Why is the Mona Lisa the most famous painting in the world? Why did Facebook succeed when other social networking sites failed? Did the surge in Iraq really lead to less violence? How much can CEO’s impact the performance of their companies? And does higher pay incentivize people to work hard?
If you think the answers to these questions are a matter of common sense, think again. As sociologist and network science pioneer Duncan Watts explains in this provocative book, the explanations that we give for the outcomes that we observe in life—explanation that seem obvious once we know the answer—are less useful than they seem.
Drawing on the latest scientific research, along with a wealth of historical and contemporary examples, Watts shows how common sense reasoning and history conspire to mislead us into believing that we understand more about the world of human behavior than we do; and in turn, why attempts to predict, manage, or manipulate social and economic systems so often go awry.
It seems obvious, for example, that people respond to incentives; yet policy makers and managers alike frequently fail to anticipate how people will respond to the incentives they create. Social trends often seem to have been driven by certain influential people; yet marketers have been unable to identify these “influencers” in advance. And although successful products or companies always seem in retrospect to have succeeded because of their unique qualities, predicting the qualities of the next hit product or hot company is notoriously difficult even for experienced professionals.
Only by understanding how and when common sense fails, Watts argues, can we improve how we plan for the future, as well as understand the present—an argument that has important implications in politics, business, and marketing, as well as in science and everyday life.
Duncan Watts is a principal researcher at Microsoft Research and a founding member of the MSR-NYC lab. From 2000-2007, he was a professor of Sociology at Columbia University, and then, prior to joining Microsoft, a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group . He has also served on the external faculty of the Santa Fe Institute and is currently a visiting fellow at Columbia University and at Nuffield College, Oxford.
His research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review. He is also the author of Six Degrees: The Science of a Connected Age (W.W. Norton, 2003) and Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton University Press, 1999).
He holds a B.Sc. in Physics from the Australian Defence Force Academy, from which he also received his officer’s commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University. He lives in New York City.
This book starts off reasonably well: the first half is devoted to giving us many examples of the failure or inadequacy of 'common sense' to explain or predict the world we live in. The most interesting underlying concept, for me, is that in this world, ALL knowledge is generated and developed for the purposes of prediction: we collect data, develop hypotheses to back up certain patterns we perceive or deduce from that data, and then use these patterns (usually in the form of mathematical formulae) to predict what the future will bring, thus allowing us to devote our resources to benefit most from our predictions. The fact is that in reality such predictions often can and do go disastrously wrong; and then when we re-examine the actual results, we fool ourselves into believing that of course, what resulted was 'common sense'. Watts points out that with such hindsight our vision is always 20/20, but that is an illusion, and if we take it seriously, a delusion as well.
It is in the second half of the book, when our sociologist author starts talking about thinking differently, that the work becomes more an apologia for sociology, but using modern technological capabilities to enable us to predict the present. It is perhaps an attribute to the cleverness of the writing that we could easily actually accept that phrase: predicting the present. What he really seems to be arguing about is that, through the use of such technologies as the Internet, Twitter, Facebook, Google, Yahoo! etc. we can cheaply and very quickly establish vastly huge 'data' bases on the preferences of the individual subscribers at any one time, and from which data applied probability theory will 'predict' how these preferences will result in specific purchases 'now'. Note there is nothing 'predictive' about this: it is merely extensive data collection at an incredibly fast rate; the normal distribution curve based on this data will then take the normal standard deviation as being the area which will provide the greatest 'dividends' to any company willing and able to provide the desired products quickly and immediately to satisfy those preferences. This will thus no doubt give the illusion that the result has been 'predicted', but in fact it hasn't been. Since this is exactly the same process as the very techniques used in the first part of the book, which the author argues is basically useless, one can hardly trust, in my opinion, in any long-term benefits of this new strategy. It is the speed of the technology that creates the illusion that one is identifying the most profitable products, and as long as we can quickly respond to these ephemera quickly enough, all will be well in market-town. This, too, is 'common sense'.
Our author wants this 'speed-up' ability to be perpetually and continually used by sociologists as a kind of groundwork for the development of a 'science' for the future, and that all corporations worth their salt should make extensive use of this as the necessary basis to establish sociology as a justifiable and real science. He is wrong. It seems to me that instead he is putting his trust in instant immediate response on all levels which, in reality, will be forever 'instantly' changing practically day by day, and which will become increasingly more ephemeral and illusory as a result. What he is proposing is scientism, not science; and serious social dangers lie therein.
What I think the book was saying in the beginning is that all predictions as to how or why people act on the information available to them are inherently fallible; the conclusion would be that we should stop doing it, since it is all pretty much a waste of time and money. The same conclusion should be applied to the author's proposed 'solution' — so for me this book ends up by cutting the ground from under its own feet.
Reasons why I liked this book (on account of my confirmation bias):
1. Watts thinks Malcolm Gladwell is an idiot 2. His criticism of Nassim Taleb's "Black Swan" events 3. Great summaries of various behavioral economics/policy/psychology/sociological experiments 4. Further proof that Nozick was wrong and Rawls/Sandel are right (obviously)
This is frequently described as a book on common sense, which it is, but more importantly it's an investigation on human cognitive limits more generally and also a call to radically restructure the discipline of sociology in light of modern advances in technology. Sociology often gets made fun of in the hierarchies of academic disciplines, but Watts argues that there are reasons why sociology seems so vague and unscientific: not only are sociological problems very complicated in ways that physics problems like orbital mechanics are not, but in addition to the fact that only now do we have the ability to run experiments to truly test our long-held prejudices about ourselves and how society works, our problem-solving skills are themselves subject to those same prejudices. It's a tall order, and though inevitably the chapters pointing out problems are stronger than the chapters suggesting ways to do better, I think this is an excellent synthesis of a lot of good information and a solid guide to outlining future research directions.
That human beings have cognitive biases is well-known, especially to readers of any Dan Ariely or Daniel Kahneman book, but the thing about them is that even if you know what they are and how they work, you're almost guaranteed to fall prey to them constantly anyway. "Common sense" is a powerful tool for navigating the complexities of life, but common sense is often just shorthand for a set of fallible mental shortcuts whose workings are almost invisible to us, and whose failures are only excusable by the fact that everyone else has all the same failures too. We use many heuristics to guide us through life, and those rules of thumb are often incoherent (Watts gives examples of proverbs that contradict each other like "look before you leap" vs "he who hesitates is lost"). This extends even down to the level of deeply held and supposedly universal beliefs about justice - when you play the ultimatum game or other simple exercises in game theory with people from different cultures, people behave in strikingly different ways due to cultural norms, and those cultural norms are themselves very difficult to clearly articulate or justify. His brief discussion of the extent to which what we think of as universal institutions like the market system vary dramatically throughout time reminded me a bit of Karl Polanyi's insights about how embedded capitalism is within culture and how unnatural in a way that is.
But the main issue is that "common sense" simply isn't designed to solve the kinds of sociological problems we now find ourselves encountering. Here Watts goes on a brief tour of some sociocultural phenomena like performance-based financial incentives, and how baffling the evidence is that they do anything at all. Not only are the effects relative to your peers (i.e. a $10,000 bonus can still be disappointing if everyone else got $20,000), but they're also relative to where you were before, and the bar for what prompts additional effort can keep being raised. Even high-value bonuses in both relative and absolute terms can have little effect on performance if what's being measured is unclear or easily gamed (think teachers being paid more for student achievement or Wall Street bankers paid for paper profits), and yet even after mounds of evidence undermining the case for simple performance metrics, it is guaranteed that you will hear someone think that the "common sense" insight that paying someone more will automatically result in better quality is essentially irrefutable.
Other examples are no less interesting. Watts poses some simple questions about the Mona Lisa: what makes the Mona Lisa the most famous painting in the world? Would the Mona Lisa's qualities have been apparent at 1700 when it was painted? What are the features that we currently consider it as having that no other painting does? Are there other paintings that share similar qualities, whether by da Vinci or someone else? Why aren't they as famous? Steady investigation shows that attempts to justify the painting's #1 ranking are either specious, examples of circular reasoning - "the Mona Lisa is the best because it has the qualities of the Mona Lisa [e.g. style, composition, brushwork, the smile, etc] and not something else" - or simply arbitrary, because as he shows, the Mona Lisa was not actually acclaimed with its current status as Best Painting Ever until a dramatic theft attempt in the early 20th century. The real reason it's the Primo Painting is basically that SOME painting has to be, and it really is arbitrary to some extent which one ends up with the top spot.
This has dramatic implications for any field where ranking depends on somewhat subjective factors (i.e. almost all fields). Music immediately leaps to mind, and Watts relates experiments he's run where people are asked to rank random songs both independently and also with the ability to see what other people have ranked those songs. Unsurprisingly, herd behavior and "trendiness" arises immediately when the equivalent of a Billboard chart is introduced to the experiment, which is something I've noticed myself when using software like last.fm that provides statistics on the music I listen to. Social network technology can just as easily be used to reinforce traditional hierarchies as to eliminate them. People ending up liking things simply because other people like them, and the implication is that many universally acclaimed bands are acclaimed not so much for any intrinsic merit as simple network effects. The same logic, with the slight complication of timing, extends to other "why this and not that" cases like Facebook's success and MySpace's failure, Minitel and the Internet, VHS and Betamex, etc etc. A slight initial random push might be enough to one product the edge over another in the cumulative advantage race, and only retrospectively are people able to offer countless competing and equally arbitrary theories on what that initial push was.
One implication of this line of reasoning that seems to disturb people is that a lot of life, including huge multi-billion dollar phenomena like why Harry Potter is so popular and not so many other superficially very similar YA series, is basically random. To put it another way, outcomes in a wide range of human endeavors that seem to depend greatly on human initiative follow simple statistical distributions that can also describe things like the outcomes of coin flips. What does that say about the common sense understanding of our own "specialness" or of our intuition that the world is divided into a few very influential people and many ordinary people? What does that say about our ability to predict the future to the extent that we follow strategies that leverage "specialness", as in trying to find "the next Harry Potter" or "the next Apple", or by trying to advertise to influential people in the hopes that they will influence their followers? After all, if you knew just the right social levers to push, you could do just about anything. Since we all know that special people are out there, waiting to be found, how do we identify their specialness and find them?
The problem is that in many cases, the special people, or the levers of history, are only able to be identified after the fact. As an example, Watts picks on Malcolm Gladwell for trying to figure out why Paul Revere is so famous while a guy named William Dawes, who went on a seemingly very similar ride at the same time, is virtually unknown today. Gladwell says that Revere was a "connector", a man unusually well-suited to his task of warning all the people on his route as opposed to the undistinguished Dawes; Watts says that nothing about Revere's current fame was destined at all, and if their routes had been swapped there's absolutely no reason to think that we wouldn't have identical "one if by land, two if by sea" poems about Dawes instead. Revere was simply in the right place at the right time, and it was only after the fact that people decided there must have been something unusual about him and his place in such a dramatic event in US history. The same story holds true with music or books: every book publisher in the world would love to be able to find "the next JK Rowling", but all save the lucky one couldn't even find the original JK Rowling, who was rejected many times. To many ostensibly well-trained people, there just wasn't anything about her work that seemed to stand out among the countless manuscripts of fantasy young adult novels they read every year (related quick prediction: Rowling's new non-fiction novel is probably pretty decent, but has a 0% chance of ever being placed in the canon alongside her Harry Potter work).
This is at root due to the fact that our brain is hard-wired to look for patterns and narratives even in realms where those metaphors are fundamentally inapplicable. History is another great example. Take the idea of the storming of the Bastille being a central event in the French Revolution, worthy of becoming the central national holiday of France. Could someone have known at the time that that particular event among all the chaos of the Revolution would have been so influential? Obviously not, but this means that all history is essentially a competition in storytelling, and that prediction in the Laplacian sense of perfect foreknowledge is impossible even if Newtonian physics were true. This notion has obvious relevance for important institutions like futures markets or business more generally. Rather than repeat myself, I'll just say that in the business sections Watts reiterates that people fall prey to all the same issues of mis-narrating history, learning the wrong lessons from the past, engaging in circular reasoning about why certain things are successful, believing that people are more special than they are, and assuming due to the halo effect that what is doing well now must have all sorts of other great attributes. (As a related note, David Romer had an excellent paper in 2006 called "Do Firms Maximize? Evidence from Professional Football" discussing how coaches systematically fail to maximize their expected points by failing to go for it on 4th down, simply because there are prevailing irrational norms about what constitutes acceptable risk; this predates the infamous Patriots 4th-and-2 against the Colts in 2008 but is still worth reading).
I'm not sure that all types of prediction are necessarily equal; off the top of my head, I would say that a book like The Limits to Growth, with its carefully-sourced numbers and logical formulae, should be looked at as a more credible forecast than a hedge fund prospectus. However, even if most attempts at predicting the future fail, and it seems best to just stick to simple models like always betting on the home team (which at a 58% success rate is within 3% of the best and most sophisticated algorithms you can concoct), there are important things you can do to reduce the failures, and hence resolve some of the issues raised in the rest of the book. It's not like you can act like the future is completely random; we have a drive to speculate for a reason. Here is where Watts is, predictably as it were, a little less helpful. Some useful sanity-check tools are aggregation of knowledge as opposed to relying on too few sources of information, encouraging experimentation rather than blindly staying the course, relying on local knowledge where possible rather on too much top-down direction (here Watts has a more level-headed take on this Hayekian principle than Tim Harford did in Adapt), and always trying to rely on measurement when rather than on intuition and "common sense" even if this is ultimately a somewhat Sisyphean goal.
With that, Watts transitions to the Big Picture. Knowing that common sense can mislead us is all when and good, but what new principles can we use to guide us in the future? Societies are big and complicated things, and merely saying that we can't trust common sense isn't good enough, especially when it comes to subjects like justice and fairness. So Watts moves in the direction of Justice as Fairness, explicitly advocating a Rawlsian view of designing institutions to maximize equity, contra Nozick. I agree with him, and I agree that the concepts explored in the book support the idea that social institutions should be designed with the least-well-off in mind, as well as that the "lemon socialism" behavior of the wealthy lately of acting like you are a special person on the upside and a helpless victim on the downside is both offensive and unjust. Acknowledging that we are members of a society and not a million Masters of the Universe is an old insight, but well-placed here, because now that we are beginning to have the technology to measure and analyze trends and social movements in great detail and in real time, we are also beginning to be able to subject foundational questions of justice to statistical analysis. Alexander Volokh had a great law review article in 1997 titled "n Guilty Men" which analyzed different societies' takes on the concept of "it is better to let X guilty people escape justice than to let one innocent person be punished" - Watts would argue that we are getting to the point where we could try to actually calculate that normative value.
Obviously that grandiose dream has many precedents - Watts mentions Auguste Comte, as one of many - and even more detractors. The idea that you could calculate something like justice seems absurd. Yet it certainly seems like more and more touchy subjects of the past are being re-examined with something approaching a scientific spirit. Dan Ariely and Michael Norton published a study in 2005 titled "Building a Better America-One Wealth Quintile at a Time" that polled people about income inequality that asked people what they thought a fair distribution should be. The results showed that most people, even rich people, supported a much more equal society than the one we have now. What role should that result play in discussions over redesigning the tax code? Is the only appropriate avenue for discussion about income during salary negotiations between each individual un-unionized employee and a hiring manager, or can/should we design broader institutions to better-implement more scientific notions of justice? These and many other questions that before belonged in the backs of philosophy and sociology books are finally able to be looked at with data, analysis, and experimentation - this book is a great overview of some good questions and hints of their answers. I'll be thinking about it for a while.
The book started out with a lot of stories and fascinating new ideas. While we are wired to try to predict outcomes, we really can't do as well as we think. If you're skeptical, you'll become a believer pretty quickly while reading.
What we think is "obvious" is really only that way after the fact. He illustrates this fact by pretending to give some outcome to a situation where the reader can easily assign reasons why the outcome happened. Then he said the opposite outcome was really true, and again, the reader can find reasons why the opposite outcome could be true. This happens more often than you think.
He says we need to apply this to business and daily life. Really the only thing we have been able to predict has been things like science and mathematics that we can replicate over and over. Social, business, predicting how thing will turn out just can never be replicated over and over with the same results. Success is often good planning, but heavily reliant on luck and circumstances.
So how can businesses succeed over time? One way is to plan for many possible outcomes --- but even this is certainly fallible -- just better planning. In reality, probably the best way is to constantly reevaluate based on current information and data, and tweak your plan frequently with the newest data.
The author gets his point across better when he tells stories. He started out with a bang in this book. I think if one read the first part of the book until bored, and then skimmed Part 2 looking for ways businesses and people can be more successful would be fine. At some point, the stories trail off and theory becomes dominant which made it more difficult to stay engaged. The book could have been edited down, kept only the main points, and made sure to break it up with more stores, and it would have been better.
This book explores the three main types of common sense errors: systemically flawed mental models of individual behavior, even more flawed models of collective behaviors, and misrepresentations of past events which result in us learning less from history than we think we do. The book does a powerful job in exposing the reality that common sense convinces us that we know more than we really do. (Warning: this truth may be more than you really want to know.)
Although common sense does help us *explain* the world, it is not so great at helping us *understand* it. The author highlights this dilemma by noting that: "Bad things happen not because we forget to use our common sense, but rather because the incredible effectiveness of common sense in solving the problems of everyday life causes us to put more faith in it than it can bear." (p. 23). For example, we often try to explain successes by describing intrinsic attributes, rather than actually identifying the complex set of factors that actually led to the success. This circular reasoning comes in the formula of "X succeeded because X had the attributes of X." Illustrative examples of common sense explanations that use this type of circular reasoning include: ---"The Mona Lisa is the most famous paining in the world because it has all the attributes of the Mona Lisa" ---"Harry Potter was successful because it had exactly the attributes of Harry Potter, and not something else." ---"People have stopped buying the gas-guzzling SUVs because social norms now dictate that people shouldn't buy gas-guzzling SUVs." ---In general: "X (fill in the blank with whatever success you are trying to explain) happened because that's what people wanted; and we know X is what they wanted because X is what happened."
Another common-sense-centric problem explored in this book is the the micro-macro problem (also known as emergence) where we attempt to go from the micro choices of individuals to explain the macro phenomena of the social world. In our attempts to do so, we use use "social actors" ("what families choose," "what the market wants,") as a way to aggregate individual behaviors, but doing so fail to account for the complex entanglement of situational, contextual, and interactional influences that ultimately determine group behaviors and outcomes. The reality is that knowing the individual does not allow us to predict the group's collective behavior. As the author nicely summarizes: "Just as you can know everything about the behavior of individual neurons and still be mystified by the emergence of the consciousness in the human brain, so too could you know everything about individuals in a given population--their likes, their dislikes, experiences, attitudes, beliefs, hopes, and dreams--and still not be able to predict much about their collective behavior." (p. 79)
And, when we can't explain an outcome in terms of the special attributes, another common sense fallback is to subscribe to the "law of the few" and conclude the outcomes resulted from a contagion social process started by a small number of influential or "special" people. A classic example of this reasoning (which Gladwell fans will immediately recognize) is: "A few special people revived the fortunes of the Hush Puppies shoe bran because a few people started buying Hush Puppies before everyone else did." But, it turns out that this explanation is just another example of circular reasoning and that "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, 107). This is another trap where common sense explanations--constructed after we know the outcome itself--simply describe, and not explain.
Other common sense traps we fall into when trying to explain "the obvious" include: creeping determinism (our tendency to perceive what actually happened as having been inevitable), hindsight bias (our after-the-fact tendency to believe that "we knew it all along"), sampling bias (focusing on what did happen and paying too little attention on most of what did not happen), the post-hoc fallacy (inferring cause-effect relationships when in fact factors were just a sequence in unrelated events), and confusing stories with theories as a way of using common sense to try to explain the world.
Commonsense is extraordinarily good at navigating particular (isolated) circumstances in our every day business and allows us to "skip from day to day and observation to observation, perpetually replacing the chaos of reality with the soothing fiction of our explanations. And, for everyday purposes, that's good enough, because the mistakes that we inevitably make don't generally have any important consequences. 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." (p. 157) Common sense approaches fail here as these cases affect large numbers of people over extended periods of time and require solutions that need to work consistently, reliably associate cause and effect, differentiate scientific explanation from mere story telling, and differentiate predictions that can be made reliably from those that can not.
As the author suggests, it is in these social world situations where "uncommonsense" is called for. He proposes a shift from a "predict and control" model for anticipating the future to a "measure and react" strategy of dealing with the present as it actually unfolds, explaining: "We cannot suppress our commonsense intuitions any more that we can will our heart to stop beating. What we can do, however, is remember that whenever it comes to questions of business strategy or government policy, or even marketing campaigns and website design, we must rely less on our common sense and more on what we can measure." (p. 212)
And, to help us accurately measure what's real in such social world situations, we need to be mindful of the commonsense traps that often get in the way, including The Halo Effect (our tendency to generalize our evaluations about one particular feature of another person to judgments about other their other, often unrelated, features), believing that success is always a reflection of talent (when in fact is often a result of other contextual factors and luck), and the myth of the corporate savior (our tendency to emphasize the influence of special individuals in directing the course of incredibly complex organizations and events).
The reality is that social science is complex, and what seems obvious really isn't so obvious at all: "When you think about the sheer complexity of human behavior, this approach to doing social science seems kind of implausible...Individual behavior is complicated by dozen of psychological biases, many of which operate outside of our conscious awareness and interact in as-yet-unknown ways. And...when individuals interact with one another, their collective behavior may simply not be derivable from their individual attributes and incentives, no matter how much you know of them....The social world, in other words, in far messier than the physical world, and the more we learn about it, the messier it is likely to seem." (pp. 252- 262)
The author's solution to understanding and navigating the messy world of social science is the realization that: "We will probably never have a science of sociology that will resemble physics...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." (p, 262) An obvious conclusion.* *Once you already know the answer.
Deep. A bit philosophical. Takes on 'common sense' explanations of social phenomenon like influencers and tipping points. Also describes some of his own very cool research (though you gotta go elsewhere for more details of it).
A couple of my favorite nuggets:
When a forest fire breaks out, we never wonder what made that spark so unique. We only wonder how much dry tinder was lying around the forest and how long the drought had been. But when a video goes viral or a brand takes off, we ONLY wonder what made that thing so unique. When in truth, ANY video or brand could've taken off because the proverbial forest was ready to burn.
Social scientists have physics envy. And physicists often disparage social scientists for their lack of rigor, theory, and laws. Well, in truth, social science is NOT rocket science.... it's harder. In rocket science you get to replicate experiments in very controlled environments where you can isolate every variable. And when you build a rocket you can ensure the fuel is pure, the metal is precisely engineered, the o-rings are exactingly elastic. When you build a school you have almost NO control. So how are you supposed to engineer education?
Whenever I hear (or read) an otherwise intelligent person deriding a social psych experiment-- e.g., "I can't believe someone had to *research* whether the media causes poor body image in teenage girls! Everybody *knows* it does!"--I weep for humanity. The tools of social science are imprecise, and what "everybody knows" is often wrong, or not proven by studies, or rendered inconclusive by the data. That's why we do studies, that's why we keep the research and the conversation going when studies contradict each other, and why we need to keep recalibrating our tools*.
It is a great relief to read anything by sociologist Duncan Watts, the world's eminent expert on social networks, who makes a good case for not accepting "common sense" wisdom to explain human behavior. People, especially in groups, are far more unpredictable than authors like Malcolm Gladwell, with his tipping points and blinks, would have you believe.
Watts is a swell writer--presenting vivid examples in a voice that's one part scholarly, one part folksy (but not condescending).
Отличная книга, которую рекомендую всем поклонникам критического и системного мышления! Да и просто мудрым людям, желающим разобраться "как это работает" (в смысле, наше сознание, принимающее решения, делающее выводы, выбирающее "лучшее" и т.п.).
По стилю подачи информации и по духу текст ближе всего к книгам Н.Талеба. Автор пишет с юмором, разоблачая расхожие стереотипы обыденного сознания; он постоянно заставляет читателя "включать голову"; удивляет широтой своей эрудиции и глубиной обоснования изложенного (20% от объёма книги - любимые мною "пруфлинки" :)), т.е. подробные комментарии и ссылки на первоисточники).
Автор - социолог. Ключевая мотивация книги - вернуть былое величие :) "социальных наук" (социологии, психологии, экономики и др.). Ну, или хотя бы оправдать их существование :))). Собственно, настоящему и желаемому будущему социальных наук посвящены первая и последняя глава в книге. Если кратко, то автор видит будущее социальных наук за "теориями среднего уровня" (по Р.Мёртону), которые не сильно уклоняются в дебри теоретизирования, но предлагают удобные для практического применения концепты отдельных сфер жизни общества, групп, индивидуумов и т.п.
В книге много интересного, но я приведу лишь один пример, чтобы было понятно, о чем в ней речь. Например, в книге неоднократно затрагиваются столь желанные для многих категории "успеха", "популярности" и т.п.
Почему, к примеру, отдельные книги (картины, кинофильмы, музыкальные произведения и т.п.) становятся "мировыми шедеврами", а другие - нет? Ответ "потому что они обладают определенными качествами" не принимается, т.к. по сути он является тавтологией и на самом деле ничего не объясняет!
Т.к. не отвечает на вопрос "А почему именно эти качества (а не какие-либо другие) сделали данное произведение шедевром?". Кроме того, практически всегда можно найти множество аналогичных произведений (обладающих такими же качествами), которые шедеврами почему-то не стали. Получается, что дело не в самом произведении искусства, а в чём-то ещё, в системных причинах более высокого уровня...
В книге есть отличный пример по поводу "Джоконды" Леонардо. Эта картина до конца 19 - начала 20 века вовсе не считалась "мировым шедевром"! Что же привело к тому, что сегодня это - несомненный и всеми признанный шедевр?
Кстати, аналогичные вопросы можно задать и по поводу т.н. "лидеров", особо "успешных" людей (или бизнес-компаний): Они стали лидерами/успешными благодаря своим особым внутренним качествам? Или благодаря другим внешним причинам? Здравый смысл (и популярные бизнес-гуру вроде М.Гладуэлла, которого автор отлично критикует) говорит нам, что успешные люди (и бизнесы) я��ляются "особенными", но на самом деле это не так. Всегда существуют процессы более высокого уровня (в макросистеме), которые вполне случайным образом делают кого-то "лидером", а кого-то "не-лидером".
Что ещё есть в книге:
- о "стратегическом парадоксе" (самые известные бизнес-провалы случаются не с теми компаниями, у которых вовсе нет бизнес-стратегии, а с теми, стратегия которых логична, и отлично разработана, но... не верна, не соответствует обстоятельствам)
- об ограниченности любого прогнозирования; о том, что любая "история событий" - это не более чем нарратив, задним числом объясняющий то, что уже произошло с учётом некой целостной картины событий. Но находясь "на старте" события мы просто не в состоянии его прогнозировать (т.е. выстроить соответствующий нарратив без учёта ВСЕЙ системы событий).
- о том, что "лидеры мнений" - миф! "Особенные люди", которые "задают тренды" - это фантазия маркетологов, которая ничего не объясняет (как и в случае с "Джокондой"). Мало того, по-прежнему "короля играет свита"; т.е. без соответствующей поддержки со стороны других людей "лидер мнений" ни на что не способен.
- о "мудрости толпы" vs прогнозы экспертов
- о том, что "локальное знание" и "тестирование рынка" гораздо эффективнее, чем анализ и прогнозирование трендов
Normalmente livros técnicos começam bem, aí pra preencher as 200-300 páginas seguintes o autor acaba caindo na armadilha de repetir conceitos. Esse livro não se repete, mas ele começa mal, trazendo exemplos manjados e explicações rasas sobre alguns dos fenômenos abordados.
Na parte II do livro, que ele chama de Senso Incomum (a primeira parte chama senso comum, o que faz total sentido), o autor coloca o chapéu de sociólogo (ele é físico de formação) e aborda teorias interessantes que ajudam a colocar luz nas críticas que cientistas sociais recebem por trazerem soluções longe dos parâmetros das avaliações precisas das demais ciências.
No fim, o livro mostra que a combinação de coisas óbvias é que são difíceis de prever. E isso justifica todo o empenho dos cientistas sociais, mesmo com toda a imprecisão inerente dos resultados que isso possa trazer...
“We can skip from day to day and observation to observation, perpetually replacing the chaos of reality with the soothing fiction of our explanations.” (p. 157)
Watts provides a thorough account of how policymakers, scientists, and everyday individuals do exactly that: comfort themselves with their fictional, commonsense pet theories. In reality, the social world is complex and unpredictable; we often cannot say with any confidence why things occurred as they did. The more we can accept the limits of our knowledge and replace common sense answers with well-researched alternatives, the better we can work toward a fair and prosperous society. There are many pitfalls to social science practices, but Watts provides compelling examples of the techniques that work.
Quotes: “We have the impression that our particular beliefs are all derived from some overarching philosophy, but the reality is that we arrive at them quite independently, and often haphazardly.” (p. 16) “Jane Jacobs… ‘There is a wistful myth that if only we had enough money to spend…we could wipe out all our slums in ten years…But look what we’ve built with the first several billions: Low-income projects that have become worse centers of delinquency, vandalism, and general social hopelessness than the slums they were supposed to replace.” (p. 21) “Common sense often works just like mythology.” (p. 28) From an experiment, “No matter what they were actually paid—and remember that some of them were getting paid ten times as much as others—everyone thought they had been underpaid.” (p. 50) “Policy makers can always persuade themselves that all they need to do is to design the correct incentive scheme—overlooking, of course, that tthis was precisely what they thought they were doing previously as well.” (p. 52) “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 triggered it.” (p. 96) “Commonsense explanations therefore seem to tell us why something happened when in fact, all they’re doing is describing what happened.” (p. 122) “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.” (p. 131) “We just can’t know what it is that we should be worrying about until after its importance has been revealed to us.” (p. 186) “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.” (p. 262)
Ack. I'm convinced that commonsense reasoning fails us. I'm convinced that policy makers should hire social scientists who actually use rigorous methodology instead of intuition and uncontrolled experiments. But I was convinced of that already.... I thought this was actually going to give us some answers. And I didn't find any that actually helped me understand anything... but I've been reading a lot of modern psychology books already....
Bonus point for the snaps on Gladwell. Subtractive points for blinkers to his own demographic and culture. Half a point down for basically giving the bulk of each chapter to the common logical fallacies that many of learned in college Freshman English and many more of us can readily research. Half a point down for not enough everyday examples that most of us can actually relate to & appreciate. Bonus point for organization, including a reference summary at the end (at least of my edition).
Example of a key logical fallacy that too many of us, including me, are too vulnerable to: "If we want to do why some people are rich, for example,... it may seem sensible to look for rich people... and identify which attributes they share. But what this exercise can't reveal is that if we instead looked at people who aren't rich... we might have found that they exhibit many of the same attributes." Watts doesn't go on to suggest an answer, really, but the point is that luck and context have a huge but often underappreciated influence.
A motto that I taught my children since they were tiny is "wishing doesn't make it so." Watts explains how many of us often (he says simply an absolute "we" but I know enough to qualify) tell stories / develop narratives / commit post hoc and other logical fallacies "because this is how we'd like the world to work, not because that is how it actually works.... Commonsense explanations therefore seem to tell us why something happened when in fact all they're doing is describing what happened.
I love the quote Watts used from Alexander Pope: "Nature and Nature's laws lay hid in night: God said, Let Newton be! and all was light."
I do give Watts credit for his first training as a physicist. As I've said in other reviews, I don't trust science books that are written by journalists rather than by actual scientists. There not always as engaging as this, but they are more valuable.
I also am sceptical about science books more than a few years old. If you do want to read this, do so soon, before what he says becomes dated.
If you are only a reader of fiction, you probably will not like this book. If however you have some interest in the psychology of human behavior, this may appeal to you. It is well suited for those of us who have some background training and or experience in clinical trials, study groups, and statistics. The text is a bit dry, but not so much so that it is difficult to read. The author does a reasonably good job in explaining how and why people decide to do what they do and form the opinions they have.
I hope you'll forgive me if I make a summation of the book with a personal reference. In the past, I raised cattle. If I wanted to move the herd, I did not try to move all the cows at the same time because they would just scatter and I would be left saying words I normally kept out of my everyday vocabulary. I found that if I selected a single animal and calmly moved it, the rest would easily follow. The message of this book is that humans are easily manipulated, like cattle. For verification of this theory, just tune in to CNN, Fox News, or MSNBC.....mgc
Ótima introdução às ciências sociais para mim, que não tinha contato. Muito legal como ele separa o que parece óbvio do que é comprovado de fato, e como dá perspectivas do que realmente podemos saber sobre o futuro e sobre o comportamento humano. Vai bem além do que se propôs.
Consider the last national election, your employer's last annual report, or your favorite sports team's last away-game victory. What made the particular outcome happen? Looking backward, conclusions seem foregone; we construct retrospective explanations that justify how what happened had to happen, because, well, it did. But Duncan J. Wells explains that what seems inevitable once it's already happened, is actually deeply contingent and controversial. Exactly why is both bizarre and revealing.
Trained as an engineer but functioning as a sociologist, Wells has conducted intensive research for America's largest corporations, including Yahoo and Microsoft. In that capacity, backed with massive corporate capital and utilizing technocratic research techniques that didn't exist fifteen years ago, he's investigated questions about how humans make decisions. Not only has this included individual decisions, but how uncountable group decisions form a consensus. That is, he's investigate how individuals make a society.
Watts' answers prove many and various, and deserve careful reading. Their common thread, however, devolves to common sense. A system useful for negotiating everyday interactions, common sense proves more fraught when confronted with the hidden inner dynamics of large groups. Human interactions prove founded on myriad rules, mostly unspoken--as anybody who has ever traveled abroad and unknowingly transgressed serious taboos already knows. These rules are not only unquestioned, but largely unacknowledged.
In this, Watts relies heavily on research avenues first utilized by Stanley Milgram. Though mostly famous for his "Obedience to Authority" experiments, Milgram also pioneered research, like the famous Six Degrees experiment, demonstrating how intensively connected society is. We cannot explain who influences us, and by whom we're influenced, because we cannot comprehend our cultural links. Watts actually replicates some Milgram experiments digitally, proving reality is more linked than Milgram could've realized.
Society proves difficult to explain. In one experiment, Watts, using double-blind research methods and sophisticated online social networks, manages to recreate the digital music marketplace. By segmenting populations into mutually unaware groups, he manages to simulate several different marketplaces, resulting in completely different bestseller lists. This proves that just because certain circumstances occurred doesn't mean they had to occur; reality is deeply provisional. We cannot prove or understand why what happened, happened.
This goes double for situations which, unlike music markets, cannot be segmented and rerun analytically. We cannot, for example, have multiple trial Presidential elections or overseas wars. Explanations for outcomes therefore lack scientific rigor. When Nate Silver gives probabilities for certain electoral outcomes, his numerical assignments mean something very different from Vegas betting pools. The differences are opaque to people who can't access Silver's original math. Therefore we construct explanations retrospectively.
This comes across in popular self-help books which examine successful people to unlock their secrets. Authors believe we'll replicate somebody else's miracle if we simply find whichever magic choice or simple connection made their success possible. However, Watts asserts, we cannot see every influence that steered so-and-so to seemingly inevitable success. Essentially we assume somebody had to succeed because they did succeed; Watts calls this creeping determinism.
(Watts specifically name-checks Malcolm Gladwell for this tendency, though in fairness, Gladwell did write "Outliers," which examines successful individuals' cultural contexts, to counter this very tendency.)
Essentially, according to Watts, we don't explain the past, we describe it. Therefore, attempts to construct actually useful predictions prove frustrating. And because most professional soothsayers' predictions go largely unexamined, we must step over corpses of numberless stupid secular prophecies to reach contemporary reality. Certainly, many people my age lament their missing flying car. But most high-profile attempts to apply past observations to future choices remain equally fruitless, and we often don't realize it's happened.
Can we then even make meaningful predictions? Watts says yes, though exactly how defies brief restatement. We must eschew many common prejudices, like expecting meaningful predictions to be particularly precise. We must also limit our horizons: decades-long predictions prove as useless as long-term weather forecasts. And our reliance on either credentialed experts or gifted rookies limits our options. Processes for making actually useful predictions are surprisingly simple, yet because of learned biases, applying them is shockingly difficult.
Watts' explanation of human reasoning, and its limits, sheds powerful light on how important decisions fail. Watts explicitly describes several implications for business, government, entertainment, and other fields, while constructive readers can imagine other fields which suffer exactly the field blindness Watts describes. If you've ever wondered how politicians, CEOs, and media pundits can be so spectacularly wrong, this book's explanations will chill your blood. As science for the masses, Watts is a master.
For me, this book is sweet, delicious Nihilism. No, that's the wrong word. It's like Nihilism for knowledge, which I don't think is a word.
Let me back up - I negative learned from this book. I unlearned many explanations for the world I thought I knew, and changed how I perceive and understand my interactions with people around me. Watts very strongly argues that it's very challenging to understand people or predict the future, so humans just kind of guess and mostly guess wrong.
Science then tries to come in but basically just confirms what people want to hear, or doesn't and people just make up some story about how that "makes sense" and move on. The examples he uses are as painfully simple as they are brilliant and effective. I will never attribute any knowledge to "common sense" (a thing I no longer believe exists) ever again. Well done.
Where the book fails is where to go from here? Now that the assumptions have been challenged so convincingly, what CAN we even know? He doesn't really make any convincing arguments in the second half of the book.
Still, I find the book worthy of great praise. Indeed, potentially unlearning is even harder than learning, but now there is a vacuum of skepticism and doubt that Watts failed to color. He peeled back how I thought, but the tools I have now seem inadequate; which is the only thing keeping this book from 5 stars.
I suppose my new outlook and the questions this book has left me asking are more than enough. I'm sure life, experiences, and books will slowly recolor my understanding of the world that Watts so deftly derailed.
Very slowly. Until then, I'm asking more questions than ever; which, I hear, is a side effect of knowledge.
Duncan Watts argues that our common sense is not as good as we think it should be. When we trust our common sense we often make bad predictions.
We are duped into believing the Mona Lisa is such an extraordinary painting or Shakespeare such amazing writing. The Mona Lisa is small and average work for Da Vinci. We study these works as masterpieces and eventually it becomes self fullfilling.
Our common sense is a poor predictor as it should have been obvious that Facebook and Yahoo and Google would be screaming success..but we didnt't but into these companies when they were in their infancies.
People predicted that No Child Left Behind would incentivise teachers to teach better, principals to fire bad teachers, and then we would see test scores improve among students. Instead we saw teachers cheat (giving the kids the answers and altering their answer sheets) to improve scores artificially and gain for themselves a nice bonus.
Watts writes in the same genre as Malcolm Gladwell (stories that try to pull together and support a sociological theory). I wasn't as engaged and did not feel that stories of Jobs, The Surge in Iraq, or the Mona Lisa really did prove that common sense is poor.
This book has a brilliant first half where it shows that common sense is a questionable appeal, a dubious guide to action, and a disastrous foundation to policy, while the second half has some key advice but fails to take the truly courageous step, unlike Kahneman, of telling us how to practically distrust ourselves. What this volume serves up instead, the measure of continually analyzing the communication patterns of the internet will literally serve as the telescope that will lead to the remaking of social science, and that we should use it to react continuously to the present, is both sound strategic advise and the path to personal pathology. Maybe this particular narrative is cleverer than I'm giving credit for, providing the ambitious with a recipe for behaving as though they had developed a sense of self-skepticism.
هذه قراءتي الثانية لهذا الكتاب، ضمن خطة أمضي بها مستعينًا بالله لإعادة قراءة أهمّ ما تحويه مكتبتي، خاصة في مجال اهتمامي. ولقد زادتني القراءة هذه إيمانًا وتقديرًا لأهمية ودور القراءة الثانية في فهم الكتاب وأفكاره، وتقييمه أيضًا. هذا كتابٌ قيّم. فكريٌ وفلسفي، يناقش كثيرًا من أفكار علم الإجتماع وعلم النفس الإجتماعي التي ظهرت مؤخرًا. أسئلة الكاتب ومحاججته تجعل هذه الأفكار على المحكّ، وتستدعي من متتبعيها ودارسيها –أمثالي- الكثير من الإنتباه، والحذر في القبول أو التعميم. وأعتبر الفصل الذي يتناول "التاريخ" من أهمّ فصول الكتاب. في الجزء الثاني من الكتاب يحاول الكاتب اقتراح طرق عملية لتزيد من قدرة الباحث على فهم ما يجري، دون الوقوع في شرك (المنطق الدوّار) أو شرك ( الإدراك العكسي). (تجدر الإشارة إلى أنّ هذه ترجمتي الشخصيةالتي أقتنع بها للمصطلحات).
The thesis of some of the chapters got a bit lost and I really had to try and piece together all the bits of information I was getting to try and form a picture of what Watts is trying to get at. The appendix has a key points section that gives a nice overview of each chapter, partially remedying some of the lost theses. But looking at the key points of each chapter also made it clear that Watts sometimes tried to stuff too much into the same chapter and ended up with sprawling topics that didn't really feel like they fit together. However! There were also some very interesting chapters with a clear line of logic. Watts comes up with some nice tangible metaphors to ground the more theoretical ideas. One thing I can say is that Watts succeeded in what he said he set out to do: make the reader reflect on the way they think. By putting a name to many of the thought processes we go through and also deconstructing the logic behind them, Watts definitely challenged the way I think about 'obvious' explanations for social phenomena. Setting everything else aside, the main reason I enjoyed the book comes down to the compelling ideas Watts presents.
This is such an excellent book. It is so underrated in the "popular social science" genre, such as Freakonomics or Nudge. The main thesis is that "common sense" is really good at coming up with stories for why the world is the way it is, but terrible at determining which stories are actually true. Watts gives example after example of this. One main point is that we need rigorous academic research in economics and other social sciences in order to understand the world, and not just the story-telling that fills up much of the popular media. For example, on any given day you can read in any major news outlet why the market went up or down the day before. But this is just hindsight rationalization, and in reality we have no idea why the market did what it did, or what it will do in the future. Another big takeaway is that we should not be surprised at how hard it is to forecast the future, since we are so far away from understanding the complexities of human society.
My one knock on the book is that it is somewhat less engaging to read that other books in this genre, and I suspect this could be why it has not caught on as much. But I found it well-written and very clear, even if it is a bit less colorful than some other books.
Anyone who works in business, economics, or politics has to read this book. Really, anyone who thinks about the future should read it. Which is, of course, everyone.
The book can feel mildly unsatisfying as you wade through the difficulties and issues with our common sense and many of our methods and systems for making decisions, and yet left without much in the way of alternatives. This isn’t a shortcoming of the author or the book as much as one of sociology and human beings in general.
For sure, the author made me think. And the excellent notes and bibliography have already led me to new trails to explore and research.
The first part of this book is an extensive discussion of the types of predictive reasoning errors the human brain makes, how that causes us to think that we know more than we actually do and how this type of reasoning leads to a tendency to discount social sciences as "real science". The second part then proposes the use of real-time technology for building predictive models. While I think the second part over-reaches a bit, in general a good, and thought-provoking analysis of how common sense creates barriers to to the progress of the social sciences.
I will say he uses the same examples as a lot of popular books which makes me kinda nuts. can nobody do their own research anymore?!
basically: if you can 'explain' with common sense something in either direction then THERE'S NO COMMON SENSE! 'Common sense' may help us tell stories but it doesn't help us predict the future.
bad: his writing style is very hit you over the head. also he denigrates representative agents then the last three chapters he uses them in exactly the way he eschews.
So many good quotes and notions:
circular reasoning "We want to believe that x succeeded because it had just the right attributes. But the only attributes we about are the attributes that x possesses. Thus we conclude that these attributes must have been responsible for x's success."
micro-macro problem "The representative agent is only and always a convenient fiction. And no matter how we try to dress them up in mathematics or other finery, explanations that invoke representative agents are making essentially the same error as common sense explanations that talk about 'firms', 'markets, and 'societies' in the same terms that we use to describe individual people." on the idea of super connectors: "It's got to be someone special, we feel compelled to conclude. How else could it work? Nor is the intuitive appeal of special people explanations restricted to problems to do with networks. The 'Great Man' view of history explains important historical events in terms of the actions of a few critical leaders."
Causation in history: "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 [thing]. 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 believe we knew it all along." only creeping determinism is worse because after the fact we believe things were inevitable.
On plausibility: " Explanations that are skillfully delivered are judged more plausible than poorly delivered ones even when the explanations themselves are identical explanations that are skillfully delivered I judged more plausible than poorly delivered one even when the explanations themselves are identical and explanations that are intuitively plausible are judged more likely than those that are counter-intuitive even though, as we know it from all those Agatha Christie novels, the most plausible explanation can be badly wrong. 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."
Past stories vs future predictions"Nevertheless because accounts of the past, once constructed, bear such a strong resemblance to the sorts of theories that we construct in science it is tempting to treat them as if they have the same power of generalization even for the most careful historians..... No matter what we say we are doing, in other words, whenever we seek to learn about the past we are invariably seeking to learn from it as well. An association that is implicit in the woods of the philosopher George Santayana "Those who cannot remember the past are condemned to repeat it. This confusion between stories and theories gets to the heart of the problem using 'common sense' as a way of understanding the world. In one breath we speak as if all we are trying to do is make sense of the world. But in the next breath we're applying the lesson that we think we have learned to whatever plan or policy we're intending to implement in the future. We make the switch between story telling and theory building so easily and instinctively that most of the time we're not even aware that we're doing it. But the switch overlooks that the two are fundamentally different exercises with different objectives and standards of evidence. It should not be surprising then that explanations that were chosen on the basis of their qualities as stories do a poor job of predicting future patterns or trends. Yet that is nonetheless what we use them for. Understanding the limits of what we can explain about the past ought therefore to shed light on what it is we can predict about the future."
Predictions "[It's] not that we are universally good or bad at [prediction]. But rather that we are bad at distinguishing predictions that we can make reliably from those that we can't."
Complex systems "Complexity arises out of many interdependent components interacting in non-linear ways. In complex systems tiny disturbances in one part of the system ca get amplified to produce large effects somewhere else."
Uncertainty "There is a difference between being uncertain about the future and the future itself being uncertain. The former is really just a lack of information.... whereas the latter implies that the information is in principle unknowable... where the best we can ever hope for is to express our predictions of various outcomes as probabilities"
Prophesies "Common sense intuition about the future [tends] to conflate predictions with prophesies...When we think about the future we imagine it to be a unique thread of events that simply hasn't been revealed to us yet. In reality no such thread exists. [the future is many] possible threads each of which [with a] probability of being drawn... But because we know that at some point in the future all these probabilities will have collapsed onto a single thread we naturally want to focus on the one thread that will actually matter."
On experts: "Because they are experts we tend to consult only one at a time. Instead what we should do is poll many opinions [experts or not] and take the average....at the end of the day both models and crowds accomplish the same objective."
Executive priorities: "Constructing scenarios, deciding what is core and what is contingent, devising strategic hedges and so on...necessarily diverts attention from the equally important business of running a company. According to Raynor, the problem with most companies is that their senior management, meaning the board of directors and the top executives, spends too much time managing and optimizing their existing strategies—what he calls operational management—and not enough thinking through strategic uncertainty. Instead he argues that they should devote all their time to managing strategic uncertainty, leaving operational management to division heads.... Once an organization has gone through the process of building scenarios, developing strategic options and identifying and acquiring the desired portfolio of strategic options, it's time to do it all over again."
"The main problem with strategic flexibility as a planning approach is precisely the same problem that it was intended to solve. Namely that in hindsight the trends that turned out to shape a given industry always appear obvious and as a result 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 list of contenders including of course the one that did in fact transpire."
"But in cases where we only get to try out a plan once, the best way to avoid the halo effect is to focus out energies on evaluating and improving what we are doing while we're doing it. Planning techniques like scenario building and strategic flexibility...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 qualities of plans before the outcome is known. Alternatively crowdsourcing, field experiments and bootstrapping... can help organizations learn what is working and what isn't and then adjust on the fly."
"In reality every distribution of wealth reflects a 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 rule while allowing other rules to sit on the books or be violated in spirit. All these choices can have considerable ramifications for who gets rich and who doesn't. But there is nothing 'natural' about any of these choices which are very bit as much the product of historical accident, political expediency, and corporate lobbying as they are economical rationality or social desirability.... it is certainly valid to argue about whether proposed changes make sense on their merits but it is not valid to oppose them simply on the grounds that altering the distribution of wealth itself is wrong on principle."
"The real reason is philosophical consistency. When times are good banks wish to be perceived as independent risk taking entities entitled to the full fruits of their hard-won labors. But during a crisis they wish to be treated as critical elements in the larger system to which their existential failure would pose a threat.... Either they are libertarians who should bear the full weight of their own failures as well as their successes or else they are Rawlsians, paying their dues to the systems that takes care of them. They should not be able to switch philosophies at their convenience."
Like many works written by academics, Everything is Obvious: Once you know the answer, starts out promising but ends up losing its way. The edition that I "read" was the audio edition, which was narrated (happily) by the author himself. Just as well he was a reasonably competent reader, though somewhat stilted.
Everything is Obvious: Once you know the answer is a challenge to the notion that common sense is good sense. It is a presentation of the author's significant researches into this topic. And the conclusion? That common sense is rarely the best way to think, in most situations. Common sense, it transpires, stops us for thinking our ways around problems because we settle for what 'seems' logical, even in face of a lack of data. In fact, Watts concludes that when the data conflicts, we will try to modify the data rather than modify our points of view.
This book is filled with studies that illustrate the many sides of this issue, and the many questions that it can raise. But it is in the delivery of these studies, that we start losing the phrase common sense, even though this was part of the promise right from the beginning. By the end of the book, it wasn't clear whether there was a clear assessment of common sense, or whether the notion of common sense was simply introduced as a means of illustrating the concept of obviousness.
Watts's book is fascinating, make no mistake. And it's relatively straightforward. But it is heavily academic, and draws so much on other studies to illustrate the point that often the point gets lost. This is a shame. It seems to happen an awful lot in books published by academics. For people who are taught to tell 'em what you're gonna tell 'em, then tell 'em, then tell 'em what you told 'em it's almost as if they want to assert their independence. Or, it could be that this overly academic style just seems kind of silly in an otherwise conversational and interesting book. Or, perhaps editors aren't as good as they used to be at balancing the narrative against the research. Or, maybe the shape of books has changed. Or, some other reason from the eleventy million that also exist. Perhaps, once academics get to a certain point in life, editors don't want to shape authors' works too closely; or perhaps the academics resist the assistance.
In any case, it's a shame because it makes books like this wander away from their original intent, when they could be helped to be strong all the way through.
This work, however, did shine a lot of light onto a lot of areas. Areas like analytics, marketing, and the establishment (and validity) of research; all areas in which worldview trumps actual data more often than people will admit. As a result of reading Watts's book, I find myself challenging my own perceptions. If I have data in front of me, from my business for example, how do I perceive it? What do I conveniently ignore because it doesn't fit with my view of my own sense? What do I glaze over? And what do I simply 'reorient'?
And more to the point, what in my life is actually the result of listening to common sense, when I could have been better served by being a bit clearer about things?
The query that Watts pursues is courageous, not so much because of its contested nature among his colleagues, but more so because it challenges one of the most widely held beliefs: That common sense is good sense.
What if the best thing to do is not what common sense tells you? For the answer, you'll need to read this book. Despite its meanderingness towards the end, it's very much worthwhile - if only to get you to rethink how you perceive your own world, and why.
Buku ini diawali dengan beberapa kasus di mana orang2 penting, seperti penulis John Gribbin, serta senator AS, menganggap riset sosiologi bukan hal yang penting, misalnya sepenting riset fisika. Riset di bidang fisika tentu amat penting. Namun agak lucu kalau menganggap riset sosial itu tak penting dengan alasan — menurut mereka — bahwa hasilnya bisa ditebak dengan logika biasa, tanpa harus melakukan riset yang luas. Di dekade kedua abad ke-21 ini, dengan analisis yang cukup banyak mengenai jejaring sosial, kita mulai tahu bahwa banyak hal yang lebih menarik di riset ilmu sosial, selain sekedar memainkan urusan game theory yang banyak berfokus di urusan insentif, motivasi, dan nilai2. Tapi banyak yang tetap menganggap, penelitian fisika memberikan hasil yang luar biasa, dan menembus batas pikiran sederhana (common sense), sementara ilmu sosial tidak menghasilkan hal2 di luar itu. Masalahnya memang, kalau ilmu sosial itu sederhana, mengapa masalah sosial tak mudah dipecahkan? Soalnya bukan pada resource yang akan terlalu besar untuk memecahkan masalah itu, tetapi benar2 pada ketidaktrampilan kita memahami manusia, sebagai individu maupun masyarakat :D.
Penulis lalu memberikan contoh2 kecil. Paul Lazarsfeld pernah membuat riset pada para prajurit di Perang Dunia II. Ia memaparkan beberapa hasil. Misalnya, penemuan nomor 2: “Pemuda dari pedesaan umumnya memiliki semangat lebih baik dalam ketentaraan daripada pemuda perkotaan.” Reaksi kita? Tentu saja. Itu masuk akal. Pemuda desa lebih terbiasa dengan lingkungan yang keras, kerja keras, gotong royong, dll. Tapi lalu Lazarfeld mengatakan, “Ups. salah baca. Maksudnya, pemuda dari perkotaan umumnya memiliki semangat lebih baik.” Reaksi kita? Haha. OK. Tetap bisa dipahami. Pemuda kota lebih biasa hidup bersesakan, kerja dengan birokrasi panjang yang tidak pasti, penuh tata cara yang mudah berubah, dll. Semuanya masuk akal. Tapi, jika kedua hasil yang berlawanan itu masuk akal, maka ada yang salah dengan urusan “masuk akal” itu: itu masuk akal hanya setelah dijelaskan hasilnya. Yang kita sebut sebagai akal sehat, common sense, sebenarnya tak mampu menyusun prediksi; selain hanya menyusun alasan atas sesuatu yang diketahui hasilnya.
Para politisi yang mencoba mengatasi masalah kemiskinan merasa telah paham mengapa manusia bisa miskin. Penyusun marketing plan menyusun kampanye dengan anggapan bahwa mereka tahu apa yang diinginkan target pasar, dan bagaimana membuat target itu tertarik lagi. Penyusun kebijakan atas kualitas pendidikan, kesehatan, dll, merasa paham atas efek skema insentif. Kesalahan pertama dalam meramalkan (dan mengubah) perilaku adalah bahwa kita mengira manusia digerakkan faktor2 seperti insentif, motivasi, dan nilai2. Sementara, banyak hal2 lain yang justru sengaja atau tidak turut mempengaruhi keputusan akhir: cara berkomunikasi (bentuk komunikasi, pilihan kata, latar musik, bentuk font, warna), atau hal2 lain yang cukup banyak. Kedua, pemodelan kita atas perilaku kolektif lebih buruk lagi. Ada berbagai warna interaksi internal di dalam kelompok yang masing2 pagarnya tak mesti tegas: alur kuasa dan pengetahuan sungguh cair, dan tak mudah diramalkan hanya dengan melihat elemen2nya. Kita cenderung menyederhanakan dengan menganggap kelompok sebagai individu, bernama crowd, market, karyawan, dll. Atau memilih representasi tak jelas seperti pemimpin, influencer, dll. Baru akhir2 ini dipahami bahwa penyederhanaan seperti itu bukan saja kurang tepat, tapi sungguh2 meleset.