Importance Of Forecasting Quotes

Once in a while, however, the future turns out to be very different from the past. It’s at these times that accurate forecasts would be of great value. It’s also at these times that forecasts are least likely to be correct. Some forecasters may turn out to be correct at these pivotal moments, suggesting that it’s possible to correctly

As a result we are more and more directing the desires of men to something which does not exist - making the role of the eye in sexuality more and more important and at the same time making its demands more and more impossible. What follows you can easily forecast!

This highlights the single most important geopolitical fact in the world: the United States controls all of the oceans. No other power in history has been able to do this. And that control is not only the foundation of America’s security but also the foundation of its

As a result we are more and more directing the desires of men to something which does not exist—making the rôle of the eye in sexuality more and more important and at the same time making its demands more and more impossible. What follows you can easily forecast! That

As CEO, you should have an opinion on absolutely everything. You should have an opinion on every forecast, every product plan, every presentation, and even every comment. Let people know what you think. If you like someone’s comment, give her the feedback. If you disagree, give her the feedback. Say what you think. Express yourself. This will have two critically important positive effects:   Feedback won’t be personal in your company. If the CEO constantly gives feedback, then everyone she interacts with will just get used to it. Nobody will think, “Gee, what did she really mean by that comment? Does she not like me?” Everybody will naturally focus on the issues, not an implicit random performance evaluation.   People will become comfortable discussing bad news. If people get comfortable talking about what each other are doing wrong, then it will be very easy to talk about what the company is doing wrong. High-quality company cultures get their cue from data networking routing protocols: Bad news travels fast and good news travels slowly. Low-quality company cultures take on the personality of the Wicked Witch of the West in The Wiz: “Don’t nobody bring me no bad news.

The most realistic distinction between the investor and the speculator is found in their attitude toward stock-market movements. The speculator’s primary interest lies in anticipating and profiting from market fluctuations. The investor’s primary interest lies in acquiring and holding suitable securities at suitable prices. Market movements are important to him in a practical sense, because they alternately create low price levels at which he would be wise to buy and high price levels at which he certainly should refrain from buying and probably would be wise to sell. It is far from certain that the typical investor should regularly hold off buying until low market levels appear, because this may involve a long wait, very likely the loss of income, and the possible missing of investment opportunities. On the whole it may be better for the investor to do his stock buying whenever he has money to put in stocks, except when the general market level is much higher than can be justified by well-established standards of value. If he wants to be shrewd he can look for the ever-present bargain opportunities in individual securities. Aside from forecasting the movements of the general market, much effort and ability are directed on Wall Street toward selecting stocks or industrial groups that in matter of price will “do better” than the rest over a fairly short period in the future. Logical as this endeavor may seem, we do not believe it is suited to the needs or temperament of the true investor—particularly since he would be competing with a large number of stock-market traders and first-class financial analysts who are trying to do the same thing. As in all other activities that emphasize price movements first and underlying values second, the work of many intelligent minds constantly engaged in this field tends to be self-neutralizing and self-defeating over the years. The investor with a portfolio of sound stocks should expect their prices to fluctuate and should neither be concerned by sizable declines nor become excited by sizable advances. He should always remember that market quotations are there for his convenience, either to be taken advantage of or to be ignored. He should never buy a stock because it has gone up or sell one because it has gone down. He would not be far wrong if this motto read more simply: “Never buy a stock immediately after a substantial rise or sell one immediately after a substantial drop.” An

The fact is, nobody has the faintest idea of what is going to happen next year, next week, or even tomorrow. If you hope to get anywhere as a speculator, you must get out of the habit of listening to forecasts. It is of the utmost importance that you never take economists, market advisers, or other financial oracles seriously.

The most important thing that most people get from the news is the prediction of the following day’s weather, which most people are usually able to predict correctly by themselves.

Psychologists have a phrase for this kind of habitual forecasting: “creating mental models.”26 Understanding how people build mental models has become one of the most important topics in cognitive psychology.27 All people rely on mental models to some degree. We all tell ourselves stories about how the world works, whether we realize we’re doing it or not. But

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.

All the forecasts were tentatively hopeful about the idea of it not actually continuing to rain for the remainder of ever, but Varney had his doubts. It really did seem slightly sinister.

Isn’t it complicated to be human, though?” she said. “Animals seem to give up their lives so naturally…And after all, I grew up, I married John, I had Debby. So knowing, being able to understand and forecast and even predict an approximate date, shouldn’t make any difference. I guess consciousness makes individuals of us, and as individuals we lose the old acceptance…” “The one thing,” Marian said in a voice that went suddenly small and tight, “the thing I can hardly bear sometimes is that I won’t ever see her grow up. She’ll have to do it without whatever I could have given her.” “Time, too, time and everything that one could do in it, and the chance of wasting or losing or never even realizing it. It’s so important to us because we see it so close. We’re individuals, we’re full of ourselves, and so we’re bad historians. We get crazy and anxious because all of sudden there’s so little time left to be loving and generous as we wish we’d always been and always intended to be…do you suppose I feel the shortness of time because I want to experience everything and feel everything that the race has ever felt? Because there’s so much to feel and I’m greedy?

As a result we are more and more directing the desires of men to something which does not exist—making the rôle of the eye in sexuality more and more important and at the same time making its demands more and more impossible. What follows you can easily forecast!

A good family farm produces more, in net terms, than the farm family consumes. The good farmer has secured enough land to grow crops and support his or her livestock. The extra production beyond the farm family’s own consumption can be sold and traded for other goods and services—TVs, clothes, books. Some countries are like good family farms, with more bio-capacity than what it takes, in net terms, to provide for their inhabitants. Compare this with a weekend hobby farm, with honeybees, a rabbit, and an apple tree, where most resources have to be bought from elsewhere. Presently 80% of the world population lives in countries that are like hobby farms. They consume more, in net terms, than what the ecosystems of their country can regenerate. The rest is imported or derives from unsustainable overuse of local fields and forests.

Many professional judgments are nonverifiable. Barring egregious errors, underwriters will never know, for instance, whether a particular policy was overpriced or underpriced. Other forecasts may be nonverifiable because they are conditional. “If we go to war, we will be crushed” is an important prediction, but it is likely to remain untested (we hope). Or forecasts may be too long term for the professionals who make them to be brought to account—like, for instance, an estimate of mean temperatures by the end of the twenty-first century.

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.

They met near the southern limit of the establishment grounds and for a while they spoke in an abbreviated and Aesopic language. They understood each other well, with many decades of communication behind them, and it was not necessary for them to involve themselves in all the elaboration's of human speech. Daneel said in an all but unhearable whisper, "Clouds. Unseen." Had Daneel been speaking for human ears, he would have said, "As you see, friend Giskard, the sky has clouded up. Had Madam Gladia waited her chance to see Solaria, she would not, in any case, have succeeded." And Giskard's reply of "Predicted. Interview, rather," was the equivalent of "So much was predicted in the weather forecast, friend Daneel, and might have been used as an excuse to get Madam Gladia to bed early. It seemed to me to be more important, however, to meet the problem squarely and to persuade her to permit this interview I have already told you about.

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.

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 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.

People like Darlene who are particularly good at managing their attention tend to share certain characteristics. One is a propensity to create pictures in their minds of what they expect to see. These people tell themselves stories about what’s going on as it occurs. They narrate their own experiences within their heads. They are more likely to answer questions with anecdotes rather than simple responses. They say when they daydream, they’re often imagining future conversations. They visualize their days with more specificity than the rest of us do. Psychologists have a phrase for this kind of habitual forecasting: “creating mental models.” Understanding how people build mental models has become one of the most important topics in cognitive psychology. All people rely on mental models to some degree. We all tell ourselves stories about how the world works, whether we realize we’re doing it or not. But some of us build more robust models than others. We envision the conversations we’re going to have with more specificity, and imagine what we are going to do later that day in greater detail. As a result, we’re better at choosing where to focus and what to ignore. The secret of people like Darlene is that they are in the habit of telling themselves stories all the time. They engage in constant forecasting. They daydream about the future and then, when life clashes with their imagination, their attention gets snagged. That helps explain why Darlene noticed the sick baby. She was in the habit of imagining what the babies in her unit ought to look like. Then, when she glanced over and the bloody Band-Aid, distended belly, and mottled skin didn’t match the image in her mind, the spotlight in her head swung toward the child’s bassinet. Cognitive tunneling and reactive thinking occur when our mental spotlights go from dim to bright in a split second. But if we are constantly telling ourselves stories and creating mental pictures, that beam never fully powers down. It’s always jumping around inside our heads. And, as a result, when it has to flare to life in the real world, we’re not blinded by its glare.

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.

Dear KDP Author, Just ahead of World War II, there was a radical invention that shook the foundations of book publishing. It was the paperback book. This was a time when movie tickets cost 10 or 20 cents, and books cost $2.50. The new paperback cost 25 cents – it was ten times cheaper. Readers loved the paperback and millions of copies were sold in just the first year. With it being so inexpensive and with so many more people able to afford to buy and read books, you would think the literary establishment of the day would have celebrated the invention of the paperback, yes? Nope. Instead, they dug in and circled the wagons. They believed low cost paperbacks would destroy literary culture and harm the industry (not to mention their own bank accounts). Many bookstores refused to stock them, and the early paperback publishers had to use unconventional methods of distribution – places like newsstands and drugstores. The famous author George Orwell came out publicly and said about the new paperback format, if “publishers had any sense, they would combine against them and suppress them.” Yes, George Orwell was suggesting collusion. Well… history doesn’t repeat itself, but it does rhyme. Fast forward to today, and it’s the e-book’s turn to be opposed by the literary establishment. Amazon and Hachette – a big US publisher and part of a $10 billion media conglomerate – are in the middle of a business dispute about e-books. We want lower e-book prices. Hachette does not. Many e-books are being released at $14.99 and even $19.99. That is unjustifiably high for an e-book. With an e-book, there’s no printing, no over-printing, no need to forecast, no returns, no lost sales due to out of stock, no warehousing costs, no transportation costs, and there is no secondary market – e-books cannot be resold as used books. E-books can and should be less expensive. Perhaps channeling Orwell’s decades old suggestion, Hachette has already been caught illegally colluding with its competitors to raise e-book prices. So far those parties have paid $166 million in penalties and restitution. Colluding with its competitors to raise prices wasn’t only illegal, it was also highly disrespectful to Hachette’s readers. The fact is many established incumbents in the industry have taken the position that lower e-book prices will “devalue books” and hurt “Arts and Letters.” They’re wrong. Just as paperbacks did not destroy book culture despite being ten times cheaper, neither will e-books. On the contrary, paperbacks ended up rejuvenating the book industry and making it stronger. The same will happen with e-books. Many inside the echo-chamber of the industry often draw the box too small. They think books only compete against books. But in reality, books compete against mobile games, television, movies, Facebook, blogs, free news sites and more. If we want a healthy reading culture, we have to work hard to be sure books actually are competitive against these other media types, and a big part of that is working hard to make books less expensive. Moreover, e-books are highly price elastic. This means that when the price goes down, customers buy much more. We've quantified the price elasticity of e-books from repeated measurements across many titles. For every copy an e-book would sell at $14.99, it would sell 1.74 copies if priced at $9.99. So, for example, if customers would buy 100,000 copies of a particular e-book at $14.99, then customers would buy 174,000 copies of that same e-book at $9.99. Total revenue at $14.99 would be $1,499,000. Total revenue at $9.99 is $1,738,000. The important thing to note here is that the lower price is good for all parties involved: the customer is paying 33% less and the author is getting a royalty check 16% larger and being read by an audience that’s 74% larger. The pie is simply bigger.

SUMMARY A vast array of additional statistical methods exists. In this concluding chapter, we summarized some of these methods (path analysis, survival analysis, and factor analysis) and briefly mentioned other related techniques. This chapter can help managers and analysts become familiar with these additional techniques and increase their access to research literature in which these techniques are used. Managers and analysts who would like more information about these techniques will likely consult other texts or on-line sources. In many instances, managers will need only simple approaches to calculate the means of their variables, produce a few good graphs that tell the story, make simple forecasts, and test for significant differences among a few groups. Why, then, bother with these more advanced techniques? They are part of the analytical world in which managers operate. Through research and consulting, managers cannot help but come in contact with them. It is hoped that this chapter whets the appetite and provides a useful reference for managers and students alike. KEY TERMS   Endogenous variables Exogenous variables Factor analysis Indirect effects Loading Path analysis Recursive models Survival analysis Notes 1. Two types of feedback loops are illustrated as follows: 2. When feedback loops are present, error terms for the different models will be correlated with exogenous variables, violating an error term assumption for such models. Then, alternative estimation methodologies are necessary, such as two-stage least squares and others discussed later in this chapter. 3. Some models may show double-headed arrows among error terms. These show the correlation between error terms, which is of no importance in estimating the beta coefficients. 4. In SPSS, survival analysis is available through the add-on module in SPSS Advanced Models. 5. The functions used to estimate probabilities are rather complex. They are so-called Weibull distributions, which are defined as h(t) = αλ(λt)a–1, where a and 1 are chosen to best fit the data. 6. Hence, the SSL is greater than the squared loadings reported. For example, because the loadings of variables in groups B and C are not shown for factor 1, the SSL of shown loadings is 3.27 rather than the reported 4.084. If one assumes the other loadings are each .25, then the SSL of the not reported loadings is [12*.252 =] .75, bringing the SSL of factor 1 to [3.27 + .75 =] 4.02, which is very close to the 4.084 value reported in the table. 7. Readers who are interested in multinomial logistic regression can consult on-line sources or the SPSS manual, Regression Models 10.0 or higher. The statistics of discriminant analysis are very dissimilar from those of logistic regression, and readers are advised to consult a separate text on that topic. Discriminant analysis is not often used in public

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

Must we once again open ourselves to political repercussion because of the failure of an important piece of equipment?

The pioneers researched for this book take a simpler approach: Budgets are established only if some forecast is needed to inform an important decision.

Thus, a person can believe in the existence of Jesus or the existence of God all that he or she wants.  But without those important elements of commitment and of trust, there is no salvation.  James 2:19 says, "Thou believest that there is one God; thou doest well: the devils also believe, and tremble."  A simple example of that occurs with the weather.  If the forecaster says a chance of rain, then the person may or may not carry an umbrella.  But if the forecaster says that it will rain then the person that truly believes in the forecast will carry an umbrella.  In the same way, the person that truly believes in Jesus in the correct biblical context will commit to Him and will also trust in Him.  That is what it means to be saved.   Solomon said above to trust in the Lord with all of one's heart and to not lean unto one's own understanding.  For the person that has not come to Him by faith, that admonition says to come to Him by faith.  For the person that has come to Him by faith, it encourages trusting Him in all the important areas of life.  A godly person trusting in the Lord should commit his or her life to Him and trust in Him.  That commitment and trust pertain to decisions about education, career, finances, and anything else that can go wrong with a bad decision.  When a person must choose between one's own path and the Lord's path, always go in the way of His leading.

Over a century timescale, a kilogram of methane is about 30 times as powerful as a kilogram of carbon dioxide. Now get this. There are billions of tons of methane in molecular cages of water ice, called “clathrates,” held tight in the permafrost soil of Earth’s northern regions and at the cold bottom of the ocean. As Siberia warms, and as the water that circulates along the seafloor warms, the clathrates in the sediments both on land and deep in the sea will release the trapped methane molecules. Once liberated, they’ll come bubbling up. The methane gas will come out in the same fashion that bubbles are released by opening a bottle of soda, or when you pop the cork on a bottle of champagne. Scientists don’t know yet how much methane clathrates will add to the warming process. But intuitively, when one considers how much permafrost there is, or used to be, it seems likely that there’s a lot of methane and a lot of potential for a lot of trouble. More important, the recent research into the possible impact of clathrates makes a crucial point: Yes, there are uncertainties in the climate projections, but many of those uncertainties are things that would make the warming much worse than restrained scientists are forecasting.

To counter apathy, most change agents focus on presenting an inspiring vision of the future. This is an important message to convey, but it’s not the type of communication that should come first. If you want people to take risks, you need first to show what’s wrong with the present. To drive people out of their comfort zones, you have to cultivate dissatisfaction, frustration, or anger at the current state of affairs, making it a guaranteed loss. “The greatest communicators of all time,” says communication expert Nancy Duarte—who has spent her career studying the shape of superb presentations—start by establishing “what is: here’s the status quo.” Then, they “compare that to what could be,” making “that gap as big as possible.” We can see this sequence in two of the most revered speeches in American history. In his famous inaugural address, President Franklin D. Roosevelt opened by acknowledging the current state of affairs. Promising to “speak the whole truth, frankly and boldly,” he described the dire straits of the Great Depression, only then turning to what could be, unveiling his hope of creating new jobs and forecasting, “This great nation . . . will revive and will prosper. . . . The only thing we have to fear is fear itself.” When we recall Martin Luther King, Jr.’s, epic speech, what stands out is a shining image of a brighter future. Yet in his 16-minute oration, it wasn’t until the eleventh minute that he first mentioned his dream. Before delivering hope for change, King stressed the unacceptable conditions of the status quo. In his introduction, he pronounced that, despite the promise of the Emancipation Proclamation, “one hundred years later, the life of the Negro is still sadly crippled by the manacles of segregation and the chains of discrimination.” Having established urgency through depicting the suffering that was, King turned to what could be: “But we refuse to believe that the bank of justice is bankrupt.” He devoted more than two thirds of the speech to these one-two punches, alternating between what was and what could be by expressing indignation at the present and hope about the future. According to sociologist Patricia Wasielewski, “King articulates the crowd’s feelings of anger at existing inequities,” strengthening their “resolve that the situation must be changed.” The audience was only prepared to be moved by his dream of tomorrow after he had exposed the nightmare of today.

A century ago, as physicians were slowly professionalizing and medicine was on the cusp of becoming scientific, a Boston doctor named Ernest Amory Codman had an idea similar in spirit to forecaster scorekeeping. He called it the End Result System. Hospitals should record what ailments incoming patients had, how they were treated, and—most important—the end result of each case. These records should be compiled and statistics released so consumers could choose hospitals on the basis of good evidence. Hospitals would respond to consumer pressure by hiring and promoting doctors on the same basis. Medicine would improve, to the benefit of all. “Codman’s plan disregarded a physician’s clinical reputation or social standing as well as bedside manner or technical skills,” noted the historian Ira Rutkow. “All that counted were the clinical consequences of a doctor’s effort.”8 Today, hospitals do much of what Codman demanded, and more, and physicians would find it flabbergasting if anyone suggested they stop. But the medical establishment saw it differently when Codman first proposed the idea.

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.

The most important mystery of ancient Egypt was presided over by a priesthood. That mystery concerned the annual inundation of the Nile flood plain. It was this flooding which made Egyptian agriculture, and therefore civilisation, possible. It was the centre of their society in both practical and ritual terms for many centuries; it made ancient Egypt the most stable society the world has ever seen. The Egyptian calendar itself was calculated with reference to the river, and was divided into three seasons, all of them linked to the Nile and the agricultural cycle it determined: Akhet, or the inundation, Peret, the growing season, and Shemu, the harvest. The size of the flood determined the size of the harvest: too little water and there would be famine; too much and there would be catastrophe; just the right amount and the whole country would bloom and prosper. Every detail of Egyptian life was linked to the flood: even the tax system was based on the level of the water, since it was that level which determined how prosperous the farmers were going to be in the subsequent season. The priests performed complicated rituals to divine the nature of that year’s flood and the resulting harvest. The religious elite had at their disposal a rich, emotionally satisfying mythological system; a subtle, complicated language of symbols that drew on that mythology; and a position of unchallenged power at the centre of their extraordinarily stable society, one which remained in an essentially static condition for thousands of years. But the priests were cheating, because they had something else too: they had a nilometer. This was a secret device made to measure and predict the level of flood water. It consisted of a large, permanent measuring station sited on the river, with lines and markers designed to predict the level of the annual flood. The calibrations used the water level to forecast levels of harvest from Hunger up through Suffering through to Happiness, Security and Abundance, to, in a year with too much water, Disaster. Nilometers were a – perhaps the – priestly secret. They were situated in temples where only priests were allowed access; Herodotus, who wrote the first outsider’s account of Egyptian life the fifth century BC, was told of their existence, but wasn’t allowed to see one. As late as 1810, thousands of years after the nilometers had entered use, foreigners were still forbidden access to them. Added to the accurate records of flood patters dating back centuries, the nilometer was an essential tool for control of Egypt. It had to be kept secret by the ruling class and institutions, because it was a central component of their authority. The world is full of priesthoods. The nilometer offers a good paradigm for many kinds of expertise, many varieties of religious and professional mystery. Many of the words for deliberately obfuscating nonsense come from priestly ritual: mumbo jumbo from the Mandinka word maamajomboo, a masked shamanic ceremonial dancer; hocus pocus from hoc est corpus meum in the Latin Mass. On the one hand, the elaborate language and ritual, designed to bamboozle and mystify and intimidate and add value; on the other the calculations that the pros make in private. Practitioners of almost every métier, from plumbers to chefs to nurses to teachers to police, have a gap between the way they talk to each other and they way they talk to their customers or audience. Grayson Perry is very funny on this phenomenon at work in the art world, as he described it in an interview with Brian Eno. ‘As for the language of the art world – “International Art English” – I think obfuscation was part of its purpose, to protect what in fact was probably a fairly simple philosophical point, to keep some sort of mystery around it. There was a fear that if it was made understandable, it wouldn’t seem important.

FROM OTHER SOURCES Pre–race and Venue Homework Get hold of any history of past events at the venue, plus any information that the conducting club may have about weather and expected conditions. Go to the weather bureau and get history for the area. Speak to sailors from your class who have this venue as their home club or who have sailed there on a number of occasions. Boat, Sails, Gear Preparation Checklist Many times the outcome of a race is as dependent on what you have done prior to the race as to what you do out on the course. Sometimes no matter how good your tactics and strategy are a simple breakage could render all that useless. Hull – make sure that your hull is well sanded and polished, centreboard strips are in good condition, venturis if fitted are working efficiently, buoyancy tanks are dry and there are no extraneous pieces of kit in your boat which adds unwanted weight. Update any gear that looks tired or worn especially control lines. Mast, boom and poles – check that all halyards, stays and trapeze wires are not worn or damaged and that pins are secure, knots tight and that anything that can tear a sail or injure flesh is taped. Mark the full hoist position on all halyards. Deck hardware – check all cam cleats for spring tension and tape anything that may cause a sail tear or cut legs hands and arms. Check the length of all sheets and control lines and shorten anything that is too long. This not only reduces weight but also minimises clutter. Have marks on sheets and stick or draw numbers and reference scales for the jib tracks, outhaul and halyards so that you can easily duplicate settings that you know are fast in various conditions. Centreboard and rudder – ensure that all nicks and gouges are filled and sanded and the surfaces are polished and most importantly that rudder safety clips are working. Sails – select the correct battens for the day’s forecast. Write on the deck, with a china graph pencil, things like the starting sequence, courses, tide times and anything else that will remind you to sail fast. Tools and spares – carry a shackle key with screwdriver head on your person along with some spare shackles and short lengths of rope or different diameters. A tool like a Leatherman can be very useful to deal with unexpected breakages that can occur even in the best prepared boat.

apparent. To counter apathy, most change agents focus on presenting an inspiring vision of the future. This is an important message to convey, but it’s not the type of communication that should come first. If you want people to take risks, you need first to show what’s wrong with the present. To drive people out of their comfort zones, you have to cultivate dissatisfaction, frustration, or anger at the current state of affairs, making it a guaranteed loss. “The greatest communicators of all time,” says communication expert Nancy Duarte—who has spent her career studying the shape of superb presentations—start by establishing “what is: here’s the status quo.” Then, they “compare that to what could be,” making “that gap as big as possible.” We can see this sequence in two of the most revered speeches in American history. In his famous inaugural address, President Franklin D. Roosevelt opened by acknowledging the current state of affairs. Promising to “speak the whole truth, frankly and boldly,” he described the dire straits of the Great Depression, only then turning to what could be, unveiling his hope of creating new jobs and forecasting, “This great nation . . . will revive and will prosper. . . . The only thing we have to fear is fear itself.” When we recall Martin Luther King, Jr.’s, epic speech, what stands out is a shining image

The most important skill to learn as a futurist is how to think, map and plan for the future of science and technology applied to its context and use

Test the market with samples first, if you can, to know what is really going to sell. • If possible, don’t build inventory in large quantities and eat up cash unless the business has the orders in its hands. • Try to find strategic partners that have quick turnarounds for building inventory. • Unless you have real-time data on customer demand and have an extremely tight connection to your suppliers, you’ll never get inventory forecasting exactly right. • Err on the side of less rather than more inventory as a rule of thumb. • If you have to make a trade-off between paying more per unit in COGS to reduce the cycle time to build inventory, choose the higher COGS and reduced production time. You’ll be placing smaller orders with greater frequency, turning inventory faster and cash faster. Read this point again—it’s not very complicated (place smaller orders, more frequently), but it’s really, really important for managing your inventory.

WATCH YOUR DOWNSIDE It’s often said that opportunity is as important as risk. That’s false. Risk can kill you or your project. No upside can compensate for that. For fat-tailed risk, which is present in most projects, forget about forecasting risk; go directly to mitigation by spotting and eliminating dangers.

The gap between a conventional forecast and one that uses RCF varies by project type, but for over half the projects for which we have data, RCF is better by 30 percentage points or more. That’s on average. A 50 percent increase in accuracy is common. Improvements of more than 100 percent are not uncommon. Most gratifyingly, given the method’s intellectual roots, Daniel Kahneman wrote in Thinking, Fast and Slow that using reference-class forecasting is “the single most important piece of advice regarding how to increase accuracy in forecasting through improved methods.

The single most important driver of forecasters’ success was how often they updated their beliefs. The best forecasters went through more rethinking cycles. They had the confident humility to doubt their judgments and the curiosity to discover new information that led them to revise their predictions.

The single most important driver of forecasters’29 success was how often they updated their beliefs. The best forecasters went through more rethinking cycles.

We have two classes of forecasters: Those who don’t know—and those who don’t know they don’t know. JOHN KENNETH GALBRAITH

The problem is that extraordinary performance comes only from correct nonconsensus forecasts, but nonconsensus forecasts are hard to make, hard to make correctly and hard to act on. Over the years, many people have told me that the matrix shown below had an impact on them: You can’t do the same things others do and expect to outperform. . . . Unconventionality shouldn’t be a goal in itself, but rather a way of thinking. In order to distinguish yourself from others, it helps to have ideas that are different and to process those ideas differently. I conceptualize the situation as a simple 2-by-2 matrix: Conventional Behavior Unconventional Behavior Favorable Outcomes Average good results Above-average results Unfavorable Outcomes Average bad results Below-average results Of course it’s not that easy and clear-cut, but I think that’s the general situation.

I like studying the investing behaviors that never change, and I’d never have the time to do that if I spent my day predicting what the economy will do next quarter. The same is true in virtually every field. The more precise you try to be, the less time you have to focus on big-picture rules that are probably more important. It’s less about admitting that we can’t forecast, and more about acknowledging that if your forecast is merely good enough, you can invest your time and resources more efficiently elsewhere. Just like evolution, the key is realizing that the more perfect you try to become, the more vulnerable you generally are.

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.

This, ultimately, is one of the most important secrets to learning how to make better decisions. Making good choices relies on forecasting the future. Accurate forecasting requires exposing ourselves to as many successes and disappointments as possible.

Technological innovations that produced certain major components of the United States military cannot be understood as resulting from a qualitative arms race. Those involved in decisions about new military technologies for the U.S. Army and Air Force simply do not appear to have had access to good intelligence about the Soviet military technological developments. How, then, were decisions made as to technologies to develop? Military research and development decisions are made amid great uncertainties. In an ideal world, such decisions would be managed by estimating the future costs of alternative programs and their prospective military values, and then pursuing the program with the best ratio of cost to value. But...there are tremendous difficulties in forecasting the real value and costs of weapons development programs. These uncertainties, combined with the empirical difficulty American technology managers had in collecting intelligence on the Soviet Union, meant that research and development strategies in the real world tended to become strategies for managing uncertainties. At least two such strategies are conceivable. One of the most politically important can be called, for want of a better phrase, "let the scientists choose." [This approach should be] compared with the theoretical and practical arguments for a strategy that concentrates on low-cot hedges against various forms of uncertainty.

-It is possible to vastly compress most learning. In a surprising number of cases, it is possible to do something in 1-10 months that is assumed to take 1-10 years. -The more you compress things, the more physical limiters become a bottleneck. All learning is physically limited. The brain is dependent on finite quantities of neurotransmitters, memories require REM and non-REM (NREM) sleep for consolidation, etc. The learning graph is not unlike the stress-recovery-hyperadaptation curves of weight training. -The more extreme your ambition, just as in sports, the more you need performance enhancement via unusual schedules, diet, drugs, etc. -Most important: due to the bipolar nature of the learning process, you can forecast setbacks. If you don't, you increase the likelihood of losing morale and quitting before the inflection point.

Despite proclamations and forecasts to the contrary, neither the nation-state nor the international system of states is dead in the new millennium. What has changed are calculations of state interest and state navigation of the international system. Both have become much more complex, owing to the increased importance of such factors as crossnational actors and forces.

This is a very important distinction between weather and climate models: for climate forecasts, the initial conditions in the atmosphere are not as important as the external forcings that have the ability to alter the character and types of weather (i.e., the statistics or what scientists would call the “distribution” of the weather) that make up the climate.

Good news is extrapolated into strong market expectations which are often not realized. As important, investor expectations are negatively correlated with model-based expected returns derived from dividend/price, consumption patterns and market valuation.  Investors, no matter what the level of experience, do not seem to use the models that provide useful information on expected returns. Put differently, when expected return models forecast higher returns, they are usually correct. When the expectations of returns are high from surveys, the actual returns are low. These market expectations are correlated with mutual fund flows. The surveys show expectation that investors actually use, albeit incorrectly.

Trends are more important now. It’s harder to reach everyone at one time, so you need to understand trends within individual groups or tribes. And with the rise in word of mouth, trends can give you data on the right places to start having a dialogue with consumers. Liisa Puolakka, Head of Brand Identity, Nokia

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.

forecast key events, but it’s unlikely to be the same people consistently. The sum of this discussion suggests that, on balance, forecasts are of very little value.

In other words, the brightness of the Pleiades in late June indeed correlates with rainfall during the growing season for potatoes the following October through March.8 This climate forecast is one of many that have come to the attention of scientists, and it reinforces the importance and significance of traditional knowledge.

increases in expenditures remains a top imperative for policymakers due to the unsustainable growth forecasts and concerns that health care will absorb an increasingly larger share of government budgets, leaving very little funding available for other important government programs.

They are placing demands on American corporations to provide prayer time for Islamic employees on the job. Dell Computers has already caved in to the pressure put forth by the Council on American-Islamic Relations (CAIR) regarding this issue and now allows its Muslim employees prayer time on the job. Our radio and TV talk show hosts are watching their tongues when criticizing even the radical Islamic element of the religion lest they be fired or sued, just as Michael Graham was fired from ABC radio for linking Islam to terrorism. The Islamic community throughout the world is outreproducing Christians and Jews almost seven to one. It will be a matter of a few generations before they can get voting power to challenge state laws and change the Constitution of the United States. Islam is already the fastest-growing religion in Europe. Driven by immigration and high birthrates, the number of Muslims on the continent has tripled in the last thirty years. Most demographers forecast a similar or even higher rate of growth in the coming decades. It is important to note that the world’s fastest-growing Muslim populations are found in Europe and the United States, where they are the second- or third-largest religious communities. This is the beginning of America’s and the West’s war with radical Islam.

We lack space here to discuss in detail the pros and cons of market forecasting. A great deal of brain power goes into this field, and undoubtedly some people can make money by being good stock-market analysts. But it is absurd to think that the general public can ever make money out of market forecasts. For who will buy when the general public, at a given signal, rushes to sell out at a profit? If you, the reader, expect to get rich over the years by following some system or leadership in market forecasting, you must be expecting to try to do what countless others are aiming at, and to be able to do it better than your numerous competitors in the market. There is no basis either in logic or in experience for assuming that any typical or average investor can anticipate market movements more successfully than the general public, of which he is himself a part. There is one aspect of the “timing” philosophy which seems to have escaped everyone’s notice. Timing is of great psychological importance to the speculator because he wants to make his profit in a hurry. The idea of waiting a year before his stock moves up is repugnant to him. But a waiting period, as such, is of no consequence to the investor. What advantage is there to him in having his money uninvested until he receives some (presumably) trustworthy signal that the time has come to buy? He enjoys an advantage only if by waiting he succeeds in buying later at a sufficiently lower price to offset his loss of dividend income. What this means is that timing is of no real value to the investor unless it coincides with pricing—that is, unless it enables him to repurchase his shares at substantially under his previous selling price.

One of the most important foundational elements of my investment philosophy is my conviction that we can’t know what the “macro future” has in store for us in terms of things like economies, markets or geopolitics. Or, to put it more precisely, few people are able on balance to know more about the macro future than others. And it’s only if we know more than others (whether that consists of having better data; doing a superior job of interpreting the data we have; knowing what actions to take on the basis of our interpretation; or having the emotional fortitude required to take those actions) that our forecasts will lead to outperformance.

Experts’ forecasts will always be wrong. It is simply impossible to predict with any useful degree of precision how disruptive products will be used or how large their markets will be. An important corollary is that, because markets for disruptive technologies are unpredictable, companies’ initial strategies for entering these markets will generally be wrong.

The first 20 percent often begins with having the right data, the right technology, and the right incentives. You need to have some information—more of it rather than less, ideally—and you need to make sure that it is quality-controlled. You need to have some familiarity with the tools of your trade—having top-shelf technology is nice, but it’s more important that you know how to use what you have. You need to care about accuracy—about getting at the objective truth—rather than about making the most pleasing or convenient prediction, or the one that might get you on television. Then you might progress to a few intermediate steps, developing some rules of thumb (heuristics) that are grounded in experience and common sense and some systematic process to make a forecast rather than doing so on an ad hoc basis.

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.

Here are the six steps Bazerman and Moore argue you should take, either implicitly or explicitly, when applying a rational decision-making process. “Define the problem. Managers often act without a thorough understanding of the problem to be solved, leading them to solve the wrong problem. Accurate judgment is required to identify and define the problem. Managers often err by (a) defining the problem in terms of a proposed solution, (b) missing a bigger problem, or (c) diagnosing the problem in terms of its symptoms. Your goal should be to solve the problem, not just eliminate its temporary symptoms.” “Identify the criteria. Most decisions require you to accomplish more than one objective. When buying a car, you may want to maximize fuel economy, minimize cost, maximize comfort, and so on. The rational decision maker will identify all relevant criteria in the decision-making process.” “Weight the criteria. Different criteria will vary in importance to a decision maker. Rational decision makers will know the relative value they place on each of the criteria identified (for example, the relative importance of fuel economy versus cost versus comfort). The value may be specified in dollars, points, or whatever scoring system makes sense.” “Generate alternatives. The fourth step in the decision-making process requires identification of possible courses of action. Decision makers often spend an inappropriate amount of search time seeking alternatives, thus creating a barrier to effective decision making. An optimal search continues only until the cost of the search outweighs the value of added information.” “Rate each alternative on each criterion. How well will each of the alternative solutions achieve each of the defined criteria? This is often the most difficult stage of the decision-making process, as it typically requires us to forecast future events. The rational decision maker carefully assesses the potential consequences on each of the identified criteria of selecting each of the alternative solutions.” “Compute the optimal decision. Ideally, after all of the first five steps have been completed, the process of computing the optimal decision consists of (a) multiplying the ratings in step 5 by the weight of each criterion, (b) adding up the weighted ratings across all of the criteria for each alternative, and (c) choosing the solution with the highest sum of weighted ratings.

Many of our most important decisions are, in fact, attempts to forecast the future.

Week 1: Build an Arsenal of Ideas Day 1: Predict the Future Day 2: Learn How Money Grows on Trees Day 3: Brainstorm, Borrow, or Steal Ideas Day 4: Weigh the Obstacles and Opportunities of Each Idea Day 5: Forecast Your Profit on the Back of a Napkin Week 2: Select Your Best Idea Day 6: Use the Side Hustle Selector to Compare Ideas Day 7: Become a Detective Day 8: Have Imaginary Coffee with Your Ideal Customer Day 9: Transform Your Idea into an Offer Day 10: Create Your Origins Story Week 3: Prepare for Liftoff Day 11: Assemble the Nuts and Bolts Day 12: Decide How to Price Your Offer Day 13: Create a Side Hustle Shopping List Day 14: Set Up a Way to Get Paid Day 15: Design Your First Workflow Day 16: Spend 10 Percent More Time on the Most Important Tasks Week 4: Launch Your Idea to the Right People Day 17: Publish Your Offer! Day 18: Sell Like a Girl Scout Day 19: Ask Ten People for Help Day 20: Test, Test, and Test Again Day 21: Burn Down the Furniture Store Day 22: Frame Your First Dollar Week 5: Regroup and Refine Day 23: Track Your Progress and Decide on Next Steps Day 24: Grow What Works, Let Go of What Doesn’t Day 25: Look for Money Lying Under a Rock Day 26: Get It Out of Your Head Day 27: Back to the Future

The car is only one of many clues we must consider, starting with what is arguably a more important question: Why would an internet company build a car that can drive itself? In forecasting, the second step tells us that we need to do some more digging. Why is Google—a nineteen-year-old company that for much of its history has specialized only in products to help us use the web better—hiring teams of researchers to develop self-driving cars?

what’s beyond the horizon. The fringe sketch is an outline of what and why, not how. Which is to say that now is not the time for process thinking, ruminating over procedures, or questioning whether something can actually be done. At this point in the forecasting process, our job is to expand our field of vision to include all of the unusual suspects and their work. Before starting a fringe sketch, it’s important to observe a few rules: 1. Include theoretical or even poor information. 2. Assume that a present-day obstacle might be overcome in the future. 3. Assume that if something can be hacked (or adapted for a slightly different use), it will.

If you were to zoom way out and look at the six steps of my forecasting method, you would see this duality in play. It’s not a happy accident. Scientific and technological advances depend on both ingenuity and rigorous evaluation. The future of our culture—how we communicate, work, shop, play games, and take care of ourselves—necessarily intersects with the future of science and technology. Daydreaming alone won’t bring new ideas to market; ideas require process engineering and budgeting before they can become tangible. However, too much emphasis on logic and linear thinking will kill moonshots while they’re still on the whiteboard. That is why it’s important to afford equal treatment to each hemisphere, alternating between broad creative thinking and more pragmatic, analytical assessment. When executed completely, the forces are balanced, allowing for innovation while ensuring a check-and-balance system for the future.