Everything Is Obvious
Everything Is Obvious

Everything Is Obvious

Understanding the influence of default settings on the choices we make is important, because our beliefs about what people choose and why they choose it affect virtually all our explanations of social, economic, and political outcomes. (Location 574)

In Freakonomics, Steven Levitt and Stephen Dubner illustrate the explanatory power of rational choice theory in a series of stories about initially puzzling behavior that, upon closer examination, turns out to be perfectly rational. (Location 620)

But once you understand that the fine assuaged the pangs of guilt they were feeling at inconveniencing the school staff—essentially, they felt they were paying for the right to be late—it makes perfect sense. (Location 629)

Even though cheating could cost them their jobs, the risk of getting caught seemed small enough that the cost of being stuck with a low-performing class outweighed the potential for being punished for cheating. (Location 634)

When someone does something that seems strange or puzzling to us, rather than writing them off as crazy or irrational, we should instead seek to analyze their situation in hopes of finding a rational incentive. (Location 638)

When we try to understand why an ordinary Iraqi citizen would wake up one morning and decide to turn himself into a living bomb, we are implicitly rationalizing his behavior. (Location 649)

And when we blame soaring medical costs on malpractice legislation or procedure-based payments, we are instinctively invoking a model of rational action to understand why doctors do what they do. (Location 651)

Rationalizing human behavior, however, is precisely an exercise in simulating, in our mind’s eye, what it would be like to be the person whose behavior we are trying to understand. (Location 661)

The reason is that when we think about how we think, we instinctively emphasize consciously accessible costs and benefits such as those associated with motivations, preferences, and beliefs—the kinds of factors that predominate in social scientists’ models of rationality. (Location 666)

In countless experiments, for example, psychologists have shown that an individual’s choices and behavior can be influenced by “priming” them with particular words, sounds, or other stimuli. (Location 673)

In one experiment, for example, participants in a wine auction were asked to write down the last two digits of their social security numbers before bidding. (Location 680)

Although these numbers were essentially random and certainly had nothing to do with the value a buyer should place on the wine, researchers nevertheless found that the higher the numbers, the more people were willing to bid. (Location 681)

Emphasizing one’s potential to lose money on a bet, for example, makes people more risk averse while emphasizing one’s potential to win has the opposite effect, even when the bet itself is identical. (Location 690)

Say, for example, that option A is a high-quality, expensive camera while B is both much lower quality and also much cheaper. (Location 692)

Continuing this litany of irrationality, psychologists have found that human judgments are often affected by the ease with which different kinds of information can be accessed or recalled. (Location 701)

Paradoxically, people rate themselves as less assertive when they are asked to recall instances where they have acted assertively— (Location 704)

They also systematically remember their own past behavior and beliefs to be more similar to their current behavior and beliefs than they really were. (Location 706)

In part, we do this by noticing information that confirms our existing beliefs more readily than information that does not. (Location 709)

Together, these two closely related tendencies—known as confirmation bias and motivated reasoning respectively— (Location 711)

greatly impede our ability to resolve disputes, from petty disagreements over domestic duties to long-running political conflicts like those in Northern Ireland or Israel-Palestine, in which the different parties look at the same set of “facts” and come away with completely different impressions of reality. (Location 712)

Scientists, that is, are supposed to follow the evidence, even if it contradicts their own preexisting beliefs; and yet, more often then they should, they question the evidence instead. (Location 714)

Taken together, the evidence from psychological experiments makes clear that there are a great many potentially relevant factors that affect our behavior in very real and tangible ways but that operate largely outside of our conscious awareness. (Location 719)

In all these cases, we want to believe that X succeeded because it had just the right attributes, but the only attributes we know about are the attributes that X possesses; thus we conclude that these attributes must have been responsible for X’s success. (Location 975)

we routinely explain social trends in terms of what society “is ready (Location 984)

Thus, in effect, all we are really saying is that “X happened because that’s what people wanted; and we know that X is what they wanted because X is what happened.” (Location 985)

And cultural institutions such as marriage, social norms, and even legal principles have relevance only to the extent that large numbers people believe that they do. (Location 992)

And at each level of the pyramid, we have essentially the same problem—how do you get from one “scale” of reality to the next? (Location 1003)

At some level the laws that apply at different scales must be consistent—one cannot have chemistry that violates the laws of physics—but it is not generally possible to derive the laws that apply at one scale from those that govern the scale below (Location 1007)

Increasingly, however, the questions that scientists find most interesting—from the genomics revolution to the preservation of ecosystems to cascading failures in power grids— (Location 1011)

Individual genes interact with each other in complex chains of activation and suppression to express phenotypic traits that are not reducible to the properties of any one gene. (Location 1013)

Markets are affected by government regulations as well as by the actions of individual firms, and sometimes even of individual people (think Warren Buffett or Ben Bernanke). (Location 1020)

Emergence, remember, is a hard problem precisely because the behavior of the whole cannot easily be related to the behavior of its parts, and in the natural sciences we implicitly acknowledge this difficulty. (Location 1025)

Explanations that ascribe individual psychological motivations to aggregate entities like firms, markets, and governments might be convenient, but they are not, as the philosopher John Watkins put it, “rock bottom” explanations.8 (Location 1046)

Rather, they specify a single “representative firm” and ask how that firm would rationally allocate its resources given certain information about the rest of the economy. (Location 1055)

attacked the representative-agent approach as flawed and misleading. (Location 1063)

There’s a possibility of things getting out of hand. But being educated, civilized people, they also understand that reason and dialogue are preferable to violence. (Location 1075)

they are doing so, at least in part, in response to what other people are doing. (Location 1079)

Nevertheless, in this crowd, as everywhere, individual people have different tendencies toward violence. (Location 1084)

So closely matched are these two crowds, in fact, that they differ with respect to just one person: (Location 1104)

If these observers were to compare notes later, they would try to figure out what it was about the people or their circumstances that must have been different. (Location 1112)

That said, the model is extremely—almost comically—simple, and is likely to be wrong in all sorts of ways. (Location 1131)

Rather, when people tend to like something that other people like, differences in popularity are subject to what is called cumulative advantage, meaning that once, say, a song or a book becomes more popular than another, it will tend to become more popular still. (Location 1140)

therefore, explanations in which intrinsic attributes were the only things that mattered would predict that the same outcome would pertain every time. (Location 1149)

would nevertheless generate different cultural or marketplace winners. (Location 1151)

It may not surprise you, therefore, that when someone uses the output of a simulation model to argue that Harry Potter may not be as special as everyone thinks it is, Harry Potter fans tend not to be persuaded. (Location 1155)

This setup therefore enabled us to test the effects of social influence directly. If people know what they like regardless of what other people think, there ought not to be any difference between the social influence and independent conditions. (Location 1196)

In all the “social influence” worlds, that is, popular songs were more popular (and unpopular songs were less popular) than in the independent condition. (Location 1202)

Rather, unpredictability was inherent to the dynamics of the market itself. (Location 1206)

It was still the case that, on average, “good” songs (as measured by their popularity in the independent condition) did better than “bad” ones. It was also true that the very best songs never did terribly, while the very worst songs never actually won. (Location 1207)

that when individuals are influenced by what other people are doing, similar groups of people can end up behaving in very different ways. This may not sound like a big deal, but it fundamentally undermines the kind of commonsense explanations that we offer for why some things succeed and others fail, why social norms dictate that we do some things and not others, or even why we believe what we believe. (Location 1231)

so too you could know everything about individuals in a given population—their likes, dislikes, experiences, attitudes, beliefs, hopes, and dreams—and still not be able to predict much about their collective behavior. (Location 1238)

Rather, the point is just that the explanations we give for its success are less relevant than they seem. (Location 1250)

When faced with the prospect that some outcome of interest cannot be explained in terms of special attributes or conditions, therefore, a common fallback is to assume that it was instead determined by a small number of important or influential people. So it is to this topic that we turn next. (Location 1261)

Instead they experienced their greatest difficulties after they had already gotten close to their targets. (Location 1322)

where, the old adage goes, you “drive for show, putt for dough.” (Location 1324)

In fact, of the sixty-four messages that got through, nearly half of them were delivered to the target by one of three people, and half of those—sixteen chains—were delivered by a single person, (Location 1328)

around the world. By the time the experiment had ended, the chains had passed through over 60,000 people in 166 countries. (Location 1349)

able to estimate not only the length of the chains that made it to their targets, but also how long the chains that failed would have been had they continued. (Location 1350)

roughly half of all chains should be expected to reach their targets in seven steps or fewer. (Location 1352)

Rather, messages reached their targets through almost as many recipients as there were chains. (Location 1356)

Instead, they pass them to people they think have something in common with the target, like geographic proximity or a similar occupation, or they simply pass them to people they think will be likely to continue passing it along. (Location 1359)

The overall message here is that real social networks are connected in more complex and more egalitarian ways than Jacobs or even Milgram imagined—a result that has now been confirmed with many experiments, empirical studies, and theoretical models. (Location 1367)

A number of studies, for example, have suggested that social influence is mostly subconscious, arising out of subtle cues that we receive from our friends and neighbors, not necessarily by “turning to them” at (Location 1403)

But contagion also has important implications for influencers—because once you include the effects of contagion, the ultimate importance of an influencer is not just the individuals he or she influences directly but also all those influenced indirectly, via his neighbors, his neighbors’ neighbors, and so (Location 1441)

first that some people are more influential than others; (Location 1448)

second, that the influence of these people is greatly magnified by some contagion process that generates social epidemics. (Location 1449)

What we found was that under most conditions, highly influential individuals were indeed more effective than the average person in triggering social epidemics. (Location 1465)

The reason is simply that when influence is spread via some contagious process, the outcome depends far more on the overall structure of the network than on the properties of the individuals who trigger it. (Location 1473)

And as it turned out, the most important condition had nothing to do with a few highly influential individuals at all. Rather, it depended on the existence of a critical mass (Location 1476)

of easily influenced people who influence other easy-to-influence people. (Location 1477)

Conversely, when the critical mass did not exist, not even the most influential individual could trigger any more than a small cascade. The result is that unless one can see where particular individuals fit into the entire network, one cannot say much about how influential they will be—no matter what you can measure about them. (Location 1479)

Most of all, however, we found that the vast majority of attempted cascades—roughly 98 percent of the total—didn’t actually spread at all. (Location 1533)

The better the marketer can predict how large a cascade any particular individual can trigger, the more efficiently it can allocate its budget for sponsored tweets. (Location 1545)

In a nutshell, what we found was that individual-level predictions are extremely noisy. (Location 1552)

Just as with the Mona Lisa, for every individual who exhibited the attributes of a successful influencer, (Location 1554)

there were many other users with indistinguishable attributes who were not successful. (Location 1555)

Rather, the problem was that, like the simulations above, much of what drives successful diffusion depends on factors outside the control of the individual seeds. (Location 1557)

Like responsible financial managers, therefore, marketers should adopt a “portfolio” approach, targeting a large number of potential influencers and harnessing their average effect, thereby effectively reducing the individual-level randomness.24 (Location 1559)

Even though the Kim Kardashians of the world were indeed more influential than average, they were so much more expensive that they did not provide (Location 1573)

the best value for the money. Rather, it was what we called ordinary influencers, meaning individuals who exhibit average or even less-than-average influence, who often proved to be the most cost-effective means to disseminate information. (Location 1574)

Until it is possible to measure influence with respect to some outcome that we actually care about, and until someone runs the real-world experiments that can measure the influence of different individuals, every result—including ours—ought to be taken with a grain of salt. (Location 1587)

It is ironic in a way that the law of the few is portrayed as a counterintuitive idea because in fact we’re so used to thinking in terms of special people that the claim that a few special people do the bulk of the work is actually extremely natural. (Location 1599)

Teachers cheated on their students’ tests because that’s what their incentives led them to do. (Location 1610)

The Mona Lisa is the most famous painting in the world because it has all the attributes of the Mona Lisa. (Location 1611)

People have stopped buying gas-guzzling SUVs because social norms now dictate that people shouldn’t buy gas-guzzling SUVs. (Location 1612)

That is, think of history as a series of experiments in which certain general “laws” of cause (Location 1619)

We may still not be able to say what it is about the Mona Lisa that makes it uniquely great, but we do at least have some data. (Location 1623)

In order to be able to infer that “A causes B,” we need to be able to run the experiment many times. (Location 1627)

For problems of economics, politics, and culture—problems that involve many people interacting over time—the combination of the frame problem and the micro-macro problem means that every situation is in some important respect different from the situations we have seen before. (Location 1645)

Likewise, nobody thinks that by studying the success of the Mona Lisa we can realistically expect to understand much about the success and failure of contemporary artists. (Location 1649)

But rather than producing doubt, the absence of “counterfactual” versions of history tends to have the opposite effect—namely that we tend to perceive what actually happened as having been inevitable. (Location 1667)

phenomenon of hindsight bias, the after-the-fact tendency to think that we “knew it all along.” (Location 1670)

Hindsight bias, it turns out, can be counteracted by reminding people of what they said before they knew the answer or by forcing them to keep records of their predictions. (Location 1674)

Creeping determinism means that we pay less attention than we should to the things that don’t happen. (Location 1681)

And we notice when a new trend appears or a small company becomes phenomenally successful, but not all the times when potential trends or new companies disappear before even registering on the public consciousness. (Location 1685)

Yet because what we care about is success, it seems pointless—or simply uninteresting—to worry about the absence of success. Thus we infer that certain attributes are related to success when in fact they may be equally related to failure. (Location 1693)

Together, creeping determinism and sampling bias lead commonsense explanations to suffer from what is called the post-hoc fallacy. (Location 1730)

Rather, people like Revere, who after the fact seem to have been influential in causing some dramatic outcome, may instead be more like the “accidental influentials” that Peter Dodds and I found in our simulations—individuals whose apparent role actually depended on a confluence of other factors. (Location 1759)

problem was a misdiagnosis of pneumonia when the patient checked into the hospital. Instead of being isolated—the standard procedure for a patient infected with an unknown respiratory virus—the misdiagnosed SARS victim was placed in an open ward with poor air circulation. (Location 1769)

outbreak, it would have been a mistake to focus on superspreading individuals rather than the circumstances that led to the virus being spread. (Location 1776)

The inability to differentiate the “why” from the “what” of historical events presents a serious problem to anyone hoping to learn from the past. (Location 1794)

And yet as the Russian-British philosopher Isaiah Berlin argued, the kinds of descriptions that historians give of historical events wouldn’t have made much sense to the people who actually participated in them. (Location 1797)

… a succession of ‘accidents’ whose origins and consequences are, by and large, untraceable and unpredictable; only loosely strung groups of events forming an ever-varying pattern, following no discernable order.” (Location 1801)

Bezukhov; it could not give the kind of descriptions of what was happening that historians provide. (Location 1820)

Danto calls narrative sentences, meaning sentences that purport to be describing something that happened at a particular point in time but do so in a way that invokes knowledge of a later point. (Location 1821)

But in fact Danto’s point is precisely that historical descriptions of “what is happening” are impossible without narrative sentences—that narrative sentences are the very essence of historical explanations. (Location 1846)

History cannot be told while it is happening, therefore, not only because the people involved are too busy or too confused to puzzle it out, but because what is happening can’t be made sense of until its implications have been resolved. And when will that be? As it turns out, even this innocent question can pose problems for commonsense explanations. (Location 1852)

To avoid this error, therefore, we need to imagine “running” history many times, and comparing the different potential outcomes that Butch and the Kid might have experienced had they made different decisions. (Location 1866)

end. But in real life, the situation is far more ambiguous. Just as the characters in a story don’t know when the ending is, we can’t know when the movie of our own life will reach its final scene. (Location 1874)

We just won’t know until we know. And even then we still may not know, because it may not be entirely up to us to decide. (Location 1884)

which point we can evaluate, once and for all, the consequences of an action is a convenient fiction. (Location 1886)

sense (along with a number of bestselling business books) suggests that we should study the successful company, identify the key drivers of its success, and then replicate those practices and attributes in our own organization. (Location 1890)

Common sense would suggest that perhaps you should look somewhere else for a model of success. But how will you know that? And how will you know what will happen the year after, or the year after that? (Location 1893)

It’s actually quite remarkable in a way that we are able to completely rewrite our previous explanations without experiencing any discomfort about the one we are currently articulating, each time acting as if now is the right time to evaluate the outcome. (Location 1911)

more likely than those that are counterintuitive—even though, as we know from all those Agatha Christie novels, the most plausible explanation can be badly wrong. Finally, (Location 1937)

people are observed to be more confident about their judgments when they have an explanation at hand, even when they have no idea how likely the explanation is to be correct. (Location 1938)

we are inevitably also seeking insight that we hope we’ll be able to apply in the future—to improve our national security or the stability of our financial markets. (Location 1955)

We make this switch between storytelling and theory building so easily and instinctively that most of the time we’re not even aware that we’re doing (Location 1964)

Understanding the limits of what we can explain about the past ought therefore to shed light on what it is that we can predict about the future. (Location 1967)

One of the strange things about predictions, in fact, is that our eagerness to make pronouncements about the future is matched only by our reluctance to be held accountable for the predictions we make. (Location 1983)

Although the experts performed slightly better than random guessing, they did not perform as well as even a minimally sophisticated statistical model. Even more surprisingly, the experts did slightly better when operating outside their area of expertise than within (Location 1991)

He concluded that roughly 80 percent of all predictions were wrong, whether they were made by experts (Location 2000)

Results like these seem to show that humans are simply bad at making predictions, but in fact that’s not quite right either. (Location 2015)

The problem is that in Santa Fe it is sunny roughly 300 days a year, so one can be right 300 days out of 365 simply by making the mindless prediction that “tomorrow it will be sunny.” (Location 2019)

The real problem of prediction, in other words, is not that we are universally good or bad at it, but rather that we are bad at distinguishing predictions that we can make reliably from those that we can’t. (Location 2021)

For philosophers, the demon was controversial because in reducing the prediction of the future to a mechanical exercise, it seemed to rob humanity of free will. As (Location 2042)

receding for more than century now. But that doesn’t mean the demon has gone away. In spite of the controversy over free will, there was something incredibly appealing about the notion that the laws of nature, applied to the appropriate data, could be used to predict the future. (Location 2045)

The oscillations of pendulums and the orbits of satellites are therefore “simple” in this sense, even though it’s not necessarily a simple matter (Location 2053)

often describe relatively simple processes. (Location 2055)

Nobody really agrees on what makes a complex system “complex” but it’s generally accepted that complexity arises out of many interdependent components interacting in nonlinear ways. (Location 2063)

When every tiny factor in a complex system can get potentially amplified in unpredictable ways, there is only so much that a model can predict. (Location 2068)

but because incremental improvements make little difference in the face of the massive errors that remain. (Location 2070)

Yet pretty much everything in the social world—from the effect of a marketing campaign to the consequences of some economic policy or the outcome of a corporate plan—falls into the category of complex systems. (Location 2074)

In simple systems, that is, it is possible to predict with high probability what will actually happen—for example when Halley’s Comet will next return or what orbit a particular satellite will enter. (Location 2081)

For complex systems, by contrast, the best that we can hope for is to correctly predict the probability that something will happen. (Location 2083)

But even knowing this rule, we still can’t correctly predict the outcome of a single coin toss any more than 50 percent of the time, no matter what strategy we adopt. (Location 2088)

To understand why this kind of unpredictability is problematic, consider another example of a complex system about which we like to make predictions—namely, the weather. (Location 2095)

Thinking of future events in terms of probabilities is difficult enough for even coin tossing or weather forecasting, where more or less the same kind of thing is happening over and over again. (Location 2105)

So does it instead translate to the odds one ought to take in a gamble? That is, to win $10 if he is elected, I will have to bet $9, whereas if he loses, you can win $10 by betting only $ (Location 2110)

But how are we to determine what the “correct” odds are, seeing as this gamble will only ever be resolved once? (Location 2112)

At some level, we understand that these events could have played out differently. But no matter how much we might remind ourselves that things might be other than they are, it remains the case that what actually happened, happened. (Location 2119)

that when we think about the future, we care mostly about what will actually happen. (Location 2121)

But at the end of the day, we know that only one such possible future will actually come to be, and we want to know which one that is. (Location 2123)

Until it is actually realized, all we can say about the future stock price is that it has a certain probability of being within a certain range—not because it actually lies somewhere in this range and we’re just not sure where it is, but in the stronger sense that it only exists at all as a range of probabilities. (Location 2136)

between being uncertain about the future and the future itself being uncertain. (Location 2139)

The latter is an essentially random world, where the best we can ever hope for is to express our predictions of various outcomes as probabilities. (Location 2141)

The distinction between predicting outcomes and predicting probabilities of outcomes is a fundamental one that should change our view about what kinds of predictions we can make. (Location 2145)

Rather, what we care about is the very small number of predictions that, had we been able to make them correctly, might have changed things in a way that actually mattered. (Location 2150)

But what we don’t appreciate is that hindsight tells us more than the outcomes of the predictions that we could have made in the past. It also reveals what predictions we should have been making. (Location 2155)

that what is relevant cannot be known until later. (Location 2163)

But if we tried to state our predictions for everything that might conceivably happen, we would immediately drown in the possibilities. (Location 2165)

For example, in his book about prediction the political scientist and “predictioneer” Bruce Bueno de Mesquita extols the power of game theory to predict the outcomes of complex political negotiations. (Location 2171)

But that’s precisely the point: Making the right prediction is just as important as getting the prediction right. (Location 2180)

Even when we are dealing with more mundane types of predictions—like how consumers will respond to such and such a color or design, or whether doctors would spend more time on preventative care if they were compensated on the basis of patients’ health outcomes rather than the number and expense of their prescribed procedures—we have the same problem. (Location 2187)

For example, we are concerned about how customers will react to the color not because we care about the reaction per se, but because we want the product to be a success, and we think the color will matter. (Location 2192)

Once again, we care about things that matter, yet it is precisely these larger, more significant predictions about the future that pose the greatest difficulties. (Location 2195)

But what makes an event a black swan? This is where matters get confusing. We tend to speak about events as if they are separate and distinct, and can be assigned a level of importance in the way that we describe natural events such as earthquakes, avalanches, and storms by their magnitude or size. (Location 2201)

Earthquakes, by contrast, along with avalanches, storms, and forest fires, display “heavy-tailed” distributions, meaning that most are relatively small and draw little attention, whereas a small number can be extremely large. (Location 2205)

Rather, “events” in the historical sense acquire their significance via the transformations they trigger in wider social arrangements. (Location 2209)

The more you want to explain about a black swan event like the storming of the Bastille, in other words, the broader you have to draw the boundaries around what (Location 2221)

you consider to be the event itself. (Location 2222)

Presumably it is all these developments together that give the Internet its black swan status. But then the Internet isn’t really a thing at all. Rather, it’s shorthand for an entire period of history, and all the interlocking technological, economic, and social changes that happened therein. (Location 2228)

What made it a black swan, therefore, had less to do with the storm itself than it did with what happened subsequently: the (Location 2232)

When we talk about Hurricane Katrina as a black swan, in other words, we are not speaking primarily about the storm itself, but rather about the whole complex of events that unfolded around it, along with an equally complicated series of social, cultural, and political consequences—consequences that are still playing out. (Location 2237)

and hence we may have to make do with predicting probabilities of outcomes rather than the outcomes themselves— (Location 2241)

Black swans, by contrast, can only be identified in retrospect because only then can we synthesize all the various elements of history under a neat label. (Location 2243)

Likewise, when we look to the past, we do not feel any confusion about what we mean by the “events” that happened, nor does it seem difficult to say which of these events were important. (Location 2256)

Thus it follows that predicting the importance of events requires predicting not just the events themselves but also the outcome of the social process that makes sense of them. (Location 2262)

By the time the future has arrived we have already forgotten most of the predictions we might have made about it, and so are untroubled by the possibility that most of them might have been wrong, or simply irrelevant. (Location 2270)

By their very nature, effective marketing or public health plans do depend on being able to reliably associate cause and effect, and so do need to differentiate scientific explanation from mere storytelling. (Location 2278)

And finally, all these sorts of plans do often have consequences of sufficient magnitude—whether financial, or political, or social—that it is worth asking whether or not there is a better, uncommonsense way to go about making them. (Location 2280)

The message of the previous chapter is that the kinds of predictions that common sense tells us we ought to be able to make are in fact impossible—for two reasons. (Location 2286)

First, common sense tells us that only one future will actually play out, and so it is natural to want to make specific predictions about (Location 2287)

the best we can hope for is to reliably estimate the probabilities with which certain kinds of events will occur. (Location 2289)

common sense also demands that we ignore the many uninteresting, unimportant predictions that we could be making all the time, and focus on those outcomes that actually matter. (Location 2290)

But just because we can’t make the kinds of predictions we’d like to make doesn’t mean that we can’t predict anything at (Location 2295)

but by knowing the odds better than your opponents you can still make a lot of money over time by placing more informed bets, and winning more often than you lose. (Location 2297)

just knowing the limits of what’s possible can still be helpful—because it forces us to change the way we plan. (Location 2300)

roll. But as long as we can gather enough data on their past behavior, we can do a reasonable job of predicting probabilities, and that can be enough for many purposes. (Location 2308)

But credit card companies can do a surprisingly good job of predicting aggregate default rates by paying attention to a range of socioeconomic, demographic, and behavioral variables. (Location 2314)

As the political scientist Ian Ayres writes in his book Super Crunchers, predictions of this kind are being made increasingly in highly data-intensive industries like finance, healthcare, and e-commerce, where the often modest gains associated with data-driven predictions can add up over millions or even billions of tiny decisions—in some cases every day—to produce very substantial gains to the bottom line.2 (Location 2317)

For example, whenever a book publisher decides how much of an advance to offer a potential author, it is effectively making a prediction about the future sales of the proposed book. (Location 2322)

but they are considerably more complicated predictions than predictions about the number of flu cases expected in North America this winter, or the probability that a given user will click on a given ad online. (Location 2330)

Likewise predictions about movies, new drugs, and other kinds of business or development projects are, in effect, predictions about complex, multifaceted processes that play out over months or years. (Location 2334)

Decision makers often also have a lot of other data on which to draw—including market research, internal evaluations of the project in question, and their knowledge of the industry in general. So as long as nothing dramatic changes in the world between when they commit to a project and when it launches, then they are still in the realm of predictions that are at least possible to make reliably. How should they go about making them? (Location 2340)

In theory, in fact, no one should be able to consistently outperform a properly designed prediction market. The reason is that if someone could outperform the market, they would have an incentive to make money in it. But the very act of making money in (Location 2358)

These are the sorts of claims that the proponents of prediction markets tend to make, and it’s easy to see why they’ve generated so much interest. In recent years, in fact, prediction markets have been set up to make predictions as varied as the likely success of new products, the box office revenues of upcoming movies, and the outcomes of sporting events. (Location 2365)

Intrade, experienced a series of strange fluctuations when an unknown trader started placing very large bets on John McCain, generating large spikes in the market’s prediction for a McCain victory. (Location 2369)

which assumes that rational traders will not deliberately lose money. (Location 2375)

To try to settle the matter, my colleagues at Yahoo! Research and I conducted a systematic comparison of several different prediction methods, where the predictions in question were the outcomes of NFL football games. (Location 2382)

The first model relied only on the historical probability that home teams win—which they do 58 percent of the time—while the second model also factored in the recent win-loss records of the two teams in question. (Location 2388)

All of them performed about the same. To be fair, the two prediction markets performed a little better than the other methods, which is consistent with the theoretical argument above. But the very best performing method—the Las Vegas Market—was only about 3 percentage points more accurate than the worst-performing method, which was the model that always predicted the home team would win with 58 percent (Location 2392)

At the same time, however, it’s surprising that the aggregated wisdom of thousands of market participants, who collectively devote countless hours to analyzing upcoming games for any shred of useful information, is only incrementally better than a simple statistical model that relies only on historical averages. (Location 2398)

Yet when we compared the Hollywood Stock Exchange (HSX)—one of the most popular prediction markets, which has a reputation for accurate prediction—with a simple statistical model, the HSX did only slightly better. (Location 2422)

But beyond that, more elaborate methods like studying the die under a microscope to map out all the tiny fissures and irregularities on its surface, or building a complex computer simulation, aren’t going to help you much in improving your prediction. (Location 2433)

that the team with the better win-loss record should have a slight advantage, gives you another significant boost. (Location 2437)

Predictions about complex systems, in other words, are highly subject to the law of diminishing returns: The first pieces of information help a lot, but very quickly you exhaust whatever potential for improvement exists. (Location 2439)

experts are just as bad at making quantitative predictions as nonexperts and maybe even worse. (Location 2455)

Instead, what we should do is poll many individual opinions—whether experts or not—and take the average. (Location 2458)

And once again, although a fancy model may work slightly better than a simple model, the difference is small relative to using no model at (Location 2462)

As the psychologist Robyn Dawes once pointed out, “the whole trick is to know what variables to look at and then know how to add.” (Location 2465)

All else being equal, for example, the further in advance you predict the outcome of an event, the larger your error will be. It is simply harder to predict the box office potential of a movie at green light stage than a week or two before its release, no matter what methods you use. (Location 2468)

Individual people may be complicated and unpredictable, but they tend to be complicated and unpredictable in much the same way this week as they were last week, and so on average the models work reasonably well. (Location 2480)

like the onset of the financial crisis, the emergence of a revolutionary new technology, the overthrow of an oppressive regime, or a precipitous drop in violent crime—are interesting to us precisely because they are not regular times. (Location 2482)

If you could make millions, or even hundreds, of such bets, it would make sense to go with the historical probabilities. (Location 2498)

making one-off strategic decisions is therefore ill suited to statistical models or crowd wisdom. (Location 2502)

In part that’s because the techniques can be difficult to implement correctly, but mostly it’s because of the problem raised in the previous chapter—that there is simply a level of uncertainty about the future that we’re stuck with, and this uncertainty inevitably introduces errors into the best-laid plans. (Location 2505)

Ironically, in fact, the organizations that embody what would seem to be the best practices in strategy planning—organizations, for example, that possess great clarity of vision and that act decisively— (Location 2508)

Sony’s blunder was twofold: First, they focused on image quality over running time, thereby conceding VHS the advantage of being able to tape full-length movies. (Location 2513)

whereas VHS was “open,” meaning that multiple manufacturers could compete to make the devices, thereby driving down the price. (Location 2514)

this small lead then grew rapidly through a process of cumulative advantage. (Location 2516)

And if it had come to pass, the superior picture quality of Betamax might well have made up for the extra cost, while the shorter taping time may have been irrelevant. (Location 2521)

distribution to the outcome of the VCR wars, they acquired their own content repository in (Location 2532)

MiniDiscs held clear technical advantages over the then-dominant CD format. (Location 2533)

By all reasonable measures the MiniDisc should have been an outrageous success. And yet it bombed. (Location 2536)

Internet would have on production, distribution, and consumption of music. Nobody did. Sony, in other words, really was doing the best that anyone could have done to learn from the past and to anticipate the future—but they got rolled anyway, by forces beyond anyone’s ability to predict or control. (Location 2540)

This is the strategy paradox. The main cause of strategic failure, Raynor argues, is not bad strategy, but great strategy that just happens to be wrong. (Location 2552)

Bad strategy is characterized by lack of vision, muddled leadership, and inept execution—not the stuff of success for sure, but more likely to lead to persistent mediocrity than colossal failure. (Location 2553)

When applied to just the right set of commitments, great strategy can lead to resounding success—as it did for Apple with the iPod—but it can also lead to resounding failure. (Location 2555)

In particular, he recommends that planners look for ways to integrate what he calls strategic uncertainty—uncertainty about the future of the business you’re in—into the planning process itself. (Location 2560)

Critically, however, scenario planners attempt to sketch out a wide range of these hypothetical futures, where the main aim is not so much to decide which of these scenarios is most likely as to challenge possibly unstated assumptions that underpin existing strategies. (Location 2564)

Optimizing for strategic flexibility, by contrast, would have led Sony to identify elements that would have worked no matter which version of the future played out, and then to hedge the residual uncertainty, perhaps by tasking different operating divisions to develop higher- and lower-quality models to be sold at different price points. (Location 2580)

However, it is also a time-consuming process—constructing scenarios, deciding what is core and what is contingent, devising strategic hedges, and so on—that necessarily diverts attention from the equally important business of running a company. (Location 2583)

Ultimately, the main problem with strategic flexibility as a planning approach is precisely the same problem that it is intended to solve—namely that in hindsight the trends that turned out to shape a given industry always appear obvious. (Location 2621)

Every year, the various industries in the business of designing, producing, selling, and commenting on shoes, clothing, and apparel are awash in predictions for what could be, might be, should be, and surely will be the next big thing. (Location 2635)

That company is Zara, the Spanish clothing retailer that has made business press headlines for over a decade with its novel approach to satisfying consumer demand. Rather than trying to anticipate what shoppers will buy next season, Zara effectively acknowledges that it has no idea. (Location 2640)

And finally, it has a very flexible manufacturing and distribution operation that can react quickly to the information that is coming directly from stores, dropping those styles that aren’t selling (with relatively little left-over inventory) and scaling up those that (Location 2645)

that planners should rely less on making predictions about long-term strategic trends and more on reacting quickly to changes on the ground. (Location 2653)

they should instead improve their ability to learn about what is working right now. (Location 2654)

dropping alternatives that are not working—no matter how promising they might have seemed in advance—and diverting resources to those that are succeeding, or even developing new alternatives on the fly.2 (Location 2655)

Clearly Mechanical Turk, along with other potential crowdsourcing solutions, comes with some limitations—most obviously the representativeness and reliability of the turkers. To many people it seems strange that anyone would work for pennies on mundane tasks, and therefore one might suspect either that the turkers are not representative of the general population or else that they do not take the work seriously. These (Location 2713)

Finally, even where their reliability is poor—which sometimes it is—it can often be boosted through simple techniques, like soliciting independent ratings for every piece of content from several different turkers and taking the majority or the average score. (Location 2719)

Determining what kind of behavior can be predicted using searches, as well as the accuracy of such predictions and the timescale over which predictions can be usefully made are therefore all questions that researchers are beginning to address. (Location 2740)

All these predictions were made at most a few weeks in advance of the event itself, so we are not talking about long-term predictions here—as discussed in the previous chapter, those are much harder to make. Nevertheless, even having a slightly better idea a week in advance of audience interest might help a movie studio or a distributor decide how many screens to devote to which movies in different local regions.15 (Location 2744)

As I discussed in the last chapter, simple models based on historical data are surprisingly hard to outperform, and the same rule applies to search-related data as well. (Location 2749)

the state of the world ought to change the conventional mind-set toward planning. Rather than predicting how people will behave and attempting to design ways to make consumers respond in a particular way—whether to an advertisement, a product, or a policy—we can instead measure directly how they respond to a whole range of possibilities, and react accordingly. (Location 2759)

Only once we concede that we cannot depend on our ability to predict the future are we open to a process that discovers it.16 (Location 2763)

In theory, of course, everyone “knows” that correlation and causation are different, but it’s so easy to get the two mixed up in practice that we do it all the time. If we go on a diet and then subsequently lose weight, it’s all too tempting to conclude that the diet caused the weight loss. (Location 2787)

But one simple solution, at least in principle, is to run an experiment in which the “treatment”—whether the diet or the ad campaign—is applied in some cases and not in others. If the effect of interest (weight loss, increased sales, etc.) happens significantly more in the presence of the treatment than it does in the “control” group, we can conclude that it is in fact causing the effect. (Location 2798)

Without experiments, moreover, it’s extremely difficult to measure how much of the apparent effect of an ad was due simply to the predisposition of the person viewing (Location 2811)

are bringing about a new era of controlled experiments in business. (Location 2857)

For decisions like these, it’s unlikely that an experimental approach will be of much help; nevertheless, the decisions still have to get made. (Location 2877)

According to Scott, the central flaw in this “high modernist” philosophy was that it underemphasized the importance of local, context-dependent knowledge in favor of rigid mental models of cause and effect. (Location 2887)

Friedrich Hayek, who argued that planning was fundamentally a matter of aggregating knowledge. (Location 2896)

Yet it is precisely the aggregation of all this information that markets achieve every day, without any oversight or direction. If, for example, someone, somewhere invents a new use for iron that allows him to make more profitable use of it than anyone else, that person will also be willing to pay more for the iron than anyone else will. (Location 2899)

Market-based mechanisms like cap and trade do indeed seem to have more chance of working than centralized bureaucratic solutions. But market-based mechanisms are not the only way to exploit local knowledge, nor are they necessarily the best way. (Location 2914)

Another nonmarket approach to harnessing local knowledge that is increasingly popular among governments and foundations alike is the prize competition. (Location 2919)

allowing anyone to work on the problem, but only rewarding solutions that satisfy prespecified objectives. (Location 2921)

Market-based solutions and prize competitions are both good ideas, but they’re not the only way that centralized bureaucracies can take advantage of local knowledge. (Location 2939)

a philosophy that has begun to gain popularity in the world of economic development. (Location 2950)

Most important, what both bright spots and bootstrapping have in common is that they require a shift in mind-set on the part of planners. (Location 2969)

creating a more nutritious diet in impoverished villages, reducing infection rates in hospitals, or improving the competitiveness of local industries—chances are that somebody out there already has part of the solution and is willing to share it with others. (Location 2970)

having realized that they do not need to figure out the solution to every problem on their own, planners can instead devote their resources to finding the existing solutions, wherever they occur, and spreading their practice more widely.31 (Location 2972)

are really just variations on the same general theme of “measuring and reacting.” (Location 2984)

What they all have in common, however, is that they require planners—whether government planners trying to reduce global poverty or advertising planners trying to launch a new campaign for a client—to abandon the conceit that they can develop plans on the basis of intuition and experience alone. (Location 2989)

It’s a horrible thought, and Herrera has every right to hate the man who destroyed his (Location 3033)

And conversely, even if you subscribe to Judge Feldman’s logic that everyone who is driving a van drunk down a city street is a potential killer of mothers and children, it is hard to imagine charging every driver who has had a few too many drinks—or these days, anyone texting or talking on a cell phone—to fifteen years in prison, simply on the grounds that they might have killed someone. (Location 3050)

If great harm is caused, great blame is called for—and conversely, if no harm is (Location 3054)

caused, we are correspondingly inclined to leniency. (Location 3054)

On the one hand, it seems an outrage not to punish a man who killed four innocent people with the full force of the law. And on the other hand, it seems grossly disproportionate to treat every otherwise decent, honest person who has ever had a few too many drinks and driven home as a criminal and a killer. Yet aside from the trembling hand of fate, there is no difference between these two instances. (Location 3058)

But occasionally an infraction is striking or serious enough that the rules have to be invoked, and the offender dealt with officially. (Location 3066)

Yet the rules nevertheless serve a larger, social purpose of providing a rough global constraint on acceptable behavior. (Location 3069)

Whether we are passing judgment on a crime, weighing up a person’s career, assessing some work of art, analyzing a business strategy, or evaluating some public policy, our evaluation of the process is invariably and often heavily swayed by our knowledge of the outcome, even when that outcome may have been driven largely by chance. (Location 3081)

Firms that are successful are consistently rated as having visionary strategies, strong leadership, and sound execution, while firms that are performing badly are described as suffering from some combination of misguided strategy, poor leadership, or shoddy execution. (Location 3093)

But as Rosenzweig shows, better information is not on its own any defense against the Halo Effect. (Location 3104)

The problem, in fact, is not that there is anything wrong with evaluating processes in terms of outcomes—just that it is unreliable to evaluate them in terms of any single outcome. (Location 3115)

for example, then by keeping track of all their successes and failures, we can indeed hope to determine their quality directly. (Location 3117)

Planning techniques like scenario analysis and strategic flexibility, which I discussed earlier, can help organizations expose questionable assumptions and avoid obvious mistakes, while prediction markets and polls can exploit the collective intelligence of their employees to evaluate the quality of plans before their outcome is known. (Location 3119)

But really it is just a symptom of a deeper problem with the whole notion of pay for performance—a problem that revolves around the Halo Effect. (Location 3130)

From a pragmatic perspective, moreover, it’s also entirely possible that if profit-generating employees aren’t compensated accordingly, they will leave for other firms, just as their bosses kept saying. (Location 3135)

Every year you flip a coin: If it comes up heads, you have a “good” year; and if it comes up tails, you have a “bad” year. Let’s assume that your bad years are really bad, meaning that you lose a ton of money for your employer, but that in your good years you earn an equally outsized profit. (Location 3141)

He feels that his success is based on his skill, experience, and hard work, not luck, and that his colleagues committed errors of judgment that he has avoided. (Location 3149)

Consider, for example, the case of Bill Miller, the legendary mutual fund manager whose Value Trust beat the S&P 500 fifteen years straight—something no other mutual fund manager has ever accomplished. (Location 3163)

Just as Michael Raynor explained for business strategies, like Sony versus Matsushita in the video war described in Chapter 7, investing strategies can be successful or unsuccessful for several years in a row for reasons that have nothing to do with skill, and everything to do with luck. (Location 3172)

Ideally then, we would like to have the equivalent of a batting average to measure performance in different professions. But outside of sports, unfortunately, such statistics are not so easy to put together. (Location 3183)

And finance is in many respects an easy case—because the existence of indices like the S&P 500 at least provide agreed-upon benchmarks against which an individual investor’s performance can be measured. (Location 3194)

Much of life, however, is characterized by what the sociologist Robert Merton called the Matthew Effect, named after a sentence from the book of Matthew in the Bible, which laments (Location 3205)

“For to all those who have, more will be given, and they will have an abundance; but from those who have nothing, even what they have will be taken away.” Matthew was referring specifically to wealth (hence the phrase “the rich get richer and the poor get poorer”), (Location 3207)

Success early on in an individual’s career, that is, confers on them certain structural advantages that make subsequent successes much more likely, regardless of their intrinsic aptitude. (Location 3209)

Once someone is perceived as a star, in other words, not only can he attract more resources and better collaborators—thus producing far more than he would otherwise have been able to—he also tends to get more than his fair share of the credit for the resulting work.15 (Location 3216)

Success leads to prominence and recognition, which leads in turn to more opportunities to succeed, more resources with which to achieve success, and more likelihood of your subsequent successes being noticed and attributed to you. (Location 3220)

For example, it is known that college students who graduate during a weak economy earn less, on average, than students who graduate in a strong economy. (Location 3224)

As Miller himself has emphasized, statistics like his fifteen-year streak are as much artifacts of the calendar as indicators of his talent.17 Nor is it even the case that his talent ought to be judged by his cumulative career success—because that too could be undone by a single unlucky stroke. As unsatisfying as it may sound, therefore, our best bet for evaluating his talent may be simply to observe his investing process itself. (Location 3242)

And that is our tendency to attribute the lion’s share of the success of an entire corporation, employing tens of thousands of talented engineers, designers, and managers to one individual. (Location 3271)

corporate performance is generally determined less by the actions of CEOs than by outside factors, (Location 3280)

like the performance of the overall industry or the economy as a whole, over which individual leaders have no control. (Location 3281)

If boards were more willing to question the very idea of the irreplaceable CEO, and if searches for CEOs were then opened to a wider pool of candidates, it would be more difficult for candidates to negotiate such extravagant packages at the outset. (Location 3300)

Rawls, by contrast, asked what kind of society each of us would choose to live in if we didn’t know beforehand where in the socioeconomic hierarchy we would end up. Rawls reasoned that any rational person would prefer an egalitarian society—one in which the worst off were as well off as possible—over one in which a few people were very rich and many were very poor, because the odds of being one of the very rich was so small.24 (Location 3307)

Nozick’s argument was appealing to many people, and not only because it provided a philosophical rationale for low taxes. By reasoning about what would be considered fair in a hypothetical “state of nature,” Nozick’s arguments also played well to commonsense notions of individual success and failure. (Location 3315)

which disproportionately large rewards can accrue to individuals who happen to possess particular attributes and who experience the right opportunities. (Location 3321)

Allowing for the possibility that through hard work and application of one’s talents one can do better than one’s peers is no doubt beneficial for society as a whole—just as libertarians believe. (Location 3331)

And if the rules of the game have it that basketball players earn more than gymnasts, or investment bankers earn more than teachers, so be it. (Location 3333)

all of which are designed to maximize safety, occasionally suffer catastrophic failures. (Location 3369)

attacks. It may be, in fact, that once a system has attained a certain level of complexity, there is no way to rule out the possibility of failure. (Location 3371)

In the bankers’ view, they had already paid back their bailout money—with interest—and therefore nothing more could legitimately be asked of them. (Location 3376)

Whatever we might like to think, we are never entirely free, nor would we want to be. The very ties that give our lives meaning also constrain us, and it is precisely by constraining us that they give us meaning. From Sandel’s perspective, it makes no more sense to reason about fairness or justice exclusively from the perspective of individual freedom than it does to reason about what is fair exclusively by analogy with some imaginary state of nature. (Location 3407)

Sandel’s view harks back to the ancient philosophy of Aristotle, who also believed that questions of justice require reasoning about the purpose of things. (Location 3423)

As it turns out, this tendency to judge sociology by the standards of physics is an old one, going all the way back to Auguste Comte, the nineteenth-century philosopher who is often credited as the founding father of sociology. (Location 3474)

Merton instead advocated that sociologists should focus on developing “theories of the middle range,” meaning theories that are broad enough to account for more than isolated phenomena but specific enough to say something concrete and useful. (Location 3512)

One response to this problem, as Lazarsfeld’s colleague Samuel Stouffer noted more than sixty years ago, is for sociologists to depend less on their common sense, not more, and instead try to cultivate uncommon sense. (Location 3549)

Finally, however, we found that individuals who were already “close” to each other, either because they shared mutual friends or belonged to the same groups, were more similar than distant pairs, and that most of the bias toward (Location 3608)

Our conclusion was that although the individuals in our community did exhibit some preference for others who were similar, it was a relatively weak preference that had been amplified over time, by successive “rounds” of choices, to generate the appearance of a much stronger preference in the observed network.18 (Location 3610)

The social world, in other words, is far messier than the physical world, and the more we learn about it, the messier it is likely to seem. (Location 3679)

what can social scientists hope to discover that an ordinary intelligent person couldn’t figure out on his or her own? Surely any thoughtful person could figure out just by introspection that we are all influenced by the opinions of our family and friends, that context matters, and that all things are relative. (Location 3687)

Any thoughtful person knows, of course, that the future is unpredictable, and that past performance is no guarantee of future returns. He or she would also know that humans are biased and sometimes irrational, that (Location 3692)

Likewise, we know that people in social networks tend to cluster together in relatively homogeneous groups. But what we can’t infer from our own observations of the world is whether these patterns are driven by psychological preferences or structural constraints. (Location 3701)

but we do not know how much of that unpredictability could be eliminated simply by thinking through the possibilities more carefully, and how much is inherently random in the way that a roll of the dice is random. (Location 3704)