.

The only function of economic forecasting is to make astrology look respectable

Coates argued at length in his book The Hour Between Dog and Wolf

The largest, longest study of experts’ economic forecasts was performed by Philip Tetlock, a professor at the Haas Business School of the University of California–Berkeley. He studied 82,000 predictions over 25 years by 300 selected experts. Tetlock concludes that expert predictions barely beat random guesses. Ironically, the more famous the expert, the less accurate his or her predictions tended to be.

The only function of economic forecasting is to make astrology look respectable.” Alan Murray of the Wall Street Journal once quipped,

What's your economic forecast for 2015? The world's improving. The U.S. economy is going to grow a little faster than last year, though probably a little weaker in the first quarter. Europe's going to be incrementally better, because they've fixed their banking crisis. They've benefited from a very weakened euro. And then you have economies like India, which was stagnating at around 5-ish percent GDP growth a year ago, and now they're going to receive the benefit of lower oil prices. That's going to add at least 1 percent to India's GDP. Prime Minister Narendra Modi's reforms will probably add 1 or 2 percent more growth to overcome all the weakness in China.

predictions can’t vary more than the thing being forecast.

The system of profit equations that Jerome Levy wrote down in 1914 anticipated a similar set of equations written down by the Polish economist Michal Kalecki in 1935. And Kalecki’s system is regarded by a lot of people as containing nearly all of what’s useful in J. M. Keynes’ General Theory of Employment, Interest and Money, published in 1936 and widely accepted as one of the greatest works of economics ever. Levy went on to demonstrate that the proverb ‘if you’re so smart, why aren’t you rich?’ was not applicable in this case; aided by his sons, the Levy family went into finance with sufficient success that the Jerome Levy Forecasting Institute they endowed at Bard College continues to promote their approach to economics today. You used to be able to buy a copy of the book Jerome wrote in 1943, Economics Is an Exact Science, from them; I got mine in about 2002. In the introduction to that book, Levy sets out his view of the purpose of capitalism: The working class is the original and fundamental economic class . . . The function of the investing class is to serve the members of the working class by insuring them against loss and by providing them with desired goods. The justification for the existence of the investing class is the service it renders the working class, measured in terms of wages and desired goods. The contrary is not true. The working class does not exist to serve the investing class. The working class has the right to insure itself through organizations composed of its members or through government, thereby eliminating the investing class.

The Fed's economic models, and economic forecasting models in general, do a poor job of incorporating the economic effects of financial instability, in part because financial crisis are (fortunately) rare enough that relevant data are scarce.

Your best estimates for the future will not match up to the actual numbers for several reasons. First, even if your information sources are impeccable, you must convert raw information into forecasts, and any mistakes that you make at this stage will cause estimation error. Next, the path that you envision for a firm can prove to be hopelessly off. The firm may do much better or much worse than you expected it to perform, and the resulting earnings and cash flows will be different from your estimates; consider this firm-specific uncertainty. When valuing Cisco in 2001, for instance, we seriously underestimated how difficult it would be for the company to maintain its acquisition-driven growth in the future, and we overvalued the company as a consequence. Finally, even if a firm evolves exactly the way you expected it to, the macroeconomic environment can change in unpredictable ways. Interest rates can go up or down, and the economy can do much better or worse than expected. Our valuation of Marriott from November 2019 looks hopelessly optimistic, in hindsight, because we did not foresee the global pandemic in 2020 and the economic consequences for the hospitality business.

There are a few things I dismiss and a few I believe in thoroughly. The former include economic forecasts, which I think don’t add value, and the list of the latter starts with cycles and the need to prepare for them. “Hey, ” you might say, “that’s contradictory. The best way to prepare for cycles is to predict them, and you just said it can’t be done.” That's absolutely true, but in my opinion by no means debilitating. All of investing consists of dealing with the future [...] and the future is something we can’t know much about. But the limits on our foreknowledge needn't doom us to failure as long as we acknowledge them and act accordingly. In my opinion, the key to dealing with the future lies in knowing where you are, even if you can’t know precisely where you're going. Knowing where you are in a cycle and what that implies for the future is different from predicting the timing, extent and shape of the cyclical move.

Assuming that something ugly will stay ugly is an easy forecast to make. And it's persuasive, because it doesn't require imagining the world changing. But problems correct and people adapt. Threats incentivise solutions in equal magnitude. That's a common plot of economic history that is too easily forgotten by pessimists who forecast in straight lines.

Taleb, Kahneman, and I agree there is no evidence that geopolitical or economic forecasters can predict anything ten years out beyond the excruciatingly obvious—“there will be conflicts”—and the odd lucky hits that are inevitable whenever lots of forecasters make lots of forecasts. These limits on predictability are the predictable results of the butterfly dynamics of nonlinear systems. In my EPJ research, the accuracy of expert predictions declined toward chance five years out. And yet, this sort of forecasting is common, even within institutions that should know better. Every four years, Congress requires the Department of Defense to produce a twenty-year forecast of the national security environment.

Some analysts have renamed the welfare state, which obtained basically from about 1945 until in the 1970s, the garrison state. State legitimacy now depends on protection from these threats by targeting of dangerous others. I’ll say more about this in two weeks, but just to repeat what I indicated last week, the idea that the globalized form of capitalism means that decisions about the economic security and welfare of citizens are no longer within the hands necessarily of nation-state governors. To preserve their legitimacy as governors, they need to find a new basis for legitimation. Some people are arguing, and I would agree with much of this, that this is the new basis. The protection from dangerous others. We have endless enemies. Foreign communism morphed into terrorism. We now have a tremendous fear of immigrants and refugees. Witness the recent ban orders, the deportations, the detentions, the demonization of others. We have domestic enemies, people of color, the young, the old, LGBTQ communities, the differently abled, and along with that the militarization of the police and the criminalization of protest, which we’ll talk about in the last couple of weeks. Where is all of this headed? The Pentagon has a very bleak view of the future (see “Megacities: Urban Future, the Emerging Complexity: A Pentagon Video”), which views urban areas (both foreign and domestic) as basically breeding grounds for instability, unrest, and chaos. To think about the kind of underlying view of humanity this way I think comes naturally in some sense out of this very long history of militarization. That is, if you think of yourself as military, then everybody outside is an enemy. This is also what becomes part of the problem of militarizing the police. As the police become increasingly militaristic, the people that they supposedly protect and serve begin to look more and more like the non-police, like the enemy. This is, I think, an extremely dangerous kind of trend that we’re seeing. The forecast that this is the way in which the military will sort of reproduce itself by now being able to respond to these kinds of future threats where the mass of humanity is either an enemy or is in a witting or unwitting cloak for enemies. It’s extremely dangerous. One we should think very carefully about, but this is the Pentagon’s view largely of what that future looks like, and it is, in fact, urban, militarized, and dangerous.

Extreme economic ignorance was displayed when various experts, including Ph. D. economists, forecast the cost of the original Medicare law. They did simple extrapolations of past costs. Well, the cost forecast was off by a factor of more than One Thousand Percent. The cost they projected was less than ten percent of the cost that happened. Once they put in place various new incentives, the behavior changed in response to the incentives, and the numbers became quite different from their projection. And medicine invented new and expensive remedies, as it was sure to do. How could a great group of experts make such a silly forecast? Answer: They oversimplified to get easy figures, like the rube rounding pi to 3.11. They chose not to consider effects of effects on effects. and so on.

In 2013, on the auspicious date of April 1, I received an email from Tetlock inviting me to join what he described as “a major new research program funded in part by Intelligence Advanced Research Projects Activity, an agency within the U.S. intelligence community.” The core of the program, which had been running since 2011, was a collection of quantifiable forecasts much like Tetlock’s long-running study. The forecasts would be of economic and geopolitical events, “real and pressing matters of the sort that concern the intelligence community—whether Greece will default, whether there will be a military strike on Iran, etc.” These forecasts took the form of a tournament with thousands of contestants; the tournament ran for four annual seasons. “You would simply log on to a website,” Tetlock’s email continued, “give your best judgment about matters you may be following anyway, and update that judgment if and when you feel it should be. When time passes and forecasts are judged, you could compare your results with those of others.” I did not participate. I told myself I was too busy; perhaps I was too much of a coward as well. But the truth is that I did not participate because, largely thanks to Tetlock’s work, I had concluded that the forecasting task was impossible. Still, more than 20,000 people embraced the idea. Some could reasonably be described as having some professional standing, with experience in intelligence analysis, think tanks, or academia. Others were pure amateurs. Tetlock and two other psychologists, Barbara Mellers (Mellers and Tetlock are married) and Don Moore, ran experiments with the cooperation of this army of volunteers. Some were given training in some basic statistical techniques (more on this in a moment); some were assembled into teams; some were given information about other forecasts; and others operated in isolation. The entire exercise was given the name Good Judgment Project, and the aim was to find better ways to see into the future. This vast project has produced a number of insights, but the most striking is that there was a select group of people whose forecasts, while they were by no means perfect, were vastly better than the dart-throwing-chimp standard reached by the typical prognosticator. What is more, they got better over time rather than fading away as their luck changed. Tetlock, with an uncharacteristic touch of hyperbole, called this group “superforecasters.” The cynics were too hasty: it is possible to see into the future after all. What makes a superforecaster? Not subject-matter expertise: professors were no better than well-informed amateurs. Nor was it a matter of intelligence; otherwise Irving Fisher would have been just fine. But there were a few common traits among the better forecasters.

As I described in the “Uncorked!” chapter, the economic background in 1970 was turning grim, and sales were weakening. I was concerned. And then, once again, Scientific American came to the rescue. Each September that wonderful magazine devotes its entire issue to a single subject. In September 1970, it was the biosphere, a term I’d never seen before. It was the first time that a major scientific journal had addressed the problem of the environment. Rachel Carson’s Silent Spring, of course, had been serialized in the New Yorker in the late sixties, so the danger to the biosphere wasn’t exactly news, but it could be considered alarmist news. The prestige of Scientific American, however, carried weight. In fact, it knocked me out. I Suffered a Conversion on the Road to Damascus Within weeks, I subscribed to The Whole Earth Catalog, all the Rodale publications like Organic Gardening and Farming, Mother Earth, and a bunch I no longer remember. I was especially impressed by Francis Moore Lappé’s book Diet for a Small Planet. I joined the board of Pasadena Planned Parenthood, where I served for six years. Paul Ehrlich surfaced with his dismal, and proved utterly wrong, predictions. But hey! This guy was from Stanford! You had to believe him! And in 1972 all this was given statistical veracity by Jay Forrester of MIT, in the Club of Rome forecasts, which proved to be even further off the mark. But I bought them at the time. Bob Hanson, the manager of the new Trader Joe’s in Santa Ana, which was off to a slow start, was a health food nut. He kept bugging me to try “health foods.” After I’d read Scientific American, I was on board! Just how eating health foods would save the biosphere was never clear in my mind, or, in my opinion, in the mind of anyone else, except the 100 percent Luddites who wanted to return to some lifestyle approximating the Stone Age. After all, the motto of the Whole Earth Catalog was “access to tools,” hardly Luddite.

it makes them much less dependent on economic forecasts. This allowed them to shrug off later crises.2

As to the question whether this modification is opportune, the fact must not be lost sight of that the Berlin Conference never intended to fix unalterably the economic system of the Free State, which, as was already then foreseen, would undergo radical modifications under the influence of progress, nor of establishing for an indefinite period regulations which may hinder, check, and even arrest its development. Provision was wisely made for the probability of future changes, which would require a certain latitude in economic matters in order to secure their easy realization... The moment has now come when the marvellous progress made by the infant State is creating fresh needs, when it would be only in accordance with wisdom and foresight to revise an economic system primarily adapted to a creative and transitional period. Can we blame the infant State for a progress which, in its rapidity, has surpassed the most optimistic forecasts? Can we hinder and arrest this progress in refusing her the means necessary for her development? Can we condemn the Sovereign who has already made such great sacrifices to support for an indefinite period a burden which daily becomes heavier, and at the same time impose upon him new and heavy expenses necessitated by the suppression of the slave-trade? We are convinced that there will be but one answer to these questions.

Today, much attention is devoted to assessing the likely impact of rapid economic growth in China, where demand for luxury goods is forecast to quadruple in the next decade, or to considering social change in India, where more people have access to a mobile phone than to a flushing toilet.

Greenspan was known for his economic forecasting skills, having run a successful consulting firm,

The only purpose of economic forecasts is to give astrology a better image.

But while these views have also been evolving, many in the economic forecasting profession, however, have insufficiently internalized the implications for what lies ahead.

nowhere has the stagger chart been more productive than in forecasting economic trends. The way it works is shown in the figure below, which gives us forecasted rates of incoming orders for an Intel division. The stagger chart then provides the same forecast prepared in the following month, in the month after that, and so on. Such a chart shows not only your outlook for business month by month but also how your outlook varied from one month to the next. This way of looking at incoming business, of course, makes whoever does the forecasting take his task very seriously, because he knows that his forecast for any given month will be routinely compared with future forecasts and eventually with the actual result. But even more important, the improvement or deterioration of the forecasted outlook from one month to the next provides the most valuable indicator of business trends that I have ever seen.

Most economic forecasts consist of extrapolations of current levels and long-term trends. And since the economy usually doesn’t depart much from those levels and trends, most extrapolation forecasts turn out to be correct. But those extrapolation forecasts are likely to be commonly shared, already reflected in the market prices for assets, and thus not generators of superior performance—even when they come true. Here’s how Nobel Prize–winning economist Milton Friedman put it:

Since the world (and the United States) “went off gold,” the money supply has been regulated by men in place of the metal. Money supply, therefore, has become a master key of economic forecasting.

Because the general prospects of the enterprise carry major weight in the establishment of market prices, it is natural for the security analyst to devote a great deal of attention to the economic position of the industry and of the individual company in its industry. Studies of this kind can go into unlimited detail. They are sometimes productive of valuable insights into important factors that will be operative in the future and are insufficiently appreciated by the current market. Where a conclusion of that kind can be drawn with a fair degree of confidence, it affords a sound basis for investment decisions. Our own observation, however, leads us to minimize somewhat the practical value of most of the industry studies that are made available to investors. The material developed is ordinarily of a kind with which the public is already fairly familiar and that has already exerted considerable influence on market quotations. Rarely does one find a brokerage-house study that points out, with a convincing array of facts, that a popular industry is heading for a fall or that an unpopular one is due to prosper. Wall Street’s view of the longer future is notoriously fallible, and this necessarily applies to that important part of its investigations which is directed toward the forecasting of the course of profits in various industries. We must recognize, however, that the rapid and pervasive growth of technology in recent years is not without major effect on the attitude and the labors of the security analyst. More so than in the past, the progress or retrogression of the typical company in the coming decade may depend on its relation to new products and new processes, which the analyst may have a chance to study and evaluate in advance. Thus there is doubtless a promising area for effective work by the analyst, based on field trips, interviews with research men, and on intensive technological investigation on his own. There are hazards connected with investment conclusions derived chiefly from such glimpses into the future, and not supported by presently demonstrable value. Yet there are perhaps equal hazards in sticking closely to the limits of value set by sober calculations resting on actual results. The investor cannot have it both ways. He can be imaginative and play for the big profits that are the reward for vision proved sound by the event; but then he must run a substantial risk of major or minor miscalculation. Or he can be conservative, and refuse to pay more than a minor premium for possibilities as yet unproved; but in that case he must be prepared for the later contemplation of golden opportunities foregone.

This chart contrasts predictive and prospective thinking: Predictive Thinking Prospective Thinking Mindset Forecasting, “We expect …” Preparing, “But what if …” Goal Reduce or even discard uncertainty, fight ambiguity Live with uncertainty, embrace ambiguity, plan for set of contingencies Level of uncertainty Average High Method Extrapolating from present and past Open, imaginative Approach Categorical, assumes continuity Global, systemic, anticipates disruptive events Information inputs Quantitative, objective, known Qualitative (whether quantifiable or not), subjective, known or unknown Relationships Static, stable structures Dynamic, evolving structures Technique Established quantitative models (economics, mathematics, data) Developing scenarios using qualitative approaches (often building on megatrends) Evaluation method Numbers Criteria

This chart contrasts predictive and prospective thinking: Predictive Thinking Prospective Thinking Mindset Forecasting, “We expect …” Preparing, “But what if …” Goal Reduce or even discard uncertainty, fight ambiguity Live with uncertainty, embrace ambiguity, plan for set of contingencies Level of uncertainty Average High Method Extrapolating from present and past Open, imaginative Approach Categorical, assumes continuity Global, systemic, anticipates disruptive events Information inputs Quantitative, objective, known Qualitative (whether quantifiable or not), subjective, known or unknown Relationships Static, stable structures Dynamic, evolving structures Technique Established quantitative models (economics, mathematics, data) Developing scenarios using qualitative approaches (often building on megatrends) Evaluation method Numbers Criteria Attitude toward the future Passive or reactive (the future will be) Proactive and creative (we create or shape the future) Way of thinking Generally deduction Greater use of induction

As of July 2017 public spending per capita had fallen by 3.9%.[58] But this figure obscures the the fact that the government is allocating proportionally less of its budget to public services. Per person, day-to-day spending on public services has been cut to about four-fifths of what it was in 2010.[59] Public sector employment was slashed by 15.5% between September 2009 and April 2017, a reduction of nearly one million jobs, primarily affecting women, who make up around two-thirds of the public sector workforce. Overall, £22bn of the £26bn in ‘savings’ since June 2010 have been shouldered by women.[60] Lone mothers (who represent 92% of lone parents) have experienced an average drop in living standards of 18% (£8,790). Black and Asian households in the lowest fifth of incomes are the most affected, with average drops in living standards of 19.2% and 20.1% – £8,407 and £11,678 – respectively.[61] The Office of Budget Responsibility (OBR) has said that the cumulative scale of cuts to welfare are “unprecedented”, with real per capita welfare cap spending in 2021-22 projected to be around 10% lower than its 2015-16 level.[62] The Conservative-Liberal Democrat coalition government initially aimed to eliminate the deficit – the difference between annual government income and expenditure – by 2015. But weaker-than-expected economic growth forced the government to push the date back to 2025. The government tried to spin this as a generous easing of austerity, but it was merely giving itself several years longer to take on the deficit. In December 2017 the OBR said that GDP per person would be 3.5% smaller in 2021 than was forecast in March 2016. Contradicting the government, the OBR said the deficit would not be eliminated until 2031. The Institute for Fiscal Studies added that national debt – then standing at £1.94 trillion, with an annual servicing cost of £48bn – may not return to pre-crisis levels until the 2060s. Pressure on the public finances, primarily from health and social care, is only going to increase. In all of the OBR’s scenarios, spending grows faster than the economy. With health costs running ahead of inflation, the National Health Service (NHS) – already suffering from a £4.3bn annual shortfall – requires a 4% minimum annual increase in funding to maintain expenditure per capita amid a growing and ageing population.

As a result, tax revenues and state budgets shrink, at least in relative terms per capita. National debt inevitably grows in order to at least partially cover the shortfall. Of course, it grew enormously after governments bailed out the banks in the wake of the financial crash. The British government did so to the tune of 136.6bn and has admitted that it will never recoup at least £27bn of that amount. In the US the bailout cost at least $14.4 trillion.[56] At the start of of 2019, the US’s national debt stood at nearly $22 trillion, having increased by 10% since Trump took office two years earlier. Under his predecessor Barack Obama, the national debt increased 100%, from $10 trillion to $20 trillion. National debt has to be repaid to the government’s creditors: bondholders, ie people, companies and foreign governments; international organisations such as the World Bank; and private financial institutions. If debt is not or cannot be repaid it becomes increasingly difficult to attract creditors. US national debt when the Great Depression kicked off stood at 16% of GDP and rose to 44% when the depression ended at the end of World War Two. Before the The Great Recession it stood at 65% and by 2013 had exploded to over 100%.[57] Gross national debt and household debt have been at record highs at the same time for the first time ever. Austerity, the socialisation of national debt, therefore becomes an economic necessity, not simply an unfair and immoral ‘political choice’, as is claimed by democratic socialists. That public spending as a share of national income in Britain in 2017 (39.6%) was at the same level as in 2007 (39.6%) after seven years of debt servicing via savage cuts to state welfare and public services suggests national income must have fallen per capita. Indeed, official forecasts suggest that GDP per adult in 2022 will be 18% lower than it would have been had it grown by 2% a year since 2008 – it has averaged 1.1% – broadly the expected rate of growth at that time.

During our interview, he chuckled and asked, “What happened to the sugar and pineapple plantations?” and then went on to forecast that the new Big Five biotech corporations will meet the same demise as the old Big Five sugar companies. When oil prices soar and it becomes too expensive to ship their chemicals and seeds in and out of Hawai‘i, they will leave because they have no vested interest here. “It's really bad, short-term economic forecasting to think they are going to be here forever.” So it will be “up to this generation to create the building blocks for a food system to grow farms, farmers, and the system of a localized food economy.

The only function of economic forecasting is to make astrology look respectable.” John Kenneth Galbraith

Threats incentivize solutions in equal magnitude. That’s a common plot of economic history that is too easily forgotten by pessimists who forecast in straight lines.

En realidad, 1984 fue un año de formación en el que el patrón de las comunicaciones, en respuesta a la nueva tecnología, demostró ser muy distinto de lo que Orwell había esbozado cincuenta anos antes. Otra corriente, más básica al desarrollo del concepto de sociedad de la información, no iba unida al desarrollo de la biología o de la tecnología de la información, sino a la economía y a la sociología (raramente en ausencia de la política). EI sociólogo norteamericano Daniel Bell era consciente de la obra de su compatriota Fritz Machlup, economista, cuando publicó The Coming of Post Industrial Society, a Venture in Social Forecasting (1974), libro que se centra en la manera en que el sector de servicios de la economía iría cobrando cada vez más importancia que la industria. Los horizontes de Bell eran nuevos, al igual que su terminología. Empleaba en el título el prefijo «pos», que terminaría por ponerse de moda, hasta que muy poco después tomó carta de ciudadanía el adjetivo «posmoderno». Sin embargo, no había nada nuevo en su identificación de un cambio desde la manufactura a los servicios, cambio que ya había sido evidente para el economista agrícola australiano Colin Clark cuando editó su desdeñado Conditions of Economic Progress (1940).

[A 2009 study of the Connecticut Development Agency found that companies receiving tax incentives] had created only 9 percent of the jobs they had forecast. The average subsidy for each new job: $367,910.

The Bayesian Invisible Hand … free-market capitalism and Bayes’ theorem come out of something of the same intellectual tradition. Adam Smith and Thomas Bayes were contemporaries, and both were educated in Scotland and were heavily influenced by the philosopher David Hume. Smith’s 'Invisible hand' might be thought of as a Bayesian process, in which prices are gradually updated in response to changes in supply and demand, eventually reaching some equilibrium. Or, Bayesian reasoning might be thought of as an 'invisible hand' wherein we gradually update and improve our beliefs as we debate our ideas, sometimes placing bets on them when we can’t agree. Both are consensus-seeking processes that take advantage of the wisdom of crowds. It might follow, then, that markets are an especially good way to make predictions. That’s really what the stock market is: a series of predictions about the future earnings and dividends of a company. My view is that this notion is 'mostly' right 'most' of the time. I advocate the use of betting markets for forecasting economic variables like GDP, for instance. One might expect these markets to improve predictions for the simple reason that they force us to put our money where our mouth is, and create an incentive for our forecasts to be accurate. Another viewpoint, the efficient-market hypothesis, makes this point much more forcefully: it holds that it is 'impossible' under certain conditions to outpredict markets. This view, which was the orthodoxy in economics departments for several decades, has become unpopular given the recent bubbles and busts in the market, some of which seemed predictable after the fact. But, the theory is more robust than you might think. And yet, a central premise of this book is that we must accept the fallibility of our judgment if we want to come to more accurate predictions. To the extent that markets are reflections of our collective judgment, they are fallible too. In fact, a market that makes perfect predictions is a logical impossibility.