# Investing: The Last Liberal Art: 

Table of Contents
 
 - [[Chapter 1: A Latticework of Mental Models]]
 - [[Chapter 2: Physics]]
 - [[Chapter 3: Biology]]
 - [[Chapter 4: Sociology]]
 - [[Chapter 5: Psychology]]
 - [[Chapter 6: Philosophy]]
 - [[Chapter 7: Literature]]
 - [[Chapter 8: Mathematics]]
 - [[Chapter 9: Decision Making]]
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# Chapter 1: A Latticework of Mental Models

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1. “Benjamin Franklin’s success as an educator was based upon three standing principles. First the student must acquire the basic skill sets: reading, writing, arithmetic, physical education, and public speaking. Then the student was introduced to the bodies of knowledge, and finally the student was taught to cultivate habits of mind by discovering the connections that exist between the bodies of knowledge.”
2. What is often lacking is his third principle: the “habits of mind” that seek to link together different bodies of knowledge.
3. Cultivating Franklin's 'habits of mind' is the key to achieving Charlie Munger's 'worldly wisdom'. The key is finding the linkages that connect one idea to another. Fortunately, the human mind already works this way. 
4. We do not learn new subjects because we have somehow become better learners but because we have become better at recognizing patterns.
5. According to Holland, innovative thinking requires us to master two important steps. First, we must understand the basic disciplines from which we are going to draw knowledge; second, we need to be aware of the use and benefit of metaphors. Innovative thinking, which is our goal, most often occurs when two or more mental models act in combination.
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# Chapter 2: Physics

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1. Our ability to answer even the most fundamental aspects of human existence depends largely upon measuring instruments available at the time and the ability of scientists to apply rigorous mathematical reasoning to the data.
2. Every complex adaptive system is actually a network of many individual agents all acting in parallel and interacting with one another. The critical variable that makes a system both complex and adaptive is the idea that agents (neurons, ants, or investors) in the system accumulate experience by interacting with other agents and then change themselves to adapt to a changing environment. No thoughtful person, looking at the present stock market, can fail to conclude that it shows all the traits of a complex adaptive system. And this takes us to the crux of the matter. If a complex adaptive system is, by definition, continuously adapting, it is impossible for any such system, including the stock market, ever to reach a state of perfect equilibrium. 
3. What does that mean for the market? It throws the classic theories of economic equilibrium into serious question. The standard equilibrium theory is rational, mechanistic, and efficient. It assumes that identical individual investors share rational expectations about stock prices and then efficiently discount that information into the market. It further assumes there are no profitable strategies available that are not already priced into the market. 
4. The counterview from Santa Fe suggests the opposite: a market that is not rational, is organic rather than mechanistic, and is imperfectly efficient. It assumes the individual agents are, in fact, irrational and hence will misprice securities, creating the possibility for profitable strategies.
5. In an environment of complexity, simple laws are insufficient to explain the entire system.
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# Chapter 3: Biology

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1. Turning to biology for insight into finance and investing may at first seem a startling move, but just as we did in our study of physics, we focus here on just one core idea from the field of biology: evolution. Whereas in nature the process of evolution is one of natural selection, seeing the market within an evolutionary framework allows us to observe the law of economic selection.
2. The originality of Darwin’s theory lay in the idea that the struggle for survival was occurring not only between species but between individuals within the same species. If having a longer beak, for example, increased a bird’s chances of survival, then more birds with long beaks would be more likely to pass this advantage on. Eventually, the longer beak would become dominant within the species.3 By this process of natural selection, Darwin theorized, favorable variations are preserved and transmitted to succeeding generations. After several generations, small gradual changes in the species begin to add up to larger changes—thus, evolution occurs.
3. “The central point of his whole life work is that capitalism can only be understood as an evolutionary process of continuous innovation and creative destruction.”
4. According to Kuhn, when an observed phenomenon is not adequately explained by the dominant paradigm, a new competing paradigm is born. Scientists left with an ineffectual model go to work on developing a new theoretical outline. Although you might think the transition from old paradigm to new is peacefully led by the collective who are in the pursuit of truth, Kuhn tells us just the opposite happens—hence the term “revolution.”
5. Unshackling themselves from the classical teachings, the Santa Fe group was able to point out four distinct features they observed about the economy. 
    - 1.  Dispersed interaction: What happens in the economy is determined by the interactions of a great number of individual agents all acting in parallel. The action of any one individual agent depends on the anticipated actions of a limited number of agents as well as on the system they cocreate. 
    - 2.  No global controller: Although there are laws and institutions, there is no one global entity that controls the economy. Rather, the system is controlled by the competition and coordination between agents of the system. 
    - 3.  Continual adaptation: The behavior, actions, and strategies of the agents, as well as their products and services, are revised continually on the basis of accumulated experience. In other words, the system adapts. It creates new products, new markets, new institutions, and new behavior. It is an ongoing system. 
    - 4.  Out-of-equilibrium dynamics: Unlike the equilibrium models that dominate the thinking in classical economics, the Santa Fe group believed the economy, because of constant change, operates far from equilibrium.
6. An essential element of complex adaptive systems is a feedback loop. That is, agents in the system first form expectations or models and then act on the basis of predictions generated by these models. But over time these models change depending on how accurately they predict the environment. If the model is useful, it is retained; if not, the agents alter the model to increase its predictability. Obviously, accuracy of predictability is a paramount concern to participants in the stock market, and we may be able to achieve broader understanding if we can learn to view the market as one type of complex adaptive system. 
7. The whole notion of complex systems is a new way of seeing the world, and it is not easily grasped. How exactly do agents in complex adaptive systems interact? How do they go about collectively creating, and then changing, a model for predicting the future? For those of us who are not scientists, finding a way to visualize the process is helpful. Brian Arthur gives us an answer with an example he dubbed “the El Farol Problem.” 
8. El Farol, a bar in Santa Fe, New Mexico, used to feature Irish music on Thursday nights. Arthur, the Irishman, loved to go there. On most occasions, the bar patrons were well behaved, and it was enjoyable to sit and listen to the music. But on some nights, the bar was packed with so many people crammed together drinking and singing that the scene became unruly. Now Arthur was confronted with a problem: How could he decide which nights to go to El Farol and which nights to stay home? The chore of having to decide led him to formulate a mathematical theory he named the El Farol Problem. It has, he says, all the characteristics of a complex adaptive system. 
9. Suppose, says Arthur, there are one hundred people in Santa Fe who are interested in going to El Farol to listen to Irish music, but none of them wants to go if the bar is going to be crowded. Now also suppose the bar published its weekly attendance for the past ten weeks. With this information, the music lovers will build models to predict how many people will show up next Thursday. Some may figure that it will be approximately the same number of people as last week. Others will take an average of the last few weeks. A few will attempt to correlate attendance data to the weather or to other activities for the same audience. There will be endless ways to build models to predict how many people will go to the bar. 
10. Now let’s say that every lover of Irish music decides that the comfort level in the small bar is sixty people. All one hundred people will decide, using whatever predictor has been the most accurate over the last few weeks, when the limit is going to be reached. Because each person has a different predictor, on any given Thursday some people will turn up at El Farol and others will stay home because their model has predicted more than sixty people will be attending. The following day, El Farol publishes its attendance and the hundred music lovers will update their models and get ready for next week’s prediction. 
11. The El Farol process can be termed an ecology of predictors, says Arthur. At any point, there is a group of models that are deemed “alive”—that is, they are useful predictors of how many people will attend the bar. Conversely, predictors that turn out to be inaccurate will slowly die off. Each week, new predictors, new models, new beliefs will compete for use by other music lovers. 
12. We can quickly see how the El Farol process echoes the Darwinian idea of survival through natural selection and how logically it extends to economies and markets. In the markets, each agent’s predictive models compete for survival against the models of all other agents, and the feedback that is generated causes some models to be changed and others to disappear. It is a world, says Arthur, that is complex, adaptive, and evolutionary.
13. In a Santa Fe Institute paper titled “Market Force, Ecology, and Evolution,” Farmer has taken the important first step in outlining the behavior of the stock market in biological terms. His analogy between a biological ecology of interacting species and a financial ecology ecology of interacting strategies is summarized in the table shown here.
14. Farmer is the first to admit the analogy is not perfect, but it does present a stimulating way in which to think about the market. Furthermore, it links the process to clearly defined science of how living systems behave and evolve. If we go back through the history of the stock market and seek to identify the trading strategies that dominated the landscape, I believe there have been five major strategies, (which in Farmer’s analogy would be species). 
    - 1.  In the 1930s and 1940s, the discount-to-hard-book value strategy, first proposed by Benjamin Graham and David Dodd in their classic 1934 textbook Security Analysis, was dominant. 
    - 2.  After World War II the second major strategy that dominated finance was the dividend model. As the memories of the 1929 market crash faded and prosperity returned, investors were increasingly attracted to stocks that paid high dividends, and lower-paying bonds lost favor. So popular was the dividend strategy that by the 1950s, the yield on dividend-paying stocks dropped below the yield of bonds—a historical first.
    - 3.  By the 1960s, a third strategy appeared. Investors exchanged stocks paying high dividends for companies that were expected to grow their earnings at a high rate. 
    - 4.  By the 1980s, a fourth strategy took over. Warren Buffett stressed the need to focus on companies with high “owner-earnings” or cash flows. 
    - 5.  Today we can see that cash return on invested capital is emerging as the fifth new strategy.
15. Most of us easily recognize these well-known strategies, and we can readily accept the idea that each one gained favor by overtaking a previously dominant strategy and was then itself eventually overtaken by a new strategy. In a word, evolution took place in the stock market via economic selection. How does economic selection occur? Remember that in Farmer’s analogy, a biological population is capital and natural selection occurs by capital allocation. This means capital varies in relation to the popularity of the strategy. If a strategy is successful, it attracts more capital and becomes the dominant strategy. When a new strategy that works is discovered, capital is reallocated—or, in biological terms, there is a change in population. As Farmer notes, “The long-term evolution of the market can be studied in terms of flows of money. Financial evolution is influenced by money in much the same way that biological evolution is influenced by food.”
16. Why are financial strategies so diverse? The answer, Farmer believes, starts with the idea that basic strategies induce patterns of behavior. Agents rush in to exploit these obvious patterns, causing an ultimate side effect. As more agents begin using the same strategy, its profitability drops. The inefficiency becomes apparent, and the original strategy is washed out. But then new agents enter the picture with new ideas. They form new strategies of which any number may become profitable. Capital shifts and the new strategy explodes, which starts the evolutionary process again. It is the classic El Farol Problem described by Brian Arthur. 
17. Will the market ever become efficient? If you accept the idea that evolution plays a role in financial markets the answer would have to be no. Each strategy that eliminates an inefficiency will soon be replaced in turn by a new strategy. The market will always maintain some level of diversity, and this we know is a principal cause of evolution. 
18. What we are learning is that studying economic and financial systems is very similar to studying biological systems. The central concept for both is the notion of change, what biologists call evolution. The models we use to explain the evolution of financial strategies are mathematically similar to the equations biologists use to study populations of predator-prey systems, competing systems, or symbiotic systems.
19. Indeed, the movement from the mechanical view of the world to the biological view of the world has been called the “second scientific revolution.” After three hundred years, the Newtonian world, the mechanized world operating in perfect equilibrium, is now the old science. The old science is about a universe of individual parts, rigid laws, and simple forces. The systems are linear: Change is proportional to the inputs. Small changes end in small results, and large changes make for large results. In the old science, the systems are predictable. 
20. The new science is connected and entangled. In the new science, the system is nonlinear and unpredictable, with sudden and abrupt changes. Small changes can have large effects while large events may result in small changes. In nonlinear systems, the individual parts interact and exhibit feedback effects that may alter behavior. Complex adaptive systems must be studied as a whole, not in individual parts, because the behavior of the system is greater than the sum of the parts.
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# Chapter 4: Sociology

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1. But man is a complex being, and those who would understand human behavior must find a way to work within the complexity. Fortunately, guidance is at hand in the scientific area of inquiry known as complexity theory.
2. We have come to understand that economies and stock markets are adaptive systems. As such, their behavior constantly changes as individuals in the system interact with other individuals and within the system itself.
3. Self-organized systems, explains Johnson, have three distinct characteristics. First, the complex global behavior occurs by simple connected local processors. In a social system, the local processors are individuals. Second, a solution arises from the diversity of the individual inputs. Third, the functionality of the system, its robustness, is far greater than any one of the individual processors. Johnson believes that the symbiotic combination of humans and networks (Internet) will generate, in a collective, far better results that any one individual can do acting alone. He envisions an “unprecedented capability in organizational and societal problem solving will result from increased human activity on smart distributed information systems.”
4. One of the great advantages of the Internet is how it helps us manage information; in this, explains Johnson, the Internet has three significant advantages over prior systems. First, it is able to integrate a wide breadth of knowledge compared to other systems whose information was often physically separated. Second, the Internet is able to capture and display depth of information. With digitization, systems are able to produce volumes of data on a single topic without significant additional cost. Third, the Internet is able to process information correctly. As we will learn in the next chapter on psychology, communication missteps between individuals sometimes result in the loss of vital information. Information exchanged via the Internet is delivered accurately, in much the same way that books and documents are able to transmit information. It is Johnson’s belief that these three advantages, along with the interconnectivity of millions of individuals, will greatly enhance the collective problem-solving ability of self-organized systems. 
5. To illustrate the phenomenon of emergence, let’s look in on a familiar social system: an ant colony. Because ants are social insects (they live in colonies, and their behavior is directed to the survival of the colony rather than the survival of any one individual ant), social scientists have long been fascinated by their decision-making process.
6. One of the ant’s most interesting behaviors is the process of foraging for food and then determining the shortest path between the food source and the nest.3 While walking between the two, ants lay down a pheromone trail that allows them to trace the path and also show other ants the location of the new food source. 
7. At the beginning, the search for food is a random process, with ants starting out in many different directions. Once they locate food, they return to the nest, laying down the pheromone trail as they go. But now comes the very sophisticated aspect to collective problem solving: the colony, acting as a whole, is able to select the shortest path. If one ant randomly finds a shorter path between the food source and the nest, its quicker return to the nest intensifies the concentration of pheromone along the path. Other ants tend to choose the path with the strongest concentration of pheromone and hence set off on this newly discovered short path. This increased number of ants along the trail deposits even more pheromone, which further attracts more ants until this path becomes the preferred line. Scientists have been able to demonstrate experimentally that the pheromone-trail behavior of the ant colony solves for the shortest path. In other words, this optimal solution is an emergent property of the collective behavior of the ant colony. 
8. Norman Johnson, who like many is fascinated by ant behavior, set out to test humans’ ability to solve collective problems. He constructed a computer version of a maze with countless paths but only a few that are short. The computer simulation consists of two phases: a learning phase and an application phase. In the learning phase, a person explores the maze with no specific knowledge of how to solve the maze until the goal is found. This is identical to the process an ant follows when it begins to look for food. In the application phase, people simply apply what they learned. Johnson discovered that people need an average of 34.3 steps to solve the maze in the first phase and 12.8 steps in the second phase. Then, to find the collective solution, Johnson combined all the individual solutions and applied the application phase. He found that if at least five people were considered, their collective solution was better than the average individual solution. It took a collective of only twenty to find the very shortest path through the maze, even though they had no global sense of the problem. This collective solution, argues Johnson, is an emergent property of the system.
9. Although Johnson’s maze is a simple problem-solving computer simulation, it does demonstrate emergent behavior. It also leads us to better understand the essential characteristic a self-organizing system must contain in order to produce emergent behavior. That characteristic is diversity. The collective solution, Johnson explains, is robust if the individual contributions to the solution represent a broad diversity of experience in the problem at hand. Interestingly, Johnson discovered that the collective solution is actually degraded if the system is limited to only high-performing people. It appears that the diverse collective is better at adapting to unexpected changes in the structure.4 To put this in perspective, Johnson’s research suggests that the stock market, theoretically, is more robust when it is composed of a diverse group of agents—some of average intelligence, some of below-average intelligence, and some very smart—than a market singularly composed of smart agents. At first, this discovery appears counterintuitive. Today, we are quick to blame the amateur behavior of uninformed individual investors and day traders for the volatile nature of the market. But if Johnson is correct, the diverse participation of all investors, traders and speculators—smart and dumb alike—should make the markets stronger, not weaker. Another important insight from Norman Johnson was his discovery that the system, as long as it is adequately diverse, is relatively insensitive to moderate amounts of noise (by which he means any sort of discordant, disruptive activity). To prove the point, Johnson intentionally degraded an individual contribution; he learned his action had no effect on participants’ finding the shortest path out of the maze. Even at the highest levels of disruption, the collective behavior, after a brief postponement, was able to discover the minimal path. Not until the system reached its highest noise level did the collective decision-making process break down. 
10. The work of Norman Johnson appears to contradict the classical views of crowd behavior. Who is right? 
11. The answer lies in an outstanding book titled, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations.
12. According to Surowiecki, the two critical variables necessary for a collective to make superior decisions are diversity and independence. If a collective is able to tabulate decisions from a diverse group of individuals who have different ideas or opinions on how to solve a problem, the results will be superior to a decision made by a group of like-minded thinkers. 
13. Independence, the second critical variable, does not mean each member of the group must remain in isolation but rather each member of the group is basically free from the influence of other members. Independence is important to the collective decision-making process for two reasons, explains Surowiecki. “First, it keeps the mistakes that people make from becoming correlated. Errors in individual judgment won’t wreck the group’s collective judgment as long as those errors aren’t systematically pointing in the same direction. Second, independent individuals are more likely to have new information rather than the same old data everyone is familiar with. 
14. So now we come to the crossroads. Is the stock market Charles Mackay’s unruly mob of irrational investors who constantly unleash booms and busts or is it Francis Galton’s county fair attendees who can miraculously make the right prediction? The answer is context dependent. In other words, it depends.
15. In a joint paper written with two colleagues titled, “Price Variations in a Stock Market with Many Agents,” Bak defended his thesis. The three scientists constructed a very simple model that sought to capture the behavior of two types of agents operating in a stock market. They called the two types noise traders and rational agents. With apologies to the authors, I will instead use the more familiar terms of fundamentalists and trend followers. Trend followers seek to profit from changes in the market by either buying when prices go up or selling when prices go down. Fundamentalists buy and sell based not on the direction of the price changes but rather because of the difference between the price of a security and its underlying value. If the value of the stock is higher than the current price, fundamentalists buy shares; if the value is lower than the current price, they sell. 
16. Most of the time, the interplay between trend followers and fundamentalists fundamentalists is somewhat balanced. Buying and selling continue with no discernible change in the overall behavior of the market. We might say the sand pile is growing without any corresponding avalanche effects. Put differently, diversification is present in the market. 
17. But when stock prices climb, the ratio of trend followers to fundamentalists begins to grow. This makes sense. As prices increase, a larger number of fundamentalists decide to sell and leave the market and are replaced by a growing number of trend followers who are attracted to rising prices. When the relative number of fundamentalists is small, stock market bubbles occur, explained Bak, because prices have moved far above the fair price a fundamentalist would pay. Extending the sand pile metaphor further, as the number of fundamentalists in the market declines, and the relative number of trend followers increases, the slope of the sand pile becomes ever steeper, increasing the possibility of an avalanche. Once again, we can put this differently by saying that when the mix of fundamentalists and trend followers becomes unbalanced, we are heading toward a diversity breakdown. 
18. It is important for us to remember at this point that while Per Bak’s self-organizing criticality explains the overall behavior of avalanches, it does nothing to explain any one particular avalanche. When we ultimately are able to predict the behavior of individual avalanches, it will not be because of self-organized criticality but because of some other science yet to be discovered. 
19. That in no way diminishes the significance of Bak’s ideas. Indeed, several notable economists have acknowledged Per Bak’s work on self-organized criticality as a credible explanation for how complex adaptive systems behave, including the Nobel physics laureate Phil Anderson and the Santa Fe Institute’s Brian Arthur. Both recognize that self-organizing systems tend to be dominated by unstable fluctuations and that instability has become an unavoidable property of economic systems.
20. Diana Richards, a political scientist, is investigating what causes a complex system of interacting agents to become unstable. Or, in Per Bak’s terms, she is trying to determine how a complex system of individuals reaches self-organized criticality. 
21. According to Richards, a complex system necessarily involves aggregation of a wide number of choices made by the individuals in the system. She calls this “collective choice.” Of course, combining all the individuals’ choices does not always result in a straightforward collective choice; nor should we assume the aggregate choice, which is the sum of individual choices, always leads to stable outcomes. Collective choice, says Richards, occurs when all the agents in the system aggregate information in a way that allows the system to reach a single collective decision. To reach this collective decision, it is not necessary that all the agents hold identical information but that they share a common interpretation of the different choices. Richards believes that this common interpretation, which she calls mutual knowledge, plays a critical role in the stability of all complex systems. The lower the level of this mutual knowledge, the greater the likelihood of instability. 
22. An obvious question at this point is how people select from a collection of choices. According to Richards, if there is no clear favorite, the tendency of the system is to continually cycle over the possibilities. You might think this cyclical outcome would lead to instability, but according to Richards, it need not if the agents share similar mental concepts (that is, mutual knowledge) about the various choices. It is when the agents in the system do not have similar concepts about the possible choices that the system is in danger of becoming unstable. And that is clearly the case in the stock market.
23. If we step back and think about the market, we can readily identify a number of groups that exhibit different meta-models. We already know that fundamentalists and trend followers possess different meta-models. What about macro-traders who are not interested in individual companies but are interested only in directional changes in the overall market? What about long-short hedge funds? What about statistical arbitrageurs versus entrepreneurs? What about quantitatively driven strategists that seek low volatility-absolute return strategies? Each of these groups works from a different reality, a different sense of how the market operates and how they should operate within it. In reality, there are many different meta-models at work in the stock market, and if Richards’s theory is correct, this all but guarantees periodic instability. 
24. The value of this way of looking at complex systems is that if we know why they become unstable, then we have a clear pathway to a solution, to finding ways to reduce overall instability. One implication, Richards says, is that we should be considering the belief structures underlying various mental concepts and not the specifics of the choices. Another is to acknowledge that if mutual knowledge fails, the problem may center on how knowledge is transferred in the system. In the next chapter on psychology, we will turn to our attention to those two points: how individuals form belief structures and how information is exchanged in the stock market.
25. At this point, we have a fixed compass on how to analyze social systems. Whether they are economic, political, or social, we can say these systems are complex (they have a large number of individual units), and they are adaptive (the individual units adapt their behavior on the basis of interactions with other units as well as with the overall system). We also recognize that these systems have self-organizing properties and that, once organized, they generate emergent behavior. Finally, we realize that complex adaptive systems are constantly unstable and periodically reach a state of self-organized criticality. 
26. We come to these conclusions by studying a large number of complex adaptive systems across a wide variety of fields in both the natural and the social sciences. In all our study, we are currently limited to understanding how the systems have behaved so far. We have not made the scientific leap that will enable us to predict the future behavior, particularly in complex social systems involving the highly unpredictable units known as human beings. But we may be on the track of something even more valuable. 
27. What separates the study of complex natural systems from complex social systems is the possibility that in social systems we can alter the behavior of their individual units. Whereas we cannot as of yet change the trajectory of hurricanes, where groups of people are concerned we may be able to affect the outcome by influencing how individuals respond in various situations. To say this another way, although self-organized criticality is an inherent property of all complex adaptive systems, including economic systems, and although some degree of instability is unavoidable, we may be able to alter potential landslides by better understanding what makes criticality inevitable.
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# Chapter 5: Psychology

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1. In essence, Kahneman and Tversky had discovered that people are generally risk averse when making a decision that offers hope of a gain but risk seeking when making a decision that will lead to certain loss.
2. However, Thaler is perhaps best known among investors for his 1995 article titled “Myopic Loss Aversion and the Equity Risk Premium Puzzle” cowritten with Shlomo Benartzi. Benartzi is professor and cochair of the behavioral decision-making group at the UCLA Anderson School of Management. In their article, Thaler and Benartzi took loss aversion described in prospect theory and connected it directly to the stock market. 
3. The title of this groundbreaking article guides us to two related ideas that call for some discussion: First, that the equity risk premium is puzzling, and second, that loss aversion, unequivocally identified by Kahneman and Tversky, is illogical and prevents investors from seeing long term; that is, it makes them myopic. 
4. Equity risk premium is a term many investors have heard but few actually understand. It refers to the potential for higher returns represented by the inherently risky stock market compared to the risk-free rate, defined as the rate of a ten-year U.S. Treasury bond in effect at whatever point you’re considering. (It is called the risk-free rate because up until now the government has never defaulted on its loans.) Whatever return an individual stock or the overall stock market earns beyond that rate is the investor’s compensation for taking on the higher risk of the stock market—the equity risk. For example, if the return on a stock is 10 percent and the risk-free rate is 5 percent over the same period, the equity risk premium would be 5 percent. The size of the risk premium will vary based on the perceived riskiness of a particular stock or the stock market as a whole. According to Aswath Damodaran, professor of finance at the Stern School of Business at New York University, the implied equity risk premium has vacillated between less than 3 percent in 1961 and 6.5 percent in the early 1980s. 
5. Thaler and Benartzi were puzzled by two questions. One, why is the equity risk premium so high; and two, why is anyone willing to hold bonds when we know that over the years, stocks have consistently outperformed? The answer, they believed, rested upon two central concepts from Kahneman and Tversky. The first was loss aversion. The second was a behavioral concept called mental accounting. 
6. Mental accounting, explains Thaler, refers to the methods people use to code financial outcomes. To help make the connection, Thaler revisited an older problem first proposed by Paul Samuelson. In 1963, Samuelson asked a colleague if he would be willing to accept the following bet: a 50 percent chance of winning $200 or a 50 percent chance of losing $100. The colleague politely turned down the bet but then announced he would be happy to play the game 100 times so long as he did not have to watch each individual outcome. That counterproposal sparked an idea for Thaler and Benartzi. 
7. Samuelson’s colleague was willing to accept the wager with two qualifiers: lengthen the time horizon for the game and reduce the frequency in which he was forced to watch the outcomes. Moving that observation into investing, Thaler and Benartzi reasoned the longer the investor holds an asset, the more attractive the asset becomes but only if the investment is not evaluated frequently. If you don’t check your portfolio every day, you will be spared the angst of watching daily price gyrations; the longer you hold off, the less you will be confronted with volatility and therefore the more attractive your choices seem. Put differently, the two factors that contribute to an investor’s unwillingness to bear the risks of holding stocks are loss aversion and a frequent evaluation period. Using the medical word for shortsightedness, 
8. Thaler and Benartzi coined the term myopic loss aversion to reflect a combination of loss aversion and the frequency with which an investment is measured. Thaler and Benartzi next considered whether myopic loss aversion could help explain the equity risk premium. They wondered what combination of loss aversion and evaluation frequency would explain the historical pattern of stock returns. How often, they asked, would an investor need to evaluate a stock portfolio to be indifferent to the historical distribution of returns on stocks and bonds? The answer: one year.
9. Thaler and Benartzi argue that any discussion of loss aversion must be accompanied by a specification of the frequency by which returns are calculated. Clearly, investors are less attracted to high-risk investments like stocks when they evaluate their portfolio over shorter time horizons. “Loss aversion is a fact of life,” explain Thaler and Benartzi. “In contrast, the frequency of evaluations is a policy choice that presumably could be altered, at least in principle.”
10. Graham devoted much of his teaching and writing to getting people to understand the critical distinction between investment and speculation. But his message went much deeper than one of mere definitions. We must all come to terms, he insisted, with the idea that common stocks have both an investment characteristic and a speculative characteristic. That is, we know the direction of stock prices is ultimately determined by the underlying economics but we must also recognize that “most of the time common stocks are subject to irrational and excessive price fluctuations in both directions, as the consequence of the ingrained tendency of most people to speculate or gamble—i.e., to give way to hope, fear, and greed.”
11. Investors must be prepared, he cautioned, for ups and downs in the market. And he meant prepared psychologically as well as financially–not merely knowing intellectually that a downturn will happen, but having the emotional wherewithal to react appropriately when it does. And what is the appropriate reaction? In his view, an investor should do just what a business owner would do when offered an unattractive price—ignore it. 
12. “The investor who permits himself to be stampeded or unduly worried by unjustified market declines in his holdings is perversely transforming his basic advantage into a basic disadvantage,” said Graham. “That man would be better off if his stocks had no market quotation at all, for he would then be spared the mental anguish caused him by another person’s mistakes of judgment.”6 With his eloquent comment about “mental anguish,” Graham is speaking directly to the debilitating effects of myopic loss aversion. It would be another forty-five years before Thaler and Benartzi would write their paper.
13. Investment professionals put strong emphasis on helping investors accurately assess their tolerance for risk. Seeing their clients boldly add stocks to their portfolio when the market rises only to watch helplessly as they sell stocks and buy bonds when the market swoons has frustrated advisors whose primary responsibility is to properly determine asset allocation. This flipping back and forth between aggressive and then conservative has prompted many to rethink how they should approach the study of risk tolerance.
14. Walter Mitty is a fictional character in James Thurber’s wonderful short story “The Secret Life of Walter Mitty.” It was first published in the The New Yorker in 1939 and later made into a movie (1947) starring Danny Kaye. Walter Mitty was a meek fellow totally intimidated by his overbearing wife. He coped by daydreaming he was magically transformed into a courageous hero. One minute he was dreading facing his wife’s sharp tongue; the next, he was a fearless bomber pilot undertaking a dangerous mission alone. 
15. Pruitt believes investors react to the stock market the way Walter Mitty reacted to life. When the market is doing well, they become brave in their own eyes and eagerly accept more risk. But when the market goes down, they rush for the door. So when you ask an investor directly to explain their risk tolerance, the answer comes from either a fearless bomber pilot (in a bull market) or a henpecked husband (in a bear market). 
16. How do we overcome the Walter Mitty effect? By finding ways to measure risk tolerance indirectly. You have to look below the surface of the standard questions and investigate the underlying psychological issues. 
17. Working with Dr. Justin Green at Villanova University, I was able to develop a risk analysis tool that focused on an individual’s personality rather than asking about risk directly. We identified important demographic factors and personality orientations that, taken together, might help people measure their risk tolerance more accurately. 
18. Comfort with risk, we found, is connected to two demographic factors: age and gender. Older people are more cautious than younger people, and women more than men. Personal wealth does not seem to be a factor; having more money or less money does not seem to affect one’s level of risk tolerance. 
19. Two personality traits are also important: personal control orientation and achievement motivation. The first refers to people’s sense that they are in control of their environment and decisions about their life. People who believe they have this control are called “internals.” In contrast, “externals” think they have little control; they see themselves as being like a leaf blown about by the wind. According to our research, high risk takers were overwhelmingly classified as internals. Achievement motivation, the second important trait, describes the degree to which people are goal oriented. We found that risk takers are also goal oriented, even though a strong focus on goals may lead to sharp disappointments.
20. Understanding your own comfort level for risk is more complicated than simply measuring personal control orientation and achievement motivation. To unlock the real relationship between these personality characteristics and risk taking, you also need to understand how you view the risk environment.12 Do you think of the stock market as (1) a game you can win only with luck, or (2) an undertaking whose success depends on accurate information combined with rational choices? 
21. Psychological research clearly demonstrates that “whether a person believes the outcomes of [their] decision are dependent upon skill or chance influences the riskiness of their choices.”13 On average, people will consistently select options of moderate to high risk when they perceive the outcome is dependent on skill. But if they think the outcome is governed largely by chance, they will limit themselves to a much more conservative array of choices. 
22. In summary, let us look at how all these personality elements work together. Assuming age and gender variables are equal, we can identify risk-tolerant investors with three traits: They set goals, they believe they control their environment and can affect its outcome, and most important, they view the stock market as a contingency dilemma in which information, combined with rational choices, will produce winning results.
23. Psychologists tell us that our ability to understand abstract or complex ideas depends on carrying in our mind a working model of the phenomena. These mental models represent a real or hypothetical situation in the same way that an architect’s model represents a planned building and that a colorful doodad made of Tinkertoy pieces can represent a complicated atomic structure. To understand inflation, for example, we use mental models that represent what inflation means to us—experiencing higher gasoline or food prices, perhaps, or paying higher wages to our employees.
24. Johnson-Laird also discovered that when people possess a set of mental models about a particular phenomenon, they often focus on only a few, sometimes only one; obviously relying on a limited number of mental models can lead to erroneous conclusions. We also learn from Johnson-Laird that mental models typically represent what is true but not what is false. We find it much easier to construct a model of what inflation is rather than what it is not. 
25. Ongoing research has shown that, overall, our use of mental models is frequently flawed. We construct incomplete representations of the phenomena we are trying to explain. Even when they are accurate, we don’t use them properly. We tend to forget details about the models, particularly when some time has passed, and so our models are often unstable. Finally, we have a distressing tendency to create mental models based on superstition and unwarranted beliefs. 
26. Because mental models enable us to understand abstract ideas, good models are particularly important for investors, many of whom consider the underlying concepts that govern markets and economies dauntingly abstract. And, because mental models determine our actions, we should not be surprised that poorly crafted mental models, built on weak information, lead to poor investment performance.
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# Chapter 6: Philosophy

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1. Strictly for organizational simplicity, we can separate the study of philosophy into three broad categories. First, critical thinking as it applies to the general nature of the world is known as metaphysics. Physics, we have learned, is the study of the physical world, tangible objects and forces in nature. It is the study of tables and chairs and their molecular components, of inclined planes and free-falling balls, and of the laws of motion that control the sun and the moon. Metaphysics means “beyond physics.” The second body of philosophical inquiry is the investigation of three related areas: aesthetics, ethics, and politics. Aesthetics is the theory of beauty. Ethics is the philosophical branch that studies the issues of right and wrong. It asks what is moral and what is immoral, what behavior is appropriate and what behavior is inappropriate. Ethics makes inquiries into the activities people undertake, the judgments they make, the values they hold, and the character they aspire to achieve. Closely connected to the idea of ethics is the philosophy of politics. Whereas ethics investigates what is right or wrong at the societal level, political philosophy is a debate over how societies should be organized, what laws should be passed, and what connections peoples should have to these societal organizations. Epistemology, the third body of inquiry, is the branch of philosophy that seeks to understand the limits and nature of knowledge. The term itself comes from the Greek words epiteme, meaning “knowledge,” and logos, which literally means “discourse” and more broadly refers to any kind of study or intellectual investigation. Epistemology then is the study of the theory of knowledge. To put it simply, when we make an epistemological inquiry, we are thinking about thinking.
2. Thinking is much more than just acquiring knowledge, and the process of thinking can be done badly or well. By learning to think well, we can better avoid confusion, noise, and ambiguities. Not only will we become more aware of possible alternatives, we will be more capable of making reliable arguments. How we think about investing ultimately determines how we do it. If we can consciously adopt an epistemological framework, always considering at some level whether our thinking process is rigorous and cohesive, we can go a long way toward improving our investment results.
3. “Failure to explain is caused by failure to describe!” His voice was so loud it exploded, booming throughout the room. There was no mistaking its intent. Someone was angry and frustrated. Stunned, we all sat frozen in our seats. The audience went silent. Slowly a few turned around to see who had the fired the vocal bazooka—it was Benoit Mandelbrot.
4. The topic that night was a big one: is the stock market efficient—or not? It was part of a three-day seminar at the Santa Fe Institute titled “Beyond Equilibrium and Efficiency,” organized by J. Doyne Farmer, a research professor at the institute, and John Geanakoplos of the Cowles Foundation at Yale. In attendance was a diverse group of physicists, economists, mathematicians, finance professors, and money managers, including some of the best investment minds in the world.
5. Benoit Mandelbrot (1924–2010) was a maverick mathematician. He spent thirty-five years at IBM’s Thomas J. Watson Research Center before moving to Yale, where, at the age of seventy-five, he became the oldest professor in the university’s history to receive tenure. Along the way he received more than fifteen honorary doctorates. Mandelbrot developed the field of fractal geometry (he coined the term) and applied it to physics, biology, and finance. A fractal is defined as a rough or fragmented shape that can split into parts, each of which is at the least a close approximation of its original self. This is a property called self-similarity. 
6. About now you might be thinking, “I wouldn’t know a fractal if one hit me in the head.” But you may be surprised to learn that fractals are easily found in nature; they surround us, and we observe them every day. Examples include clouds, mountains, trees, ferns, river networks, cauliflower, and broccoli. The recursive nature of each of these is somewhat obvious. The branch from a tree or a frond from a fern is a miniature of its whole. Below the surface we have discovered that blood vessels and pulmonary vessels are a fractal system. And from thirty thousand feet looking down, we can see that a coastline, once thought to be impossible to measure, is one of nature’s fractals. For those who are now intrigued, Mandelbrot’s The Fractal Geometry of Nature (1982) is considered the seminal book that brought fractals into the mainstream of professional mathematics. 
7. What I find fascinating about Mandelbrot is not the mathematical rigor of fractals (which is obviously impressive) but the realization that he looked at nature’s constituents, as we all have, but saw something different. “Clouds are not spheres, mountains are not cones, coastlines are not circles, and bark is not smooth, nor does lightning travel in a straight line.” Because his description of clouds and lightning is different from ours, it should not be surprising his explanation differed. Now we can better appreciate his late-night pronouncement that “failure to explain is caused by failure to describe.” 
8. Are descriptions important in investing? You bet they are. But our study of descriptions will not take us to the mathematics department; that part will come later. Rather, we will stay with the philosophy curriculum and next meet someone who is arguably the most distinguished philosopher of the twentieth century. Bertrand Russell described him as “the most perfect example I have ever known of genius as traditionally conceived, passionate, profound, intense, and dominating.”
9. To help us better understand how this new philosophy of meaning actually worked, Wittgenstein drew a very simple three-sided figure. 
10. He then writes, “Take as an example the aspects of a triangle. This triangle can be seen as a triangular hole, as a solid, as a geometrical drawing, as standing on its base, as hanging from its apex; as a mountain, as a wedge, as an arrow or pointer, as an overturned object, which is meant to stand on the shorter side of the right angle, as a half parallelogram, and as various other things…. You can think now of this now of this as you look at it, can regard it now as this now as this, and then you will see it now this way, now this.” It is a compelling, even poetic way to describe his belief that reality is shaped by the words we select. Words give meaning. 
11. How does this relate to investing? As we will see, stocks have a lot in common with Wittgenstein’s triangle.
12. On May 15, 1997 Amazon became a publicly traded company. There have been many bull and bear cases about Amazon over the years. Is Amazon best described as a company that is similar to Barnes & Noble,  Walmart or to Dell?
13. Mandelbrot was right. Failure to explain is caused by failure to describe. Wittgenstein lives. The words we choose give meaning (description) to what we observe. In order to further explain and/or defend our description, we in turn develop a story about what we believe is true. There is nothing wrong with storytelling. In fact, it is a very effective way of transferring ideas. If you stop and think, the way we communicate with each other is basically through a series of stories. Stories are open-ended and metaphorical rather than determinate. Think back to our first chapter where Lakoff and Johnson (Metaphors We Live By) remind us that we fundamentally think and act metaphorically. Today, scientists and philosophers have dropped the word “storytelling” and instead use the word “narrative.” Indeed, it appears that “narrative” has now slipped into the mainstream.
14. And yes, investors use narratives. There is a narrative about the economic recovery following the financial crisis. There is a narrative about inflation following the massive printing of money used to combat the financial crisis. There is a narrative for deflation, which tells the depressing story of how the massive debt levels accumulated over the past decade will take years to pay down, causing prices and wages to fall.
15. Why should investors care about a half-century-old debate between humanists and scientists? Because the narratives investors use to explain the market or economy sometimes lack the statistical rigor required for a proper description. And as we have learned, if the description is faulty the explanation is likely wrong. 
16. An individual who has given this subject a great deal of thought is John Allen Paulos, professor of mathematics at Temple University. Paulos is a best-selling author, best known for Innumeracy (1988) and A Mathematician Reads the Newspaper (1995). Both books are enjoyable reads, but it was his 1998 book, Once Upon a Number: The Hidden Mathematical Logic of Stories, that is best connected to our philosophy chapter.
17. When we listen to stories we have the tendency to suspend disbelief in order to be entertained, says Paulos. But when we evaluate statistics, we are less willing to suspend disbelief in order that we are not duped. Paulos goes on to describe the two types of errors in formal statistics. Type I error occurs when we observe something that is not really there. A Type II error occurs when we fail to observe something that is actually there. According to Paulos, those who like to be entertained and wish to avoid making a Type II error are more likely to prefer stories over statistics. Those who do not necessarily yearn for entertainment but are desperate to avoid Type I errors are apt to prefer statistics to stories.
18. For investors it is important to realize the slippery slope of narratives. Storytelling inadvertently increases our confidence in propositions as the story itself becomes its own proof. “The focus of stories is on the individual rather than the averages, on motives rather than movements, on context rather than raw data,” explains Paulos. Because investors primarily use storytelling to explain markets and economies, the absence of statistical evidence weakens the description. Quoting James Boswell, best known as the biographer of Samuel Johnson: “A thousand stories which the ignorant tell, and believe, die away at once when the computist takes them in his gripe [sic].”
19. In investing, no one is perfect. Some of our mistakes will be minor and easy to overcome. Others will be intransigent. It is difficult to navigate our faults, particularly if they are steadfast and deeply held beliefs. To be a successful investor we must be prepared for redescriptions. Fortunately there is a philosophical guidepost that will make our journey easier and more sensible. We find such a guidepost in the philosophy of pragmatism. 
20. As a formal branch of philosophy, pragmatism is only about one hundred years old; it was first brought to public attention by William James in an 1898 lecture at the University of California, Berkeley. In his lecture, “Philosophical Conceptions and Practical Results,” James introduced what he called “the principle of Peirce, the principal of pragmatism.” It was a clear homage to his friend and fellow philosopher Charles Sander Peirce.
21. Through lively discussions at the Metaphysical Club, Peirce refined his theories and eventually came to this proposition: It is through thinking that people resolve doubts and form their beliefs, and their subsequent actions follow from those beliefs and become habits. Therefore anyone who seeks to determine the true definition of a belief should look not at the belief itself but at the actions that result from it. He called this proposition “pragmatism,” a term, he pointed out, with the same root as practice or practical, thus cementing his view that the meaning of an idea is the same as its practical results. “Our idea of anything,” he explained, “is our idea of its sensible effects.” In his classic 1878 paper, “How to Make Our Ideas Clear,” Peirce continued: “The whole function of thought is to produce habits of action. To develop its meaning, we have, therefore, simply to determine what habits it produces, for what a thing means is simply what habit it involves.”
22. To state the matter as simply as possible, pragmatism holds that truth (in statements) and rightness (in actions) are defined by their practical outcomes. An idea or an action is true, and real, and good, if it makes a meaningful difference. To understand something, then, we must ask what difference it makes, what its consequences are. “Truth,” James wrote, “is the name of whatever proves itself to be good in the way of belief.”
23. If truth and value are determined by their practical applications in the world, then it follows that truth will change as circumstances change and as new discoveries about the world are made. Our understanding of truth evolves. Darwin smiles.
24. The great use of beliefs, James pointed out, is to help summarize old facts and then lead the way to new ones. After all, he reminded the audience, all our beliefs are man-made. They are a conceptual language we use to write down our observations of nature, and as such, they become the choice of our experience. Thus, he summarized, “ideas (which themselves are but parts of our experience) become true just in so far as they help us get into satisfactory relation with other parts of our experience.”
25. How do we get from old beliefs to new beliefs? According to James, the process is the same as that followed by any scientist. 
26. An individual has a stock of old opinions already, but he meets a new experience that puts them to strain. Somebody contradicts them; or in a reflective moment he discovers that they contradict each other; or he hears of facts with which they are incompatible; or desires arise in him which they cease to satisfy. The result is inward trouble to which his mind till then had been a stranger and from which he seeks to escape by modifying his previous mass of opinions. He saves as many of them as he can, for in this matter of belief we are all extreme conservatives. So he tries to change first that opinion and then that (for they resist change very variously), until at least some idea comes up that he can graft upon the ancient stock with a minimum of disturbances of the latter, some idea that mediates between the stock and the new experience and runs them into one another most felicitously and expediently.
27. What happens, to summarize James, is that the new idea is adopted while the older truths are preserved with as little disruption as possible. The new truths are simply go-betweens, transition-smoothers, that help us get from one point to the next. “Our thoughts become true,” says James, “as they successfully exert their go-between function.” A belief is true and has “cash-value” if it helps us get from one place to another. Truth then becomes a verb, not a noun.
28. We learn by trying new things, by being open to new ideas, by thinking differently. This is how knowledge progresses. In short, pragmatism is the perfect philosophy for building and using a latticework of mental models.
29. The philosophic foundation of successful investors is twofold. First, they quickly recognize the difference between first- and second-order models, and as such they never become a prisoner of the second-order absolutes. Second, they carry their pragmatic investigations far from the field of finance and economics. It can be best thought of as a Rubik’s Cube approach to investing. The successful investor should enthusiastically examine every issue from every possible angle, from every possible discipline, to get the best possible description—or redescription—of what is going on. Only then is an investor in a position to accurately explain. 
30. The only way to do better than someone else, or more importantly, to outperform the stock market, is to have a way of interpreting the data that is different from other people’s interpretations. To that I would add the need to have sources of information and experiences that are different.26 In studying the great minds in investing, the one trait that stands out is the broad reach of their interests. Once your field of vision is widened, you are able to understand more fully what you observe, and then you use those insights for greater investment success.

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# Chapter 7: Literature

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1. Charlie Munger, whose concept of a latticework of mental models inspired this book, is sometimes asked, when he describes his concept to audiences, how a person goes about learning those models. They may use different words to frame their question, but essentially those in the audience are asking, “I certainly understand the value of knowing key ideas from different disciplines and building my own latticework, but I didn’t learn any of that in school, and I’d be starting from ground zero. Frankly, it seems overwhelming. How do I cultivate the kind of depth and breadth of knowledge that leads to worldly wisdom?” 
2. Charlie is not known for pulling his punches; his answer is blunt. Most people didn’t get the right kind of education, he says; too many academic departments are too narrow, too territorial, too self-absorbed with parochial issues to focus on what they should be about, which is helping students become truly educated people. Even earning a degree from a prestigious university is no guarantee that we have acquired what he calls worldly wisdom or even started on the path toward it.
3. If that is the case, he says with a smile, then the answer is simple: we must educate ourselves. The key principles, the truly big ideas, are already written down, waiting for us to discover them and make them our own.
4. Yes, I know; you already have too much to read as it is. But I ask you to consider for a moment whether you might be emphasizing the wrong material. I suspect much of what you currently read regularly (the material about which you think “but I have to read that”) is about adding facts rather than increasing understanding. In this chapter we are more concerned with the latter than the former. We can all acquire new insights through reading if we perfect the skill of reading thoughtfully. The benefits are profound: Not only will you substantially add to your working knowledge of various fields, you will at the same time sharpen your skill at critical thinking.
5. It is important to note that the techniques we have discussed thus far apply to nonfiction books, or what Adler calls expository work. (We shall consider fiction a bit later.) Adler defines as expository any book that conveys knowledge, and subdivides those books into two categories: practical and theoretical.
6. Don’t forget that your goal as a reader is to determine whether the book is true, not whether it supports what you already think. “You must check your opinions at the door,” says Adler. “You cannot understand a book if you refuse to hear what it is saying.”
7. Let’s take a moment to put into perspective what we have been learning in this chapter. We start with this irrefutable point: The mental skill of critical analysis is fundamental to success in investing. Perfecting that skill—developing the mind-set of thoughtful, careful analysis—is intimately connected to the skill of thoughtful, careful reading. Each one reinforces the other in a kind of double feedback loop. Good readers are good thinkers; good thinkers tend to be great readers and in the process learn to be even better thinkers. 
8. So the very act of reading critically improves your analytical skills. At the same time, the content of what you read adds to your compendium of knowledge, and this is enormously valuable. If you decide to expand your knowledge base by reading in areas outside finance, including some of the other disciplines presented in this book, you are assembling the individual elements to construct your own latticework of mental models. 
9. Or, to put the matter more directly, learning to be a careful reader has two enormous benefits to investors: it makes you smarter in an overall sense, and it makes you see the value of developing a critical mind-set, not necessarily taking information at face value. 
10. This critical mind-set, in turn, has two aspects that relate to the reading process: (1) evaluate the facts, and (2) separate fact from opinion. To see the process at work, let us briefly consider an analyst’s report. I chose this as a specific example because we all spend so much time reading them, but of course the general approach can be, and should be, used universally. 
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# Chapter 8: Mathematics

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1. Nightingale, perched upon an oak, was seen by Hawk, who swooped down and snatched him. Nightingale, begging earnestly, besought Hawk to let him go, insisting he wasn’t big enough to satisfy the hunger of Hawk, who ought instead to pursue larger birds. Hawk replied, “I should indeed have lost my senses if I should let go food ready to my hand, for the sake of pursuing birds which are not even seen within sight.” 
2. Undoubtedly you recognize the fable of “The Hawk and the Nightingale,” and you already know the moral of the story: “A bird in hand is worth two in the bush.”
3. But my favorite version of Aesop’s fable comes from Warren Buffett: “A girl in a convertible is worth five in the phone book.” 
4. I am quite sure that when Aesop wrote “The Hawk and the Nightingale” 2,600 years ago, he had no idea he was laying down one of the definitive laws of investing. 
5. Listen to Buffett: “The formula we use for evaluating stocks and businesses is identical. Indeed, the formula for valuing all assets that are purchased for financial gain has been unchanged since it was first laid out by a very smart man in about 600 B.C.E. The oracle was Aesop and his enduring, though somewhat incomplete, insight was ‘a bird in the hand is worth two in the bush.’ To flesh out this principle, you must answer only three questions. How certain are you that there are indeed birds in the bush? When will they emerge and how many will there be? What is the risk-free interest rate? If you can answer these three questions, you will know the maximum value of the bush—and the maximum number of birds you now possess that should be offered for it. And, of course, don’t literally think birds. Think dollars.”
6. Buffett goes on to say that Aesop’s investment axiom is immutable. And it matters not whether you apply the fable to stocks, bonds, manufacturing plants, farms, oil royalties, or lottery tickets. Buffett also points out that Aesop’s “formula” survived the advent of the steam engine, electricity, automobiles, airplanes, and the Internet. All you need to do, Buffett says, is insert the correct numbers and the attractiveness of all investment opportunities will be rank-ordered.
7. Buffett gives a great deal of thought about the company he is going to invest with as well as the industry the company operates within. He also closely examines the behavior of management, particularly how management thinks about allocating capital. These are all important variables, but they are largely subjective measurements. As such, they do not easily lend themselves to mathematical computation. In contrast, Buffett’s mathematical principles of investing are straightforward. He has often said he can do most business-value calculations on the back of an envelope. First, tabulate the cash. Second, estimate the growth probabilities probabilities of the cash coming and going over the life of the business. Then, discount the cash flows to present value.
8. You may be asking yourself, if the discounted present value of future cash flows is the immutable law for determining value, why do investors rely on relative valuation factors, second-order models? Because predicting a company’s future cash flows is so very difficult. We can calculate the future cash flows of a bond with near certainty—it’s a contractual obligation. But a business does not have a contractual obligation to generate a fixed rate of return. A business does the best it can, but many forces—the vagaries of the economy, the intensity of competitors, and innovators who have the ability to disrupt an industry—combine to make predictions about future cash flows less than precise. That doesn’t excuse us from making the effort, for as Buffett often quips, “I would rather be approximately right than precisely wrong.”
9. Pascal and Fermat exchanged a series of letters, which ultimately formed the basis of what today is called probability theory. In Against the Gods, the brilliant treatise on risk, Peter Bernstein writes that this correspondence “signaled an epochal event in the history of mathematics and the theory of probability.” Although they attacked the problem differently—Fermat used algebra whereas Pascal turned to geometry—each was able to construct a system for determining the probability of several possible but not yet realized outcomes. Indeed, Pascal’s geometric triangle of numbers can be used today to solve many problems, including the probability that your favorite baseball team will win the World Series after losing the first game.
10. The contributions of Pascal and Fermat mark the beginning of what we now call decision theory—the process by which we can make optimal decisions even in the face of an uncertain future. “Making that decision,” wrote Bernstein, “is the essential first step in any effort to manage risk.”
11. We now know probability theory is a potent instrument for forecasting. But, as we also know, the devil is in the details. In our case, the details are the quality of information, which forms the basis for the probability estimate.
12. The first person to think scientifically about probabilities and information quality was Jacob Bernoulli, a member of the famed Dutch-Swiss family of mathematicians that also included both Johann and Daniel Bernoulli. Jacob Bernoulli recognized the differences between establishing odds for a game of chance and odds for answering life’s dilemmas. As he pointed out, you do not need to actually spin the roulette wheel to figure out the odds of the ball landing on the number seventeen. However, in real life, relevant information is essential in understanding the probability of an outcome. As Bernoulli explained, nature’s patterns are only partly established, so probabilities in nature should be thought of as degrees of certainty, not as absolute certainty.
13. Although Pascal, Fermat, and Bernoulli are credited with developing the theory of probability, it was another mathematician, Thomas Bayes, who laid the groundwork for putting the theory into practical action. 
14. Thomas Bayes (1701–1761) was both a Presbyterian minister and a talented mathematician. Born one hundred years after Fermat and seventy-eight years after Pascal, Bayes lived an unremarkable life in the British county of Kent, south of London. He was elected to membership in the Royal Society in 1742 on the basis of his treatise, published anonymously, about Sir Isaac Newton’s calculus. During his lifetime, he published nothing else in mathematics. However, he stipulated in his will that at his death a draft of an essay he had written and one hundred pounds sterling was to be given to Richard Price, a preacher in neighboring Newington Green. Two years after Bayes’s death, Price sent a copy of the paper, “Essay Towards Solving a Problem in the Doctrine of Chances,” to John Canton, a member of the Royal Society. In his paper, Bayes laid down the foundation for the method of statistical inference—the issue first proposed by Jacob Bernoulli. In 1764, the Royal Society published Bayes’s essay in its journal, Philosophical Transactions. According to Peter Bernstein, it was a “strikingly original piece of work that immortalized  Bayes among statisticians, economists, and other social scientists.”
15. Bayes’s theorem is strikingly simple: When we update our initial belief with new information, we get a new and improved belief. In Sharon Bertsch McGrayne’s thoughtful book on Bayes, The Theory That Would Not Die, she succinctly lays out the Bayesian process. “We modify our opinions with objective information: Initial Beliefs + Recent Objective Data = A New and Improved Belief.” Later mathematicians assigned terms to each part of the method. Priori is the probability of the initial belief; likelihood for the probability of a new hypothesis based on recent objective data; and posterior for the probability of a newly revised belief. McGrayne tells us “each time the system is recalculated, the posterior becomes the prior of the new iteration. It was an evolving system, with each bit of new information pushed closer and closer to certitude.” Darwin smiles. 
16. Bayes’s theorem gives us a mathematical procedure for updating our original beliefs and thus changing the relevant odds. Here’s a short, easy example of how it works. 
17. Let’s imagine that you and a friend have spent the afternoon playing your favorite board game and now, at the end of the game, are chatting about this and that. Something your friend says leads you to make a friendly wager: that with one roll of the die you will get a “6.” Straight odds are one in six, a 16 percent probability. But then suppose your friend rolls the die again, quickly covers it with her hand, and takes a peek. “I can tell you this much,” she says; “it’s an even number.” With this new information your odds change to one in three, a 33 percent probability. While you consider whether to change your bet, your friend teasingly adds: “And it’s not a 4.” Now your odds have changed again, to one in two, a 50 percent probability. With this very simple sequence, you have performed a Bayesian analysis. Each new piece of information affected the original probability. 
18. Bayesian analysis is an attempt to incorporate all available information into a process for making inferences, or decisions. Colleges and universities use Bayes’s theorem to help students learn decision making. In the classroom, the Bayesian approach is more popularly called the “decision tree theory,” in which each branch of the tree represents new information that, in turn, changes the odds in making decisions. “At Harvard Business School,” explains Charlie Munger, “the great quantitative thing that bonds the first-year class together is what they call decision tree theory. All they do is take high school algebra and apply it to real-life problems. The students love it. They're amazed to find that high school algebra works in life."
19. There are two broad categories of probability interpretations. The first is called physical probabilities, more commonly referred to as frequency probabilities. They are commonly associated with systems that can generate tons of data over very long periods. Think roulette wheels, flipping coins, and card and dice games. But frequency probabilities can also include probability estimates for automobile accidents and life expectancy. Yes, cars and drivers are different, but there are enough similarities among people driving in a particular area that tons of data can be generated over a multiyear period that in turn will give you frequency-like interpretations. 
20. When a sufficient frequency of events, along with an extended time period to analyze the results, is not available, we must turn to evidential probabilities, commonly referred to as subjective probabilities. It is important to remember, a subjective probability can be assigned to any statement whatsoever, even when no random process is involved, as a way to represent the “subjective” plausibility. According to the textbooks on Bayesian analysis, “if you believe your assumptions are reasonable, it is perfectly acceptable to make your subjective probability of a certain event equal to a frequency probability.” What you have to do is to sift out the unreasonable and illogical in favor of reasonable. 
21. A subjective probability, then, is not based on precise computations but is often a reasonable assessment made by a knowledgeable person. Unfortunately, when it comes to money, people are not consistently reasonable or knowledgeable. We also know that subjective probabilities can contain a high degree of personal bias. 
22. Any time subjective probabilities are in use, it is important to remember the behavioral finance missteps we are prone to make and the personal biases to which we are susceptible. A decision tree is only as good as its inputs, and static probabilities—those that haven’t been updated—have little value. It is only through the process of continually updating probabilities with objective information that the decision tree will work. 
23. Whether or not they recognize it, virtually all decisions investors make are exercises in probability. To succeed, it is critical that their probability statements combine the historical record with the most recent data available. That is Bayesian analysis in action.
24. Kelly Criterion: Two caveats to the Kelly criterion that are often overlooked: You need (1) an unlimited bankroll and (2) an infinite time horizon. Of course, no investor has either, so we need to modify the Kelly approach. Again, the solution is mathematical in the form of simple arithmetic.
25. At age 40, Stephen Jay Gould, the famous American paleontologist and evolutionary biologist, was diagnosed with abdominal mesothelioma, a rare and fatal form of cancer, and was rushed into surgery. After the operation Gould asked his doctor what he could read to learn more about the disease. She told him there was “not much to be learned from the literature.”
26.  Undeterred, Gould headed to Harvard’s Countway medical library and punched “mesothelioma” into the computer. After spending an hour reading a few of the latest articles, Gould understood why his doctor was not so forthcoming. The information was brutally straightforward: mesothelioma was incurable, with a median life expectancy of only eight months. Gould sat stunned until his mind began working again. Then he smiled. 
27. What exactly did an eight-month median mortality signify? The median, etymologically speaking, is the halfway point between a string of values. In any grouping, half the members of the group will be below the median and half above it. In Gould’s case, half of those diagnosed with mesothelioma would die in less than eight months and half would die sometime after eight months. (For the record, the other two measures of central tendency are mean and mode. Mean is calculated by adding up all the values and dividing by the number of cases—a simple average. Mode refers to the most common value. For example, in the string of numbers 1, 2, 3, 4, 4, 4, 7, 9, 12, the number 4 is the mode.) 
28. Most people look on averages as basic reality, giving little thought to the possible variances. Seen this way, “eight months’ median mortality” meant he would be dead in eight months. But Gould was an evolutionary biologist and evolutionary biologists live in a world of variation. What interests them is not the average of what happened but the variation in the system over time. To them, means and medians are abstractions.
29. Most of us have a tendency to see the world along the bell shape curve with two equal sides, where mean, median, and mode are all the same value. But as we have learned, nature does not always fit so neatly along a normal, symmetrical distribution but sometimes skews asymmetrically to one side or the other. These distributions are called either right or left skewed depending on the direction of the elongation. 
30. Gould the biologist did not see himself as the average patient of all mesothelioma patients but as one individual inside a population set of mesothelioma patients. With further investigation, he discovered that the life expectancy of patients was strongly right skewed, meaning that those on the plus side of the eight-month mark lived significantly longer than eight months. 
31. What causes a distribution to skew either left or right? In a word, variation. As variation on one or the other side of the median increases, the sides of the bell curve are pulled either right or left. Continuing with our example, in Gould’s case, those patients who lived past the eight-month mark showed high variance (many of them lived not just more months but years), and that pulled the curve to form a right skew. In a right-skewed distribution, the measures of central tendency do not coincide; the median lies to the right of the mode and the mean lies to the right of the median.
32. Gould began to think about the characteristics of those patients who populated the right skew of the distribution, who exceeded the median distribution of life expectancy. Not surprisingly, they were young, generally in good health, and had benefited from early diagnosis. This was Gould’s own profile, and so he reasoned there was a good chance he would live well beyond the eight-month mark. Indeed, Gould lived for another twenty years. 
33. “Our culture encodes a strong bias either to neglect or ignore variation,” Gould said. “We tend to focus instead on measures of central tendency, and as a result we make some terrible mistakes, often with considerable practical import.”
34. The most important lesson investors can learn from Gould’s experience is to appreciate the differences between the trend of the system and trends in the system. Put differently, investors need to understand the difference between the average return of the stock market and the performance variation of individual stocks. One of the easiest ways for investors to appreciate the differences is to study sideways markets.
35. Most investors have experienced two types of stock markets—bull and bear—that go either up or down over time. But there is a third, less familiar type of market. It is called a “sidewinder” and it produces a sideways market—one that barely changes over time.
36. One of the more famous sideways markets occurred between 1975 and 1982. On October 1, 1975, the Dow Jones Industrial Average stood at 784. Nearly seven years later, on August 6, 1982, the Dow closed at the exact 784. Even though nominal earnings grew over the time period, the price paid for those earnings dropped. By the end of 1975, the trailing price-earnings multiple for the S&P 500 was almost 12 times. By the fall of 1982, it had declined to nearly 7 times. 
37. Some stock market forecasters are drawing analogies to what happened then to what may be happening today. There are concerns about the rate of corporate profit growth against the backdrop of a weak global economic recovery. Others fear the massive stimulation provided by the monetary authorities will cause a rise in commodity prices, inflation, and decline in the dollar. This will, in turn, feed back into the stock market, causing price-earnings multiples to fall. Ultimately, investors could face a prolonged period when the market barely budges—and when they are best advised to avoid stocks. 
38. When I first heard that argument—that we might be facing a sideways market similar to the late 1970s and it was best to avoid stocks—I was puzzled. Was it really true that sideways markets are unprofitable for long-term investors? Warren Buffett, for one, had generated excellent returns during the period; so did his friend and Columbia University classmate Bill Ruane. From 1975 through 1982, Buffett generated a cumulative total return of 676 percent at Berkshire Hathaway; Ruane and his Sequoia Fund partner Rick Cunniff posted a 415 percent cumulative return. How did they manage these outstanding returns in a market that went nowhere? I decided to dig a little. 
39. First, I examined the return performance of the 500 largest stocks in the market between 1975 and 1982. I was specifically looking for stocks that had produced outsized gains for shareholders. Over the 8-year period, only 3 percent of the 500 stocks went up in price by at least 100 percent in any one year. When I extended the holding period to 3 years, the results were more encouraging: Over rolling 3-year periods, 18.6 percent of the stocks, on average, doubled. That equals 93 out of 500. Then I extended the holding period to 5 years. Here the returns were eye-popping. On average, an astonishing 38 percent of the stocks went up 100 percent or more; that’s 190 out of 500.14 
40. Putting it in Gould’s terms, investors who observed the stock market between 1975 and 1982 and focused on the market average came to the wrong conclusion. They wrongly assumed that the direction of the market was sideways, when in fact the variation within the market was dramatic and led to plenty of opportunities to earn high excess returns. Gould tells us “the old Platonic strategy of abstracting the full house as a single figure (an average) and then tracing the pathway of this single figure through time, usually leads to error and confusion.” Because investors have a “strong desire to identify trends,” it often leads them “to detect a directionality that doesn’t exist.” As a result, they completely “misread the expanding or contracting variation within a system. “In Darwin’s world,” said Gould, “variation stands as the fundamental reality and calculated averages become abstractions.”
41. On the first page of their seminal book Security Analysis, Benjamin Graham and David Dodd included a quote from Quintus Horatius Flaccus, (65-8 B.C.E.) "Many shall be restored that now are fallen and many shall fall that are now in honor." Just as Aesop had no clue his fable about Hawk and Nightingale was the literary preamble to the discounted cash flow model, so too I am sure Horace had no idea he had just written down the narrative formula for regression to the mean.
42. Whenever you hear someone say, "It all averages out," that's a colloquial rendition of regression to the mean- a statistical phenomenon that, in essence, describes the tendency of unusually high or unusually low values to eventually drift back toward the middle. As used in investing, it suggests that very high or very low performance is not likely to continue and will probably reverse in a later period. (That's why it is sometimes called reversion to the mean.) Regression to the mean, Peter Bernstein points out, is the core of several homilies, including "what goes up must come down," "pride goeth before a fall." and Joseph's prediction to Pharaoh that seven years of famine would follow seven years of plenty. And, Bernstein tells us, it also lies at the heart of investing, for regression to the mean is a common strategy-often applied and sometimes overused-for picking stocks and predicting markets. 
43. We can trace the mathematical discovery of regression to the mean to Sir Francis Galton, a British intellectual and cousin of Charles Darwin. (You may recall Galton and his ox-weighing contest in our chapter on sociology). Galton had no interest in business or economics. Rather, one of his principal investigations was to understand how talent persisted in a family generation after generation-including the Darwin clan.
44. Galton was the beneficiary of the work by a Belgian scientist named Lambert Adolphe Jacques Quetelet (1796-1874). Twenty years older than Galton, Quetelet had founded the Brussels Observatory and was instrumental in introducing statistical methods to the social sciences. Chief among his contributions was the recognition that normal distributions appeared rooted in social structures and the physical attributes of human beings.
45. Galton was enthralled with Quetelet's discovery that "the very curious theoretical law of the deviation from the average-the normal distribution-was ubiquitous, especially in such measurements as body height and chest measurements." Galton was in the process of writing Hereditary Genius, his most important work, which sought to prove that heredity alone was the source of special talents, not education or subsequent professional careers. But Quetelet's deviation from the average stood in his way. The only way Galton could advance his theory was to explain how the differences within a normal distribution occurred. And the only way he could do this was to figure out how data arranged itself in the first place. In doing so, Galton made what Peter Bernstein calls an "extraordinary discovery" that has had vast influence in the world of investing.
46. Galton's first experiments were mechanical. He in vented the Quincunx, an unconventional pinball machine shaped like an hourglass with twenty pins stuck in the neck. Demonstrating his idea before the Royal Society, Galton showed that when he dropped balls at random they tended to distribute themselves in compartments at the bottom of the hourglass in a classic Gaussian fashion. Next he studied garden peas-or more specifically, the peas in the pod. He measured and weighed thousands of peas and sent ten specimens to friends throughout the British Isles with specific instructions on how to plant them. When he studied the off spring of the ten different groups, Galton found that their physical attributes were arranged in normal Gaussian distribution just as the Quincunx would have predicted.
47. This experiment, along with others including the study of height variation between parents and their children, became known as regression, or reversion, to the mean. "Reversion," said Galton, "is the tendency of the ideal filial type to depart from the parent type, reverting to what may be roughly and perhaps fairly described as the average ancestral type." If this process were not at work, explained Galton, then large peas would produce ever-larger peas and small peas would produce ever-smaller peas until we had a world that consisted of nothing but giants and midgets.
48. J. P. Morgan was once asked what the stock market would do next. His response: "It will fluctuate." No one at the time thought this was a backhanded way of describing regression to the mean. But this now-famous reply has become the credo for contrarian investors. They would tell you greed forces stock prices to move higher and higher from intrinsic value, just as fear forces prices lower and lower from intrinsic value, until regression to the mean takes over. Eventually, variance will be corrected in the system.
49. It is easy to understand why regression to the mean is slavishly followed on Wall Street as a forecasting tool.
50. It is a neat and simple mathematical conjecture that allows us to predict the future. But if Galton's Law is immutable, why is forecasting so difficult?
51. The frustration comes from three sources. First, reversion to the mean is not always instantaneous. Over valuation and undervaluation can persist for a period longer-much longer--than patient rationality might dictate. Second, volatility is so high, with deviations so irregular, that stock prices don't correct neatly or come to rest easily on top of the mean. Last, and most important, in fluid environments (like markets) the mean itself may be unstable. Yesterday's normal is not tomorrow's. The mean may have shifted to a new location. 
52. In physics-based systems, the mean is stable. We can run a physics experiment ten thousand times and get roughly the same mean over and over again. But markets are biological systems. Agents in the system-investors-learn and adapt to an ever-changing landscape. The behavior of investors today, their thoughts, opinions and reasoning, is different from investors of the last generation.
53. Up until the 1950s, the dividend yield on common stocks was always higher than the yield on government bonds. That's because the generation that lived through the 1929 stock market crash and Great Depression demanded safety in the form of higher dividends if they were to purchase stocks over bonds. They may not have used the term, but in fact they employed a simply strategy of regression to the mean. When common stock yields approached or dipped below government bond yields, they sold stocks and bought bonds. Galton's Law reset prices.
54. As economic prosperity returned in the 1950s, a generation removed from the painful stock market losses of the 1930s embraced common stocks. Had you held steadfast to the idea that common stock yields would revert back to levels higher than bond yields, you would have lost money. And an example from today's market: In a striking turn of events, the dividend yields on many common stocks in 2011 were higher than the yield on 10-year U.S. Treasury notes. Following the regression approach, you would have sold bonds in favor of stocks. Yet as we move into 2012, bonds have continued to outpace stocks. How long will this economic deviation from the mean last? Or has the mean now shifted?
55. Most people think the S&P 500 Index is a passively managed basket of stocks that rarely changes. But that is untrue. Each year the selection committee at Standard & Poor's subtracts companies and adds new ones; about 15 percent of the index, roughly 75 companies, is exchanged. Some companies exit the index because they have been taken over by another company. Others are removed because their declining economic prospects mean they no longer qualify for the largest 500 companies. The companies that are added are typically healthy and vibrant in industries that are having a positive impact on the economy. As such, the S&P 500 Index evolves in a Darwinian manner, populating itself with stronger and stronger companies-survival of the fittest
56. Fifty years ago, the S&P 500 Index was dominated by manufacturing, energy, and utility companies. Today it is dominated by technology, health care, and financial companies. Because the return on equity for the latter three is higher than the first group of three, the average return on equity of the index is now higher today than it was thirty years ago. The mean has shifted. In the words of Thomas Kuhn, there has been a paradigm shift.
57. Overemphasizing the present without understanding the subtle shifts in composition can lead to perilous and faulty decisions. Although regression to the mean remains an important strategy, it is imperative that investors remember it is not inviolable. Stocks that are thought to be high in price can still move higher; stocks that are low in price can continue to decline. It is important to remain flexible in your thinking. Although reversion to the mean is the most likely outcome in markets, its presence is not sacrosanct.
58. A "black swan, " as described by Taleb, is an event with three attributes: (1) "it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility, (2) it carries an extreme impact, (3) in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable."
59. In The Black Swan, Taleb's goal was to help investors better appreciate the disproportionate role of events that are hard-to-predict, high-impact, and rare a swan born black-events well beyond the normal expectations we have for history, science, technology, and finance. Second, he wanted to bring attention to the incomputable nature of these ultrarare events using scientific methods based on the nature of a small probability set. Lastly, he wanted to bring to light the psychological biases, the blindness, we have to uncertainty and history's rare events.
60. According to Taleb, our assumptions about what is going to happen grow out of the bell-shape curve of predictability-what he calls "Mediocristan." Instead, the world is shaped by wild, unpredictable, and powerful events he calls "Extremistan." In Taleb's world, "history does not crawl, it jumps."
61. The attack on Pearl Harbor in 1941 and the 9/11 terrorist attack on the World Trade Center are examples of black swan events. Both were outside the realm of expectation, both had extreme impact, and both were readily explainable after the fact. Unfortunately, the term black swan has become trivialized. Media is quick to attach the moniker to just about anything that is the least bit irregular, including freak snowstorms, earth quakes, and stock market volatility. It would be more appropriate to label these events "gray swans."
62. Statisticians have a term for black swan events: it is called a fat tail. William Safire, New York Times columnist, explains the terminology: In a normal distribution, the bell curve is tall and wide in the middle and drops and flattens out at the bottom. The extremities at the bottom, either on the right side or the left, are called tails. When the tails balloon instead of vanishing in a normal distribution, the tails are designated as "fat." Taleb's black swan event shows up as a fat tail. In statistics, events that deviate from a normal distribution mean by five or more standard deviations are considered extremely rare. Like the term black swan, fat tail has become a part of the investing nomenclature. We hear constantly that investors cannot suffer another "left-tail" event. Institutional investors are now buying "left-tail" insurance; hedge funds are selling "left-tail" protection. Here again, I believe we are misusing terms. Today, any mild deviation from the norm is quickly labeled as a black swan or a fat tail.
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# Chapter 9: Decision Making

< [[Investing: The Last Liberal Art: Table of Contents]]

1. For years, psychologists have been interested in the idea that our cognitive processes are divided into two modes of thinking, traditionally referred to as intuition, which produces “quick and associative” cognition, and reason, described as “slow and rule-governed.” Today, these cognitive systems are commonly referred to as System 1 and System 2. System 1 thinking is intuitive. It operates automatically, quickly, and effortlessly with no sense of voluntary control. System 2 is reflective. It operates in a controlled manner, slowly and with effort. The operations of System 2 thinking require concentration and are associated with subjective experiences that have rule-based applications.
2. Put differently, intuition appears to work well in linear systems where cause and effect is easy to identify. But in nonlinear systems, including stock markets and economies, System 1 thinking, the intuitive side of our brain, is much less effectual.
3. Thus, Kahneman believes, increasing the amount of information stored in memory increases our skill at intuitive thinking. Further, he says, the failure of System 2 to override System 1 is largely a resource condition. “In some judgmental tasks, information (in System 2 thinking) that could serve to supplement or correct the heuristic (occurring in System 1 thinking) is not neglected nor underweighted, but simply lacking.”
4. Improving the resource condition of our System 2 thinking—that is to say, deepening and broadening our reserves of relevant information—is the principal reason this book was written.
5. Sadly, but perhaps not surprisingly, the predictions of experts are no better than “dart-throwing chimpanzees.”How can this be? According to Tetlock, “How you think matters more than what you think?”
6. Tetlock tells us Foxes have three distinct cognitive advantages. 1.  They begin with “reasonable starter” probability estimates. They have better “inertial-guidance” systems that keep their initial guesses closer to short-term base rates. 2.  They are willing to acknowledge their mistakes and update their views in response to new information. They have a healthy Bayesian process. 3.  They can see the pull of contradictory forces, and, most importantly, they can appreciate relevant analogies.
7. Hedgehogs start with one big idea and follow through—no matter the logical implications of doing so. Foxes stitch together a collection of big ideas. They see and understand the analogies and then create an aggregate hypothesis. I think we can say the fox is the perfect mascot for the College of Liberal Arts Investing.
8. The idea that people with high IQs could be so bad at decision making at first seems counterintuitive. We assume that anyone with high intelligence will also act rationally. But Stanovich sees it differently. In his book, What Intelligence Tests Miss: The Psychology of Rational Thought, he coined the term “dysrationalia”—the inability to think and behave rationally despite having high intelligence. 
9. Research in cognitive psychology suggests there are two principal causes of dysrationalia. The first is a processing problem. The second is a content problem. 
10. Stanovich believes we process poorly. When solving a problem, he says, people have several different cognitive mechanisms to choose from. At one end of the spectrum are mechanisms with great computational power, but they are slow and require a great deal of concentration. At the opposite end of the spectrum are mechanisms that have low computational power, require very little concentration, and make quick action possible. “Humans are cognitive misers,” Stanovich writes, “because our basic tendency is to default to the processing mechanisms that require less computational effort, even if they are less accurate.” In a word, humans are lazy thinkers. They take the easy way out when solving problems and as a result, their solutions are often illogical. 
11. The second cause of dysrationalia is the lack of adequate content. Psychologists who study decision making refer to content deficiency as a “mindware gap.” First articulated by David Perkins, a Harvard cognitive scientist, mindware refers to the rules, strategies, procedures, and knowledge people have at their mental disposal to help solve a problem. “Just as kitchenware consists in tools for working in the kitchen, and software consists in tools for working with your computer, mindware consists in the tools for the mind,” explains Perkins. “A piece of mindware is anything a person can learn that extends the person’s general powers to think critically and creatively.”
12. Mindware gaps, he believes, are generally caused by the lack of a broad education. In Perkins’s view, schools do a good job of teaching the facts of each discipline but a poor job of connecting the facts of each discipline together in such a way to improve our overall understanding of the world. “What is missing,” he says, “is the metacurriculum—the ‘higher order’ curriculum that deals with good patterns of thinking in general and across subject matters.”
13. According to Kahneman, “Those who avoid the sin of intellectual sloth could be called ‘engaged.’ They are more alert, more intellectually active, less willing to be satisfied with superficially attractive answers, more skeptical about their intuitions.”14 What does it mean to be engaged? Quite simply, it means your System 2 thinking is strong, vibrant, and less prone to fatigue. So distinct is System 2 thinking from System 1 thinking that Keith Stanovich has termed the two as having “separate minds.” 
14. But a “separate mind” is only separate if it is distinguishable. If your System 2 thinking is not adequately armed with the required understanding of the major mental models collected from the study of several different disciplines, then its function will be weak—or, says Kahneman, lazy. 
15. Having been schooled in modern portfolio theory and the efficient market hypothesis, will you quickly and automatically default to this physics-based model of how markets operate, or will you slow down your thinking and also consider the possibility that the market’s biological function could be altering the outcome? Even if the market looks hopelessly efficient, will you also consider that the wisdom of the crowds is only temporary—until the next diversity breakdown?
16. When you analyze your portfolio, will you resist the almost uncontrollable urge to sell a losing position, knowing full well the angst you feel is an irrational bias the pain of loss being twice as discomforting as the pleasure of an equal unit of gain? Will you stop yourself from looking at your price positions day in and day out, knowing that the frequency with which you do isworking against your better judgment? Or will you bow down to your first instinct and sell first and ask questions later
17. When thinking about companies, markets, and economies, will you rest with your first description of events? Knowing that more than one description is possible and the dominant description is most often determined by the extent of media coverage, will you dig deeper to uncover additional, perhaps more appropriate, descriptions? Yes, it takes mental energy to do this. Yes, it will take more time to reach a decision. Yes, this is more difficult than defaulting to your first intuition.
18. Lastly, with all that you have to read to get through the requirements of your job, will you read a new book that will increase your understanding? As Charlie Munger has said so many times, it is only by reading that you are able to continuously learn.
19. All this and more are the mental exercises that help to close the mindware gap and strengthen your System 2 thinking. It serves to keep you engaged. It works to fully develop your separate mind.
20. Building an effective model for investing is very similar to operating a flight simulator. Because we know the environment is going to change continually, we must be in a position to shift the building blocks to construct different models. Pragmatically speaking, we are searching for the right combination of building blocks that best describes the current environment. Ultimately, when you have discovered the right building blocks for each scenario, you have built up experiences that in turn enable you to recognize patterns and make the correct decisions.
21. One thing to remember is that effective decision making is very much about weighting the right building blocks, putting them into some hierarchical structure. Of course, we may never fully know what all the optimal building blocks are, but we can put into place a process of improving what we already have. If we have a sufficient number of building blocks, then model building becomes very much about reweighting and recombining them in different situations.
22. One thing we know from recent research by John Holland and other scientists (see Chapter 1) is that people are more likely to change the weighting of their existing building blocks than to spend any time discovering new ones. And that is a mistake. We must, argues Holland, find a way to use productively what we already know and at the same time actively search for new knowledge- or, as Holland adroitly phrases it, we must strike a balance between exploitation and exploration. When our model reveals readily available profits, of course we should intensely exploit the market's in efficiency. But we should never stop exploring for new building blocks.
23. Although the greatest number of ants in a colony will follow the most intense pheromone trail to a food source, there are always some ants that are randomly seeking the next food source. When Native Americans were sent out to hunt, most of those in the party would return to the proven hunting grounds. However, a few hunters, directed by a medicine man rolling spirit bones, were sent in different directions to find new herds. The same was true of Norwegian fishermen. Each day most of the ships in the fleet returned to the same spot where the previous day's catch had yielded the greatest bounty, but a few vessels were also sent in random directions to locate the next school of fish. As investors, we too must strike a balance between exploiting what is most obvious while allocating some mental energy to exploring new possibilities.
24. By recombining our existing building blocks, we are in fact learning and adapting to a changing environment. Think back for a moment to the description of neural networks and the theory of connectionism in Chapter 1. It will be immediately obvious to you that by choosing and then recombining building blocks, what we are doing is creating our own neural network, our connectionist model.
25. The process is similar to genetic crossover that occurs in biological evolution. Indeed, biologists agree that genetic crossover is chiefly responsible for evolution. Similarly, the constant recombination of our existing mental building blocks will, over time, be responsible for the greatest amount of investment progress. However, there are occasions when a new and rare discovery opens up new opportunities for investors. In much the same way that a mutation can accelerate the evolutionary process, so too can newfound ideas speed us along in our understanding of how markets work. If you are able to discover a new building block, you have the potential to add another level to your model of understanding.
26. One of the principal goals of this book is to give you a broader explanation of how markets behave and in the process help you make better investment decisions. One thing we have learned thus far is that our failures to explain are caused by our failures to describe. If we cannot accurately describe a phenomenon, it is fairly certain we will not be able to accurately explain it. The lesson we are taking away from this book is that the descriptions based solely on finance theories are not enough to explain the behaviour of markets.