Prophets of the 20th century

The Napoleon of Notting Hill (G. K. (Gilbert Keith) Chesterton and W. Graham (Walford Graham) Robertson)
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Thus, for instance, there were Mr. H. G. Wells and others, who thought that science would take charge of the future; and just as the motor-car was quicker than the coach, so some lovely thing would be quicker than the motor-car; and so on for ever.

Prophets of the 20th century

The Napoleon of Notting Hill (G. K. (Gilbert Keith) Chesterton and W. Graham (Walford Graham) Robertson)
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But the way the prophets of the twentieth century went to work was this. They took something or other that was certainly going on in their time, and then said that it would go on more and more until something extraordinary happened.

Reflections on Computer Science, Hopcroft (J E Hopcroft)

Reflections on Computer Science, Hopcroft (J E Hopcroft)
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A deeper understanding of the process of knowledge fusion will be achieved in the near future, and this will have a significant impact on computer science. Humans instantaneously comprehend images; when we see a tree, we immediately recognize it as one. Since trees come in an infinite variety of 10 HOPCROFT shapes, it is obvious that our recognition does not depend upon geometrical pattern-matching. Moreover, we recognize trees we have never seen before. What makes a tree a tree is not its shape and texture alone, but something more intrinsic. Our recognition of the tree involves the fusion of many bits of information, some that support the decision for treeness and some that do not. By some as yet unknown technique, humans immediately combine various sensory data to arrive at a correct decision. This combination algorithm must be far from the bottom-up methods of object recognition Annu. Rev. Comput. Sci. 1990.4:1-12. Downloaded from www.annualreviews.org by 189.12.124.179 on 10/30/11. For personal use only. used in computer vision systems today, which identify features such as edges and corners and try to combine these features into more complex patterns. Somehow the human computer possesses top-down information about what is most likely in view. (For example, if one is outdoors, an object is more likely to be a tree than if one is indoors.) The human computer simultaneously processes bottom-up information about what it is seeing and top-down information about what it expects to see; then it fuses these two computations. The fusion probably occurs at many differ ent levels rather than at a single interface. Our recognition processes are rarely fooled; and when they are, the error is usually only momentary. For example, when looking out a train window, we may believe that our train is moving when a train on an adjacent track starts to move. However, since additional information does not jibe with this belief our brain immediately corrects its impression. No computer program yet written has capabilities so advanced, but I foresee developments like this in the near future.

Physical particles are unmotivated

A Note On Mathematical Economics
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In physics, the facts of nature are given to us. They may be broken down into their simple elements in the laboratory and their movements observed. On the other hand, we do not know the laws explaining the movements of physical particles; they are unmotivated. In economics, however, the conditions are almost reversed. Here we know the cause, for human action, unlike the movement of stones, is motivated.

Study Guide to the Theory of Money and Credit (Robert P. Murphy)

Study Guide to the Theory of Money and Credit (Robert P. Murphy)
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The important point is not whether such redemption were a legal requirement, but merely whether in practice people expected the option to be available upon demand.

Study Guide to the Theory of Money and Credit (Robert P. Murphy)

Study Guide to the Theory of Money and Credit (Robert P. Murphy)
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Thus the green pieces of paper appear to become money by “fiat” (i.e., command) of the U.S. government.

Contos Fluminenses
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— Vossa Excelência disse agora uma falsidade.
— Qual foi?
— Disse que lhe era agradável a minha conversa. Ora, isso é falso como tudo quanto é falso...
— Quer um elogio?
— Não, falo franco. Eu nem sei como Vossa Excelência me atura; desabrido, maçante, chocarreiro, sem fé em coisa alguma, sou um conversador muito pouco digno de ser desejado.

Contos Fluminenses

Contos Fluminenses
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— Tá, tá, tá. Cala a boca.

Excel

The Black Swan (Nassim Nicholas Taleb)
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In the not too distant past, say the precomputer days, projections remained vague and qualitative, one had to make a mental effort to keep track of them, and it was a strain to push scenarios into the future. It took pencils, erasers, reams of paper, and huge wastebaskets to engage in the activity. Add to that an accountant’s love for tedious, slow work. The activity of projecting, in short, was effortful, undesirable, and marred with self-doubt. But things changed with the intrusion of the spreadsheet. When you put an Excel spreadsheet into computer-literate hands you get a “sales projection” effortlessly extending ad infinitum!

The Black Swan (Nassim Nicholas Taleb) The Black Swan (Nassim Nicholas Taleb)

The Black Swan (Nassim Nicholas Taleb)
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The most interesting test of how academic methods fare in the real world was run by Spyros Makridakis, who spent part of his career managing competitions between forecasters who practice a “scientific method” called econometrics—an approach that combines economic theory with statistical measurements. Simply put, he made people forecast in real life and then he judged their accuracy. This led to the series of “M-Competitions” he ran, with assistance from Michele Hibon, of which M3 was the third and most recent one, completed in 1999. Makridakis and Hibon reached the sad conclusion that “statistically sophisticated or complex methods do not necessarily provide more accurate forecasts than simpler ones.”

Driving skills

The Black Swan (Nassim Nicholas Taleb)
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We feel responsible for the good stuff, but not for the bad. This causes us to think that we are better than others at whatever we do for a living. Ninety-four percent of Swedes believe that their driving skills put them in the top 50 percent of Swedish drivers;

Não vale criticar os modelos.

The Black Swan (Nassim Nicholas Taleb)
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You invoke the outlier. Something happened that was outside the system, outside the scope of your science. Given that it was not predictable, you are not to blame. It was a Black Swan and you are not supposed to predict Black Swans. Black Swans, NNT tells us, are fundamentally unpredictable (but then I think that NNT would ask you, Why rely on predictions?). Such events are “exogenous,” coming from outside your science. Or maybe it was an event of very, very low probability, a thousand-year flood, and we were unlucky to be exposed to it. But next time, it will not happen. This focus on the narrow game and linking one’s performance to a given script is how the nerds explain the failures of mathematical methods in society. The model was right, it worked well, but the game turned out to be a different one than anticipated.

Outlook for 20XX

The Black Swan (Nassim Nicholas Taleb)
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Worse yet, many financial institutions produce booklets every year-end called “Outlook for 200X,” reading into the following year. Of course they do not check how their previous forecasts fared after they were formulated. The public might have been even more foolish in buying the arguments without requiring the following simple tests—easy though they are, very few of them have been done. One elementary empirical test is to compare these star economists to a hypothetical cabdriver (the equivalent of Mikhail from Chapter 1): you create a synthetic agent, someone who takes the most recent number as the best predictor of the next, while assuming that he does not know anything. Then all you have to do is compare the error rates of the hotshot economists and your synthetic agent. The problem is that when you are swayed by stories you forget about the necessity of such testing.

Contos Fluminenses

Contos Fluminenses
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Nesse momento entrou Diogo.
— Ia sair, D. Emília? perguntou ele.
— Esperava o seu braço.
— Aqui o tem.

Contos Fluminenses

Contos Fluminenses
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— Do amor, dizia ele, eu só sei que é uma palavra de quatro letras, um tanto eufônica, é verdade, mas núncia de lutas e desgraças. Os bons amores são cheios de felicidade, porque têm a virtude de não alçarem olhos para as estrelas do céu; contentam-se com ceias à meia-noite e alguns passeios a cavalo ou por mar.

Ideas are sticky

The Black Swan (Nassim Nicholas Taleb)
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The problem is that our ideas are sticky: once we produce a theory, we are not likely to change our minds—so those who delay developing their theories are better off.

The Black Swan (Nassim Nicholas Taleb) The Black Swan (Nassim Nicholas Taleb)

The Black Swan (Nassim Nicholas Taleb)
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Show two groups of people a blurry image of a fire hydrant, blurry enough for them not to recognize what it is. For one group, increase the resolution slowly, in ten steps. For the second, do it faster, in five steps. Stop at a point where both groups have been presented an identical image and ask each of them to identify what they see. The members of the group that saw fewer intermediate steps are likely to recognize the hydrant much faster. Moral? The more information you give someone, the more hypotheses they will formulate along the way, and the worse off they will be.

The Black Swan (Nassim Nicholas Taleb) The Black Swan (Nassim Nicholas Taleb)

The Black Swan (Nassim Nicholas Taleb)
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If you study Onassis’s life, which I spent part of my early adulthood doing, you would notice an interesting regularity: “work,” in the conventional sense, was not his thing. He did not even bother to have a desk, let alone an office. He was not just a dealmaker, which does not necessitate having an office, but he also ran a shipping empire, which requires day-to-day monitoring. Yet his main tool was a notebook, which contained all the information he needed. Onassis spent his life trying to socialize with the rich and famous, and to pursue (and collect) women. He generally woke up at noon. If he needed legal advice, he would summon his lawyers to some nightclub in Paris at two A.M. He was said to have an irresistible charm, which helped him take advantage of people.