Learning to think probabilistically is one of the most critical skills one can master. Nate Silver’s The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t is a valuable book on thinking probabilistically and forecasting in an uncertain environment. It compares and contrasts examples across multiple disciplines, including weather forecasting, seismology, finance, and more.
This book pairs well with Against the Gods, Fortune’s Formula and Superforecasting. Against the Gods is in my opinion, the most important book on the development of probabilistic thinking. Early civilizations were good with geometry and logic, but helpless with uncertainty. Ironically it was gamblers and heretics who moved mankind forward by developing the science of probability, statistics, and ultimately risk management. Fortune’s Formula shows the connection between information theory, gambling, and correct position sizing for investors. It helps the answer the question: when you have a slight edge, how much should you bet? Nate Silver draws heavily on Superforecasting. Particularly important is the idea of “foxes and hedgehogs”. Foxes are multidisciplinary, adaptable, self critical , tolerant of complexity, cautious and empirical. In contrast, Hedgehogs are specialized, stalwart, stubborn, order-seeking, confident, and ideological. As you might expect, foxes make far better forecasters than hedgehogs, even though hedgehogs make for better television.
Anyways, here are a few key insights from my notes on The Signal and the Noise
1) Data is useless without context.
There are always patterns to find in data, but its critical to understand the theory behind the system you are studying to avoid being fooled by noise. This is true in forecasting the weather, investing, betting on sports, or any other probabilistic endeavor. The ability to understand context is also a critical advantage humans have over computer programs.
“Statistical inferences are much stronger when backed up by theory or at least some deeper thinking about their root causes. “
The importance of understanding context comes to the forefront when you compare human’s success with weather forecasting, vs relative failure with earthquake forecasting.
“Chaos theory is a demon that can be tamed- weather forecasts did so, at least in part. But weather forecasters have a much better theoretical understanding of th earth’s atmosphere than seismologists do of the earth’s crust. They know more or less, how weather works, right down to the molecular level. Seismologists don’t have that advantage. “
The ability to understand context is what separates success from failure in all pursuits dealing with uncertainty. The profile of professional sports gambler Bob Voulgaris, is highly instructive. Voulgaris focuses on NBA basketball. A key insight is that Voulgaris has powerful tools for analyzing data, and he makes good use of the data, but he also has deep understanding of the qualitative subletities of how NBA basketball works. Obvious statistical patterns are quickly incorporated into betting lines, whether they are signal or noise. Voulgaris looks deeper, and finds places where the line misprices true probabilities.
“Finding patterns is easy in any data rich environment; thats what mediocre gamblers do. The key is in determining whether the patterns represent noise or signal. “
2) Beware of overconfidence
“… the amount of confidence someone expresses in a prediction is not good indication of its accuracy, to the contrary, these qualities are often inversely correlated. “
3) Think big, and think small. Mix the macro and the micro.
“Good innovators typically think very big, and they think very small. New ideas are sometimes found in the most granular of details where few others bother to look. And they are sometimes found when you are doing your most abstract and philosophical thinking, considering why the world is the way that it is and whether there might be an alternative to the dominant paradigm.”
This is reminiscent of the “global micro” approach used by several manager’s profiled in Inside the House of Money: Top Hedge Fund Traders on Profiting in the Global Markets
4) Recognize the Value of Bayesian Thinking
The work of Thomas Bayes forms the framework underlying how good gamblers think.
Bayes was an English minister who argued in his theological work that admitting our own imperfections is a necessary step on the way to redemption. His most famous work, however, was “An Essay toward Solving a Problem in the Doctrine of Chances,” which was not published until after his death. One interpretation of the essay concerns a person who emerges into the world( ie Adam, or someone from Plato’s cave), and rises to see the sun for the first time:
“At first the does not know whether this is typical of some sort of freak occurrence. However each day that he survives and the sun rises again, his confidence increases that it is a permanent feature of nature. Gradually, through this purely statistical form of inference, the probability that he assigns to his prediction that the sun will rise again tomorrow approaches(although never exactly reaches) 100 percent.”
In essence, beliefs on probability are updated as new information comes in.
Ironically Bayes philosophical work was extended by the mathematician and astronomer Pierre Simon-Laplace, who was likely an atheist. Although Laplace believed in scientific determinism, he was frustrated with the disconnect between (what he believed to be the perfection of nature, and human imperfections in understanding it, in particular with regards to astronomical observations. Consequently, he developed some measuring techniques that relied on probabilistic inferences, rather than exact measurements. “Laplace came to view probability as a waypoint between ignorance and knowledge.” The combined work of Laplace and Bayes led to simple expression that is concerned with conditional probability. In essence Bayesian math can be used to tell us the probability that a theory or hypothesis if some event has happened.
5) The road to wisdom is to be less and less wrong.
forecasting, or at least operating in an uncertain environment, is an iterative process.
Nate Silver titles one of the chapters “Less and Less Wrong, as a homage to the Danish mathematician, scientist, inventor, and poet Piet Hein, author of Grooks:
The road to wisdom? — Well, it’s plain
and simple to express:
and err again
George Soros treats developments in financial markets as a historical process. In The Alchemy of Finance, he outlines his theory of reflexivity, discusses historical developments in markets, and describes a real time “experiment” he undertook while running the Quantum fund in the 1980s.
Markets are an ideal laboratory for testing theories: changes are expressed in quantitative terms, and the data are easily accessible.
Three of the key interrelated concepts in his framework, are anti-equilibrium, Imperfect Knowledge, and Reflexivity.
In markets, equilibrium is a very rare special case. Further, adjustments rarely lead to new equilibrium. The economy is always in adjustment.
According to George Soros:
If we want to understand the real world we must divert our gaze from a hypothetical final outcome , and concentrate our attention on the process of change that we observe all around us.
In trying to deal with macroeconomic developments, equilibrium analysis is totally inappropriate. Nothing could be further removed from reality than the assumptions that the participants base their decisions on perfect knowledge. People are groping to anticipate the future with the help of whatever guideposts they can establish. The outcome tends to diverge from expectations, leading to constantly changing expectations, and constantly changing outcomes. The process is reflexive.
The stock market, is of course a perfect example:
The concept of an equilibrium seems irrelevant at best and misleading at worst. The evidence shows persistent fluctuations, whatever length of time is chosen as the period of observation. Admittedly, the underlying conditions that are supposed to be reflected in stock prices are also constantly changing, but it is difficult to establish any firm relationship between changes in stock prices and changes in underlying conditions. Whatever relationship can be established has to be imputed rather than observed.
So its better to focus on nature and direction of ongoing adjustments, rather than trying to identify an equilibrium.
Perhaps more problematic with an exclusive focus on rarely occurring equilibrium conditions is the assumption of perfect knowledge. Perfect knowledge is impossible. Everything is a provisional hypothesis, subject to improvement. Soros makes the bias of market participants the center part of his analysis.
In natural sciences, usually the thinking of participants and the events themselves can be separated. However, when people are involved, there is interplay between thoughts and actions. There is a partial link to Heisenberg’s uncertainty principle. The basic deductive nomological approach of science is inadequate. Use of probabilistic generalization, or some other novel scientific method is preferable.
Thinking plays a dual role. On the one hand, participants seek to understand the situation in which they participate; on the other, their understanding serves as the basis of decisions which influence the course of the events. The two roles interfere with each other.
The influence of this idea is inseparable from the theory of imperfect knowledge.
The participants’ perceptions are inherently flawed, and there is a two-way connection between flawed perceptions and the actual course of events, which results in a lack of correspondence between the two.
This two way connection is what Soros called “reflexivity.”
The thinking of participants, exactly because it is not governed by reality, is easily influenced by theories. In the field of natural phenomena, scientific method is effective only then its theories are valid, but in social political , and economic matters, theories can be effective without being valid.
Effective here, means having an impact. For example, in a bubble, the cost of capital for some companies drops to be absurdly low, relative to the risk of their respective enterprises. Consequently, some businesses that would have otherwise died, may go on to survive. (Example from two decades after the Alchemy of Finance was written: Peter Thiel mentions when being interviewed in Inside the House of Money, that Paypal did a massive capital raise right a the height of the tech bubble, even though it didn’t need the money at the time) On the flip side, a depression can be self fulfilling, if businesses are unable to refinance.
This seems to be especially true in the credit markets:
Loans are based on the lender’s estimation of the borrowers ability to service his debt. The valuation of the collateral is supposed to be independent of the act of lending; but in actual fact the act of lending can affect the value of the collateral. This is true of the individual case and of the economy as a whole. Credit expansion stimulates the economy and enhances the collateral values; the repayment or contraction of credit has a depressing influence both on the economy and on the valuation of collateral. The connection between credit and economy activity is anything but constant- for instance , credit for building a new factory has quite a different effect from credit for a leveraged buyout. This makes it difficult to quantify the connection between credit and economic activity. Yet it is a mistake to ignore it.
This is reminiscent of Hyman Minsky’s Financial Instability Hypothesis
In terms of the stock market, Soros asserts (1)Markets are always biased in one direction or another. (2) Markets can influence the events that they anticipate.
In The Landscape of History: How Historians Map the Past, John Gaddis discusses the methods that historians use, comparing them to methods of sciences such as astronomy, paleontology, and geology. The book is a great exploration of different approaches to examining evidence. Here are a few key lessons.
Since we’re historians, not novelists , we’re obliged to tier our narrative as closely as possible to the evidence that has survived: that’s an inductive process. But we have no way of knowing, until we begin looking for evidence with the purposes of our narrative in mind, how much of its going to be relevant: that’s a deductive calculation. Composing the narrative will then produce places where more research is needed, and we’re back to induction again. But that new evidence will still have to fit within the modified narrative so we’re back to deduction. And so on until, as I earlier quoted William H. McNeill, “it feels right, and then I write it up and ship it off to the publisher.” That’s why the distinction between induction and deduction is largely meaningless for the historian seeking to establish causation. The verb “to fit” which implies both procedures, is much better. Its not just tailors who look at what they have to cover, and then what they have with which to cover it, and then back and forth, again and again, until the fit is as good as its going to get.
Biography, like the larger sphere of history within which it resides is at once a deductive and an inductive exercise. Patterns of human behavior extending across time and space can alert us to the kinds of questions we should be asking about the particular individual we’re dealing with: that’s where deduction comes in.…But these patterns alone can’t determine the answers, for it’s all to easy to to find what you’re looking for when you’ve already decided a head of time what it is. The evidence of particular experience in biography has got to discipline what we know from collective experience: induction is how we do that.
Theories like relativity, plate tectonics, and natural selection emphasize relationships among variables, some of them continuous and others contingent. Regularity and randomness coexist within such theories: they allow for punctuations that upset equilibria such as asteroid impacts, earthquakes, or the outbreak of new and lethal diseases. Nor do they require singling out certain variables as more important than others.Nor do they require singling out certain variables as more important than others: what would the independent variables be for the Andromeda galaxy or the Norwegian coastline, or the Darwin finch? Reductionism in these realms is only a stepping stone towards synthesis.
Historians,however, reject the doctrine of immaculate causation, which seems to be implied in the idea that one can identify, without reference to all that has preceded it, such a thing as an independent variable. Causes always have antecedents. We may rank their relative significance, but we’d think it irresponsible to seek to isolate or “tease out” single causes for complex events. We see history as proceeding instead from multiple causes and their intersections. Interconnections matter more to us than does the enshrinement of particular variables.
Cal Newport defines Deep Work as activities performed in a state of distraction-free concentration that push cognitive capabilities to the limit. Deep Work is essential in order to quickly master new things quickly, and to produce at an elite level in terms of both quality and speed.
Cal Newport quotes A.G. Sertillanges, a Dominican friar and professor of moral philosophy who wrote in The Intellectual Life
Men of genius themselves were great only by bringing all their power to bear on the point on which they had decided to show their full measure.
Winifred Gallagher concludes in the book Rapt: Attention and the Focused Life that management of attention is the key to improving nearly every aspect of existence. Realizing this is the easy part. The hard part is figuring out how to fit Deep Work into a busy schedule. Newport outlines 4 applicable philosophies for fitting deep work into the demands of a modern schedule.
1) The Monastic Philosophy
This is available to a limited pool of people, mainly tenured professors, and successful authors. Examples include computer scientist Donald Knuth and science fiction writer Neal Stephenson, who both go to extreme lengths to eliminate shallow tasks and communication.
2) The Bimodal Philosophy
Practically speaking, this is about taking a proper holiday, or carefully blocking off certain days for different kinds of work. It need not be long. Carl Jung, on several key occasions in the 1920s retreated to a house in the woods in order to work on writing, but spent most of his time to living a very active social life in Zurich. Adam Grant, a famous business school professor is, is very active with university responsibilities most of the time, but when working on a book, he’ll cut himself off from most office communication for 2-4 day periods.
3) The Rhythmic Philosophy
This is perhaps the most practical method for most people. Basically, it means creating a simple regular habit of deep work. Combine a simple scheduling heuristic, and an easy way to keep track.
One suggested method is to get up 90 minutes earlier and spend the extra time on deep work(this happens to be the method used by yours truly to get more reading/writing/coding done )
4) The Journalistic Philosophy
This seems like it could be combined with the rhythmic philosophy. In essence it means getting deep work in whenever you can fit it. It does require strong attention discipline(but like muscles, this can be trained). Walter Isaacson managed to write his first 800+ page book while working full time as a journalist using this method. (us mere mortal can use noise cancelling headphones as an aid in applying the journalistic philosophy of deep work)
Newport also offers two suggestions for ramping up the amount of deep work one does.
- Beware of distractions and looping. This is when the brain wanders into unrelated issues when it should be focused on a critical task. Newport used the example of when his brain would rehash preliminary results over and over again when he was trying to work on a proof.
- Structure deep thinking. Identify relevant variables, define the specific next step questions, and once it is solved , consolidate the gains by reviewing the identified answer. Approach problem solving methodically.
In the classic book Influence, Robert Cialdini outlines six principles(reciprocation, liking, social proof, authority, scarcity and consistency) that represent psychological universals in persuasion. In general, all these persuasive techniques exploit people’s heuristics. The proliferation of information in this digital age means people need to rely more on heuristics than ever before. This has broad and deep consequences.
“Because technology can evolve much faster than we can, our natural capacity to process information is likely to be increasingly inadequate to handle the surfeit of change, choice, and challenge that is characteristic of modern life. More and more frequently we will find ourselves in the position of the lower animals, – with a mental apparatus that is unequipped to deal thoroughly with the intricacy and richness of the outside environment. “ Influence
One of the thirteen laws of Data Smog outlined by David Shena is Cialdini’s Law: Though culture moves much more swiftly than evolution, it cannot change the pace of evolution. This of course leads to a dangerous situation, where the unwary can be tricked into making dumb decisions. Worse yet are the broader societal consequences.
“In the electronic age, a good lie well-told can zip around th world and back in a matter of seconds while the truth is trapped, buried under a filing cabinet full of statistics.” –Data Smog
Cialdini’s follow up book, Pre-Suasion discusses how persuasiveness can be enhanced by carefully crafting what is done and said before making a request. Information overload also makes people more susceptible to the “presuasive“ techniques:
“…(1)what is more accessible in the mind becomes more probable in action, and (2)accessibility is influenced by the informational cues around us, and our raw associations to them….
… In addition to its time-challenged character, other aspects of modern life undermine our ability (and motivation) to think in a fully reasoned way about even important decisions. The sheer amount of information today can be overwhelming- its complexity befuddling, its relentlessness depleting, its range distracting, and its prospects agitating. Couple those culprits with with the concentration-disrupting alerts of devices nearly everyone now carries to deliver that input, and careful assessments role as a ready decision-making corrective becomes sorely diminished. Thus a communicator who channels attention to a particular concept in order to heighten audience receptivity to a forthcoming message- via the focus-based, automatic, crudely associative mechanisms of pre-suasion- won’t have to worry much about the tactics being defeated by deliberation. The calvary of deep analysis will rarely arrive to reverse the outcome because it will rarely be summoned.” –Pre-Suasion
Summon the calvalry of deep analysis
What can one do about this?” Hueristics are necessary to function in the modern world, but they must be examined from time to time. The calvary of deep analysis must be summoned for big decisions. Cialdini also recommends forceful counters assault. Recognize the tricks being employed are often enough to blunt their force, but in other cases it may be necessary to aggressively fight against the tricks. These books are a great place to start.
Trust Me, I’m Lying: Confessions of a Media Manipulator exposes the twisted incentive system that makes the media susceptible to manipulation, and the boiler room environment in which much of the “news” is manufactured. The book outlines tricks used to steal people’s time and attention. while serving some other agenda. By understanding the logic behind business choices that the media makes, readers can better predict and anticipate actions(some might even be able to use the book to redirect, accelerate and control stories). It was written back in 2012, but after reading, it makes sense that clickbait could help swing an election.