So why is it that rocket science seems hard, whereas problems having to do with people—which arguably are much harder—seem like they ought to be just a matter of common sense? In this book, I argue that the key to the paradox is common sense itself.
The paradox of common sense, therefore, is that even as it helps us make sense of the world, it can actively undermine our ability to understand it. If you don’t quite understand what that last sentence means, that’s OK, because explaining it, along with its implications for policy, planning, forecasting, business strategy, marketing, and social science is what the rest of this book is about (pp. xvii-xviii).
作者提出常識有兩項特徵，即注重實踐與因應實際情況解決問題︰
The first of these features is that unlike formal systems of knowledge, which are fundamentally theoretical, common sense is overwhelmingly practical, meaning that it is more concerned with providing answers to questions than in worrying about how it came by the answers.
The second feature that differentiates common sense from formal knowledge is that while the power of formal systems resides in their ability to organize their specific findings into logical categories described by general principles, the power of common sense lies in its ability to deal with every concrete situation on its own terms (p.9).
因此常識彼此之間並沒有一致邏輯，例如不同諺語就有時互相矛盾。這在應對日常生活並無問題，但在我們以常識解決超出日常生活問題，如戰爭、經濟，政治等 就可能會出錯，因為這些問題過於複雜︰
Where it does start to matter, however, is when we use common sense to solve problems that are not grounded in the immediate here and now of everyday life—problems that involve anticipating or managing the behavior of large numbers of people, in situations that are distant from us either in time or space.
In none of these cases are we using our common sense to reason about how we should behave in the here and now. Rather, we are using it to reason about how other people behaved—or will behave—in circumstances about which we have at best an incomplete understanding. At some level we understand that the world is complicated, and that everything is somehow connected to everything else. But when we read some story about reforming the healthcare system, or about banker bonuses, or about the Israel-Palestine conflict, we don’t try to understand how all these different problems fit together. We just focus on the one little piece of the huge underlying tapestry of the world that’s being presented to us at that moment, and form our opinion accordingly (pp. 18-19).
儘管社會問題非常複雜，有趣的是我們對社會問題總是比科學問題更有自信︰
We are constantly immersed in markets, politics, and culture, and so are intimately familiar with how they work—or at least that is how it seems to us. Unlike problems in physics, biology, and so on, therefore, when the topic is human or social behavior, the idea of running expensive, time-consuming “scientific” studies to figure out what we’re pretty sure we already know seems largely unnecessary (p. 25).
作者歸納出三種常識的誤差︰過於注重誘因、動機與信念等有意識的因素；以為集體行為反映某位或某群代表人物的邏輯；過於強調歷史對將來的參考價值。之後幾章作者就討論這三種誤差。
我們的思考方式有時會超出我們想像，作者舉出德國與奧地利器官捐贈率的差異為例︰
And for all their creativity, my students never pegged the real reason, which is actually absurdly simple: In Austria, the default choice is to be an organ donor, whereas in Germany the default is not to be. The difference in policies seems trivial—it’s just the difference between having to mail in a simple form and not having to—but it’s enough to push the donor rate from 12 percent to 99.9 percent. And what was true for Austria and Germany was true across all of Europe—all the countries with very high rates of organ donation had opt-out policies, while the countries with low rates were all opt-in (pp. 31-32).
這說明預設選項對行為可以有顯著影響。作者也舉出其他影響行為的心理學現象，priming是指某些背景音樂、文字、影像、顏色可以誘導行為； anchoring與adjustment是提及不相關的數字會令估算改變；framing是在兩者之間加入第三項選擇會令最初兩者的評價改變，即使選擇的人從不會選那第三選項；是否容易回想或接觸某項資訊會影響判斷；只記住與自己立場一致資訊的confirmation bias，以及對與自己立場不同資訊特別懷疑的motivated reasoning，則會鞏固自己的立場，令分歧更難解決。
除了這些心理學現象外，在某情境中究竟甚麼事相關也不容易釐清，這就是frame problem︰要知道甚麼特徵與這情境相關就要將這情境與類似情境比較，但要知道甚麼情境可以比較又要知道甚麼特徵相關，這就陷入迴圈。
上述心理學現象與frame problem說明，像理性選擇與常識那樣，聲稱人們因某種既定原因而行動這種說法難以成立︰
Like rational choice theory, in other words, common sense insists that people have reasons for what they do—and this may be true. But it doesn’t necessarily allow us to predict in advance either what they will do or what their reasons will be for doing it. Once they do it, of course, the reasons will appear obvious, and we will conclude that had we only known about some particular factor that turned out to be important, we could have predicted the outcome. After the fact, it will always seem as if the right incentive system could have produced the desired result. But this appearance of after-the-fact predictability is deeply deceptive, for two reasons. First, the frame problem tells us that we can never know everything that could be relevant to a situation. And second, a huge psychological literature tells us that much of what could be relevant lies beyond the reach of our conscious minds (pp. 52-53).
常識在解釋某事為何發生時不時會出現循環論證，例如說蒙羅麗莎成功是因為某些特徵，但解釋為甚麼同樣有這些特徵的畫作不像蒙羅麗莎成功時，又會說出蒙羅麗莎的獨特之處，這樣蒙羅麗莎成功的原因就變成因為它是蒙羅麗莎。同樣，在解釋一項新的社會現象時，人們會說是因為社會習慣改變，但社會習慣改變的證據，卻正是有一項新的社會現象出現。作者指出常識解釋的循環論證來自於micro-macro problem，即社會宏觀現象怎樣由微觀個體組成︰
The circularity evident in commonsense explanations is important to address because it derives from what is arguably the central intellectual problem of sociology—which sociologists call the micro-macro problem. The problem, in a nutshell, is that the outcomes that sociologists seek to explain are intrinsically “macro” in nature, meaning that they involve large numbers of people.
So how do we get from the micro choices of individuals to the macro phenomena of the social world? Where, in other words, do families, firms, markets, cultures, and societies come from, and why do they exhibit the particular features that they exhibit? This is the micro-macro problem (pp. 61-62).
作者認為常識忽略社會系統中不同人互動會引起新的現象，令社會系統更為複雜︰
Social systems are also replete with interactions... Individual people are influenced by what other people are doing or saying or wearing.... In the kinds of systems that sociologists study, in fact, the interactions come in so many forms and carry such consequence, that our own version of emergence—the micro-macro problem— is arguably more complex and intractable than in any other discipline.
Common sense, however, has a remarkable knack for papering over this complexity. 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 (p. 64).
常識及某些社會理論提倡的representative agent，就是這種忽略互動的典型例子︰
By ignoring the interactions between thousands or millions of individual actors, the representative agent simplifies the analysis of business cycles enormously. It assumes, in effect, that as long as economists have a good model of how individuals behave, they effectively have a good model for how the economy behaves as well. In eliminating the complexity, however, the representative-agent approach effectively ignores the crux of the micro-macro problem—the very core of what makes macroeconomic phenomena “macro” in the first place. It was for precisely this reason, in fact, that the economist Joseph Schumpeter, who is often regarded as the founding father of methodological individualism, attacked the representative-agent approach as flawed and misleading (pp. 66-67).
作者引用Granovetter的暴動模型說明互動為何重要，假設各有100人在A市與B市示威，每個人在n人參與暴力行為時就會加入，n在A市與B市100人中的分佈如下︰
A: 0, 1, 2, 3, 4, 5, 6... 99
B: 0, 1, 2, 4, 4, 5, 6... 99
這樣在A市中第一個人出現暴力行為就會觸發第二個人加入，連鎖效應下最終100人都參與暴力行為；而B市第一個人有暴力行為，觸發第二、三個人參與，但僅此為止，因為只有三個人參與的暴力行為觸發不到要四個人參與才加入的個體加入。A市與B市的n分佈其實相當相似，卻因為個體互動的微細差異導致結果非常不同。常識解釋並沒有顧及這種微細差異，反而可能會決為結果相當不同而判斷在A市與B市參與示威的群體本質有別。作者表示︰
To understand how the different outcomes emerge, you must take into account the interactions between individuals, which in turn requires that you follow the full sequence of individual decisions, each unfolding on top of the others. This is the micro-macro problem arriving in full force. And the minute you try to skip over it, say by substituting a representative agent for the behavior of the collective, you will have missed the whole essence of what is happening, no matter what you assume about the agent (p. 71).
常識解釋也沒有注意到cumulative advantage，也就是起初些微差異可導致結果相當不同；而在人們互相影響的世界中，選擇也有部份不可預測︰
Introducing social influence into human decision making, in other words, increased not just inequality but unpredictability as well. Nor could this unpredictability be eliminated by accumulating more information about the songs any more than studying the surfaces of a pair of dice could help you predict the outcome of a roll. Rather, unpredictability was inherent to the dynamics of the market itself (p. 77).
常識認為某些特殊人士交遊廣闊，對他人特別有影響，帶領潮流。但作者指出社會網絡比這種解釋更為複雜︰
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. In spite of all this evidence, however, when we think about how social networks work, we continue to be drawn to the idea that certain “special people,” whether famous wives of presidents or gregarious local businessmen, are disproportionately responsible for connecting the rest of us. Evidence, in fact, seems to have very little to do with why we think this way (p. 89).
作者藉由電腦模擬模型發現，「有影響力的人」影響比常識想像中低︰
What we found was that under most conditions, highly influential individuals were indeed more effective than the average person in triggering social epidemics. But their relative importance was much less than what the law of the few would suggest (p. 96).
The majority of the work was being done not by a tiny percentage of people who acted as the triggers, but rather by the much larger critical mass of easily influenced people. What we concluded, therefore, is that the kind of influential person whose energy and connections can turn your book into a bestseller or your product into a hit is most likely an accident of timing and circumstances. An “accidental influential” as it were (pp. 97-98).
作者在twitter的研究中在發現有影響力與否似乎與個人特徵無關︰
In a nutshell, what we found was that individual-level predictions are extremely noisy. Even though it was the case that on average, individuals with many followers who had been successful at triggering cascades of retweets in the past were more likely to be successful in the future, individual cases fluctuated wildly at random. Just as with the Mona Lisa, for every individual who exhibited the attributes of a successful influencer, there were many other users with indistinguishable attributes who were not successful (p. 103).
作者認為，與其他常識解釋相似，「少數法則」也是另一種循環論證︰
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. We think that by acknowledging the importance of interpersonal influence and social networks, we have somehow moved beyond the circular claim from the previous chapter that “X happened because that’s what people wanted.” But when we try to imagine how a complex network of millions of people is connected—or worse still, how influence propagates through it—our intuition is immediately defeated. By effectively concentrating all the agency into the hands of a few individuals, “special people” arguments like the law of the few reduce the problem of understanding how network structure affects outcomes to the much simpler problem of understanding what it is that motivates the special people. As with all commonsense explanations, it sounds reasonable and it might be right. But in claiming that “X happened because a few special people made it happen,” we have effectively replaced one piece of circular reasoning with another (p. 106).
作者指出從歷史中學習容易造成誤差，首先是因為 creeping determinism，即認為所有已發生的事都是理所當然︰
In reality, of course, this experiment got run only once, and so we never got to see all the other versions of it that may or may not have turned out differently. As a result, we can’t ever really be sure what caused the drop in violence. 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.
This tendency, which psychologists call creeping determinism, is related to the better-known phenomenon of hindsight bias, the after-the-fact tendency to think that we “knew it all along”(p. 112).
另一原因是sampling bias ，我們往往會記住較罕見卻有意思的事情，而不會留意較常見但沒意思的相反事例︰
Just as with our tendency to emphasize the things that happened over those that didn’t, our bias toward “interesting” things is completely understandable. Why would we be in terested in uninteresting things? Nevertheless, it exacerbates our tendency to construct explanations that account for only some of the data.
This problem of “sampling bias” is especially acute when the things we pay attention to—the interesting events— happen only rarely (pp. 113-114).
而creeping determinism加上sampling bias就會造成post-hoc fallacy，即認發生在前某事件是發生在後另一事件的原因︰
Together, creeping determinism and sampling bias lead commonsense explanations to suffer from what is called the post-hoc fallacy. The fallacy is related to a fundamental requirement of cause and effect—that in order for A to be said to cause B, A must precede B in time... All of this is fine. But just because B follows A doesn’t mean that A has caused B... It’s an obvious point, and in the physical world we have good enough theories about how things work that we can usually sort plausible from implausible. But when it comes to social phenomena, common sense is extremely good at making all sorts of potential causes seem plausible. The result is that we are tempted to infer a cause-and-effect relationship when all we have witnessed is a sequence of events. This is the post-hoc fallacy (p. 118).
作者以SARS在香港爆發為例，所謂「超級帶菌者」與其說在醫學上與其他患者有別，不如說正是因為他是實際歷史中的源頭而被稱為「超級帶菌者」，當中原因包括各種巧合。如果歷史重演，「超級帶菌者」就可能另有其人。作者稱歷史是描繪甚麼事發生，而不是解釋事情為何發生︰
On the one hand, common sense excels in generating plausible causes, whether special people, or special attributes, or special circumstances. And on the other hand, history obligingly discards most of the evidence, leaving only a single thread of events to explain. Commonsense explanations therefore seem to tell us why something happened when in fact all they’re doing is describing what happened (p. 122).
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 (p. 126).
In the absence of experiments, therefore, our storytelling abilities are allowed to run unchecked, in the process burying most of the evidence that is left, either because it’s not interesting or doesn’t fit with the story we want to tell. Expecting history to obey the standards of scientific explanation is therefore not just unrealistic, but fundamentally confused—it is, as Berlin concluded, “to ask it to contradict its essence” (p. 133).
作者提到預測的問題在於我們不清楚甚麼可以預測，例如前面提及過社會行為如何複雜︰
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 (p. 138).
Whenever people get together... they affect one another’s thinking and behavior. As I discussed in Chapter 3, it is these interactions that make social systems “social” in the first place—because they cause a collection of people to be something other than just a collection of people. But in the process they also produce tremendous complexity (p. 142).
我們想要的預測不是未來某事發生機率高低，而是它會不會實際發生︰
It follows naturally, therefore, that when we think about the future, we care mostly about what will actually happen. To arrive at our prediction, we might contemplate a range of possible alternative futures, and maybe we even go as far as to determine that some of them are more likely than others. 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 (p. 145).
然而正如前述，在預測時有甚麼資訊相關只能在事後得知︰
In effect, this problem is the flip side of Danto’s argument about history in the previous chapter—that what is relevant cannot be known until later. The kinds of predictions we most want to make, that is, require us to first determine which of all the things that might happen in the future will turn out to be relevant, in order that we can start paying attention to them now (p. 149).
例如預測所謂「黑天鵝」，即出現率極低的重大事件時，就不可能事先知道甚麼才重要，故此連要預測甚麼也不清楚︰
Predicting black swans is therefore fundamentally different than predicting events like plane crashes or changes in the rate of unemployment. The latter kind of event may be impossible to predict with certainty—and hence we may have to make do with predicting probabilities of outcomes rather than the outcomes themselves—but it is at least possible to say in advance what it is that we are trying to predict. 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. Predicting black swans, in other words, requires us not only to see the future outcome about which we’re making a prediction but also to see the future beyond that outcome, because only then will its importance be known.
Nevertheless, once we know about black swans, we can’t help wishing that we had been able to predict them. And just as commonsense explanations of the past confuse stories with theories—the topic of the last chapter—so too does commonsense intuition about the future tend to conflate predictions with prophecies (p. 154-155).
常識預測犯錯在日常生活中問題不大，因為錯誤終會隨時間忘卻，而且應對日常生活也不需要連貫一致的解釋。但當預測用於影響重大的政策或市場時，運用常識預測就有所不足︰
Where these mistakes do start to have important consequences is when we rely on our common sense to make the kinds of plans that underpin government policy or corporate strategy or marketing campaigns. By their very nature, foreign policy or economic development plans affect large numbers of people over extended periods of time, and so do need to work consistently across many different specific contexts. 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. By their very nature, strategic plans, whether for corporations or political parties, do necessarily make predictions about the future, and so do need to differentiate predictions that can be made reliably from those that cannot. 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 i
s a better, uncommonsense way to go about making them (p. 157).
接著作者就開始詳細討論預測，首先是prediction market，即以眾人判斷的比例來評估未來某事發生機率，其問題則是容易受別有用心的人操縱。之後作者提及考量眾多變項的預測模型，表現往往不比簡單模型優勝多少，作者以美式足球聯盟分析為例︰
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 (p. 169).
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 (p. 172).
紀錄預測表現可反映預測是否值得重視︰
As I mentioned at the beginning of the previous chapter, keeping track of our predictions is not something that comes naturally to us: We make lots of predictions, but rarely check back to see how often we got them right. But keeping track of performance is possibly the most important activity of all—because only then can you learn how accurately it is possible to predict, and therefore how much weight you should put on the predictions you make (p. 174).
有時良好策略也會造成失敗的結果，這就是strategy paradox︰
This is the strategy paradox. The main cause of strategic failure... is not bad strategy, but great strategy that just happens to be wrong. Bad strategy is characterized by lack of vision, muddled leadership, and inept execution... Great strategy, by contrast, is marked by clarity of vision, bold leadership, and laser-focused execution. 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. Whether great strategy succeeds or fails therefore depends entirely on whether the initial vision happens to be right or not. And that is not just difficult to know in advance, but impossible (p. 180 .
即使為策略帶來彈性也可能因無法事先知道甚麼是關鍵轉變而失敗︰
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. And as a result, when we revisit history it is all too easy to persuade ourselves that had we been faced with a strategic decision “back then,” we could have boiled down the list of possible futures to a small number of contenders—including, of course, the one future that did in fact transpire. But when we look to our own future, what we see instead is myriad potential trends, any one of which could be game changing and most of which will prove fleeting or irrelevant. How are we to know which is which? And without knowing what is relevant, how wide a range of possibilities should we consider? (p. 185)
既然策略無論如何也有可能失敗，另一種可能取態就是擴大應變能力以迅速回應現狀，作者以 Zara的emergent strategy銷售方式為例︰
Rather than attempting to anticipate correctly what will work in the future, that is, they should instead improve their ability to learn about what is working right now. Then, like Zara, they should react to it as rapidly as possible, 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 (p. 188).
另一例子是網上媒體的 crowdsourcing，由不同作者供稿並主力推薦已有讀者迴響的文章，重點是由事先計劃轉為理解現狀並回應︰
The real point is that our increasing ability to measure 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. In other words, the shift from “predict and control” to “measure and react” is not just technological—although technology is needed—but psychological. Only once we concede that we cannot depend on our ability to predict the future are we open to a process that discovers it (pp. 195-196).
作者指出廣告成效需要由實驗測試，實驗結果也可用於改進廣告效益︰
Without experiments, it’s actually close to impossible to ascertain cause and effect, and therefore to measure the real return on investment of an advertising campaign.
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 it (p. 199).
Regardless, the only way to improve one’s marketing effectiveness over time is to first know what is working and what isn’t. Advertising experiments, therefore, should not be viewed as a one-off exercise that either yields “the answer” or doesn’t, but rather as part of an ongoing learning process that is built into all advertising (p. 202).
當沒有條件進行實驗時，則可以運用地方知識找出可行解決方案︰
The solution, Scott argued, is that plans should be designed to exploit “a wide array of practical skills and acquired intelligence in responding to a constantly changing natural and human environment.” This kind of knowledge, moreover, is hard to reduce to generally applicable principles precisely because “the environments in which it is exercised are so complex and non-repeatable that formal procedures of rational decision making are impossible to apply.” In other words, the knowledge on which a plan should be based is necessarily local to the concrete situation in which it is to be applied (p. 205).
運用地方知識的方法包括市場機制、公開比賽、參考既有的成功例子，以及bootstrapping，即系統部份發生時整個系統暫停運作直至問題解決︰
The basic idea is that production systems should be engineered along “just in time” principles, which assure that if one part of the system fails, the whole system must stop until the problem is fixed. At first, this sounds like a bad idea..., but its advantage is that it forces organizations to address problems quickly and aggressively. It also forces them to trace problems to their “root causes”—a process that frequently requires looking beyond the immediate cause of the failure to discover how flaws in one part of the system can result in failures somewhere else. And finally, it forces them to look either for existing solutions or else adapt solutions from related activities—a process known as benchmarking” (p. 209).
這些方法都表示計劃者不應只依自己的直覺與經驗推行計劃︰
There are, in fact, as many ways to measure and react to different problems as there are problems to solve, and no one-size-fits-all approach exists. What they all have in common, however, is that they require planners... to abandon the conceit that they can develop plans on the basis of intuition and experience alone. Plans fail, in other words, not because planners ignore common sense, but rather because they rely on their own common sense to reason about the behavior of people who are different from them (p. 212).
我們在評價事物時往往會受結果影響，儘管結果有可能是由偶然引起︰
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 (p. 219).
這種現象其中一種常見方式就是Halo effect，即以某人擁有一項優點來判斷他擁有其他優點︰
In social psychology, the Halo Effect refers to our tendency to extend our evaluation about one particular feature of another person—say that they’re tall or good-looking—to judgments about other features, like their intelligence or character, that aren’t necessarily related to the first feature at all. Just because someone is good-looking doesn’t mean they’re smart, for example, yet subjects in laboratory experiments consistently evaluate good-looking people as smarter than unattractive people, even when they have no reason to believe anything about either person’s intelligence (pp. 219-220).
例如成就與個人才能往往會混為一談，但成功與否卻與偶然有關︰
By improving the way we make plans and implement them, all these methods are designed to increase the likelihood of success. But they can’t, and should not, guarantee success. In any one instance, therefore, we need to bear in mind that a good plan can fail while a bad plan can succeed—just by random chance—and therefore try to judge the plan on its own merits as well as on the known outcome (p. 222).
Delaying bonuses and indexing performance to peers are worthy ideas, but they may still not solve the deeper problem of differentiating luck from talent... 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. Naturally, it won’t seem like luck, but there is no way to know that whatever story is concocted to explain that success isn’t simply another manifestation of the Halo Effect (pp. 225-226).
The Matthew Effect，即已有的將得到更多，沒有的則完全沒有，也反映成功與個人才能並不相等︰
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 “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”), but Merton argued that the same rule applied to success more generally. 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 (p. 228).
其中一例就是畢業生年份與收入的關係︰
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. On its own, that doesn’t sound too surprising, but the kicker is that this difference applies not just to the years of the recession itself, but continues to accumulate over decades. Because the timing of one’s graduation obviously has nothing to do with one’s innate talent, the persistence of these effects is strong evidence that the Matthew Effect is present everywhere (p. 229).
作者因此認為，個人才能是個人才能，成功是成功，兩者並不相同︰
What we conclude may or may not correlate with his track record, and it is almost certainly a more difficult evaluation to perform. But whenever we find ourselves describing someone’s ability in terms of societal measures of success—prizes, wealth, fancy titles—rather than in terms of what they are capable of doing, we ought to worry that we are deceiving ourselves. Put another way, the cynic’s question, if you’re so smart, why aren’t you rich? is misguided not only for the obvious reason that at least some smart people care about rewards other than material wealth, but also because talent is talent, and success is success, and the latter does not always reflect the former (p. 231.
由此推論，財富再分配並不是干擾自然狀態，因為任何一種財富分佈都並不自然︰
For much the same reasons, arguments about the so-called redistribution of wealth are mistaken in assuming that the existing distribution is somehow the natural state of things, from which any deviation is unnatural, and hence morally undesirable. In reality, every distribution of wealth reflects a particular set of choices that a society has made: to value some skills over others; to tax or prohibit some activities while subsidizing or encouraging other activities; and to enforce some rules while allowing other rules to sit on the books, or to be violated in spirit. All these choices can have considerable ramifications for who gets rich and who doesn’t—as recent revelations about explicit and implicit government subsidies to student lenders and multinational oil companies exemplify. But there is nothing “natural” about any of these choices, which are every bit as much the product of historical accident, political expediency, and corporate lobbying as they are of economic rationality or social desirability (p. 238).
作者指社會科學家也有時會以常識判斷，畢竟他們也是社會生活成員︰
If much of what sociology has to offer seems like common sense, in other words, it is not just because everything about human behavior seems obvious once you know the answer. Part of the problem is also that social scientists, like everyone else, participate in social life and so feel as if they can understand why people do what they do simply by thinking about it. It is not surprising, therefore, that many social scientific explanations suffer from the same weaknesses—ex post facto assertions of rationality, representative individuals, special people, and correlation substituting for causation—that pervade our commonsense explanations as well (pp. 252-253).
互聯網與社交網絡興起令社會科學開始可以研究大型團體的實時資訊，例如一項 Facebook的研究就發現人們與熟人政治取向並沒有想像中那麼一致︰
What we found was that friends are indeed more similar than strangers, and that close friends and friends who say they talk about politics are more similar than casual acquaintances—just as the homophily principle would predict. But friends, whether close or not, also consistently believe themselves to be more similar than they actually are. In particular, our respondents were very bad at guessing when one of their friends—even a close friend with whom they discussed politics—disagreed with them (p. 259).
最後作者談論社會科學雖無法也不應尋找社會行為的統一法則，卻可藉發現社會行為不同機制，幫助我們解決問題︰
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. The result is that we will probably never have a science of sociology that will resemble physics. But that’s OK... Surely the real nature of science is not to exhibit any particular form at all, but rather to follow scientific procedures—of theory, observation, and experiment—that incrementally and iteratively chip away at the mysteries of the world. And surely the point of these procedures is not to discover laws of any particular kind, but rather to figure things out—to solve problems. So the less we worry about looking for general laws in social science, and the more we worry about solving actual problems, the more progress we are likely to make.
When the subject is human behavior, in other words, it is actually hard to imagine anything that social scientists could possibly discover that wouldn’t sound obvious to a thoughtful person, no matter how difficult it might have been to figure it out. What isn’t obvious, however, is how all these “obvious” things fit together.
It is in resolving these sorts of puzzles that social science can hope to advance well beyond where we can get on the strength of common sense and intuition alone. Better yet, as more such puzzles get resolved, it may turn out that similar sorts of mechanisms come into play in many of them, leading us, perhaps, to the kind of “middle-range” theories that Robert Merton had in mind back in the 1960s.
Ultimately, we will probably need to pursue all these approaches simultaneously, attempting to converge on an understanding of how people behave and how the world works both from above and from below, bringing to bear every method and resource that we have at our disposal (pp. 262-265).
