第117集 说真话：不用虚假替代真实余秋雨 中国文化必修课
也许这是一个比较小众的需求，我就不展开解释具体为什么要这样设定了。大体来说就是如果系统语言为英语（不管是 iOS, macOS 还是 Windows），Apple Music 日本区的很多日文曲目、中文曲目也会变成拉丁字母显示，一堆罗马音实在很难接受……但如果单纯将系统语言改为日语，Apple Music 仍然不会改变语言。
前几天我终于发现 Apple Music（其实还包括 App Store）的语言到底受什么因素影响。Apple Music 使用什么语言，取决于登录 Apple Music / iTunes Store 时的系统语言。所以如果要想更改，先要更改系统语言，然后在 iTunes Store 首页底部 Sign Out，在系统语言为日语的前提下 Sign In，Apple Music, iTunes Store 和 App Store 就会是日语的了。这时再把系统语言切回英语，也不会对 Apple Music 有任何影响，就像之前系统语言切成日语，Apple Music 仍然保持英语不变一样。但系统语言为英语时不能有再次输入密码的操作，也就是说不能购买，购买必须在日文系统下购买，否则买回来就可能是罗马字。
Windows, macOS, iOS 的操作原理都相同。
2018 年 10 月 31 日更新：已购项目的问题现在我也基本上弄明白了。
首先，对新购买的歌曲，需要在日文系统下购买，否则买回来就可能是罗马字的，还会把整个 Apple Music / iTunes Store 变回英文——相当于购买行为是重新登录了一次，登录时的系统语言会改变 Apple Music 的语言。
对于已经购买的歌曲，如果是罗马字标题希望换成日文，那么可以先在 Library 里删掉，然后在日文系统下打开 iTunes，点选菜单栏的 Account（アカウント） – View My Account…（マイアカウントを表示） 输入密码（注意这里又算登录了一次，所以必须是日文系统下输入密码），然后在页面里找到 iTunes in the Cloud 里面的 Hidden Purchases，点 Manage 之后就能把之前删掉的已购项目找回来（点选「表示する」），然后再在已购项目里重新下载就可以了。
所以一面要用英文系统，一面要用日文 Apple Music 真的是神烦。
以下摘自《纽约时报》今年 1 月底的报道：
Students have long requested that Yale offer a course on positive psychology, according to Woo-Kyoung Ahn, director of undergraduate studies in psychology, who said she was “blown away” by Dr. Santos’s proposal for the class.
本科生心理研究主任 Woo-Kyoung Ahn 表示，长期以来，学生们一直要求耶鲁开设一门积极心理学课程。她说，桑托斯博士提出开设这门课程时，她「特别高兴」。
Administrators like Dr. Ahn expected significant enrollment for the class, but none anticipated it to be quite so large. Psychology and the Good Life, with 1,182 undergraduates currently enrolled, stands as the most popular course in Yale’s 316-year history. The previous record-holder — Psychology and the Law — was offered in 1992 and had about 1,050 students, according to Marvin Chun, the Yale College dean. Most large lectures at Yale don’t exceed 600.
安博士等管理人员预计这门课的选修人数会很多，但谁也没预料到会这么多。「心理学与美好生活」这门课目前有 1182 名本科生选修，成为耶鲁大学 316 年历史上最受欢迎的课程。耶鲁学院的院长 Marvin Chun 表示，此前的纪录保持者是 1992 年推出的「心理学和法律」课程，约有 1050 名学生选修。耶鲁的大多数大型课程的选修人数都不超过 600 人。Yale’s Most Popular Class Ever: Happiness
Google 了一下发现这门课已经上线 Coursera（《纽约时报》今年 1 月底报道这门课的时候还只是说很快就会上线）。最近几年觉得国内上 Coursera 的网络状况真的不太好，当然我也不是随时都在测试，毕竟试过几次感觉很糟糕之后就不会太有动力去听课了。但今天的网络效果很好，不知道是不是最近用了另一家代理服务……
附一封 Santos 老师的欢迎信：
Congratulations on taking part in this journey! Over the next several weeks, we’ll explore what new results in psychological science teach us about how to be happier, how to feel less stressed, and how to flourish more. We’ll then have a chance to put these scientific findings into practice by building the sorts of habits that will allow us to live a happier and more fulfilling life.
In Spring 2018, I taught “Psychology and the Good Life” for the first time. I created this Yale course because I was worried about the levels of student depression, anxiety, and stress that I was seeing as a Professor and Head of College at Yale. I originally developed this course to teach Yale students how the science of psychology can provide important hints about how to make wiser choices and how to live a life that’s happier and more fulfilling. Since I’m not an expert on positive psychology, I began by learning more about this topic, diving into the work of pioneering scientists like Martin Seligman, Ed Diener, Barbara Fredrickson, Sonja Lyubomirsky, Mihaly Csikszentmihalyi, Daniel Gilbert, Robert Emmons, and others. I also learned more about work in social psychology and behavior change, including work by scholars such as Liz Dunn, Mike Norton, Nick Epley, Gabriele Oettingen, and others. The Yale course was my attempt at synthesizing work in positive psychology along with the science of behavior change. My goal was to present these scientific findings in a way that made it clear how this science could be applied in people’s daily lives.
When I first developed the class, I had no idea it would become the most popular class ever taught at Yale University. The Yale class was featured in both the national and international news media, and I was flooded with requests from people around the world to find a way to share the content of this Yale class more broadly.
This Coursera class is an attempt to do just that. My goal is to share the insights from that popular Yale class with learners far beyond Yale. To make the lectures feel more intimate, we filmed at my home in one of Yale’s residential colleges with a small group of Yale students in the audience. I hope you’ll enjoy this more personal format, which allows you to hear the sorts of questions Yale students had about the material and how they applied the science in their daily lives. We understand that many of you taking the course are not currently college students, but we hope you see yourselves as though you are part of this virtual classroom.
During this course, you’ll have the opportunity to enhance your own well-being by implementing a few simple research-based methods to your own life.
I am thrilled to share this information with a wider audience. As you go through the lessons please share your feedback with the course team! You can direct item-specific feedback via content flags and general course feedback in the Discussion Forums or in the post-course survey when you complete the course.
这是一篇 5 年前写下的留在知乎草稿箱里的文章（估计当时我还想继续往下写几个部分但中途放弃了）。突然翻到了之后通读了一遍，觉得还行，就发在这里，只字未改。
非科班出身的民间哲学家想要写出好的东西不一定要去学哲学史，只要你能清晰地思考和表达自己感兴趣的哲学问题，就可以写出能被读懂、能被理解、能用来正常讨论的哲学文字。至于这些文字有多少学术上的价值则是后话，但只要这些文字是意义明确的思考，那么能让自己明白、让能读到这篇文字的少数几个人明白就已经有意义。只不过我会怀疑写这样的东西是不是还能让那些「爱好者」感兴趣。因为不少民哲给我的印象是「自 high」的，而自 high 到一定程度就写不出正常的、能让别人看懂的东西。
To appreciate the depth of this gap, imagine the difficulties that a scientist would face in trying to express some obvious causal relationships—say, that the barometer reading B tracks the atmospheric pressure P. We can easily write down this relationship in an equation such as B = kP, where k is some constant of proportionality. The rules of algebra now permit us to rewrite this same equation in a wild variety of forms, for example, P = B/k, k = B/P, or B–kP = 0. They all mean the same thing—that if we know any two of the three quantities, the third is determined. None of the letters k, B, or P is in any mathematical way privileged over any of the others. How then can we express our strong conviction that it is the pressure that causes the barometer to change and not the other way around? And if we cannot express even this, how can we hope to express the many other causal convictions that do not have mathematical formulas, such as that the rooster’s crow does not cause the sun to rise?
My college professors could not do it and never complained. I would be willing to bet that none of yours ever did either. We now understand why: never were they shown a mathematical language of causes; nor were they shown its benefits. It is in fact an indictment of science that it has neglected to develop such a language for so many generations. Everyone knows that flipping a switch will cause a light to turn on or off and that a hot, sultry summer afternoon will cause sales to go up at the local ice-cream parlor. Why then have scientists not captured such obvious facts in formulas, as they did with the basic laws of optics, mechanics, or geometry? Why have they allowed these facts to languish in bare intuition, deprived of mathematical tools that have enabled other branches of science to flourish and mature?
Part of the answer is that scientific tools are developed to meet scientific needs. Precisely because we are so good at handling questions about switches, ice cream, and barometers, our need for special mathematical machinery to handle them was not obvious. But as scientific curiosity increased and we began posing causal questions in complex legal, business, medical, and policy-making situations, we found ourselves lacking the tools and principles that mature science should provide.
Belated awakenings of this sort are not uncommon in science. For example, until about four hundred years ago, people were quite happy with their natural ability to manage the uncertainties in daily life, from crossing a street to risking a fistfight. Only after gamblers invented intricate games of chance, sometimes carefully designed to trick us into making bad choices, did mathematicians like Blaise Pascal (1654), Pierre de Fermat (1654), and Christiaan Huygens (1657) find it necessary to develop what we today call probability theory. Likewise, only when insurance organizations demanded accurate estimates of life annuity did mathematicians like Edmond Halley (1693) and Abraham de Moivre (1725) begin looking at mortality tables to calculate life expectancies. Similarly, astronomers’ demands for accurate predictions of celestial motion led Jacob Bernoulli, Pierre-Simon Laplace, and Carl Friedrich Gauss to develop a theory of errors to help us extract signals from noise. These methods were all predecessors of today’s statistics.
Ironically, the need for a theory of causation began to surface at the same time that statistics came into being. In fact, modern statistics hatched from the causal questions that Galton and Pearson asked about heredity and their ingenious attempts to answer them using cross-generational data. Unfortunately, they failed in this endeavor, and rather than pause to ask why, they declared those questions off limits and turned to developing a thriving, causality-free enterprise called statistics.
This was a critical moment in the history of science. The opportunity to equip causal questions with a language of their own came very close to being realized but was squandered. In the following years, these questions were declared unscientific and went underground. Despite heroic efforts by the geneticist Sewall Wright (1889–1988), causal vocabulary was virtually prohibited for more than half a century. And when you prohibit speech, you prohibit thought and stifle principles, methods, and tools.
Readers do not have to be scientists to witness this prohibition. In Statistics 101, every student learns to chant, “Correlation is not causation.” With good reason! The rooster’s crow is highly correlated with the sunrise; yet it does not cause the sunrise.
Unfortunately, statistics has fetishized this commonsense observation. It tells us that correlation is not causation, but it does not tell us what causation is. In vain will you search the index of a statistics textbook for an entry on “cause.” Students are not allowed to say that X is the cause of Y—only that X and Y are “related” or “associated.”
… I hope with this book to convince you that data are profoundly dumb. Data can tell you that the people who took a medicine recovered faster than those who did not take it, but they can’t tell you why. Maybe those who took the medicine did so because they could afford it and would have recovered just as fast without it.
Over and over again, in science and in business, we see situations where mere data aren’t enough. Most big-data enthusiasts, while somewhat aware of these limitations, continue the chase after data-centric intelligence, as if we were still in the Prohibition era.
As I mentioned earlier, things have changed dramatically in the past three decades. Nowadays, thanks to carefully crafted causal models, contemporary scientists can address problems that would have once been considered unsolvable or even beyond the pale of scientific inquiry. For example, only a hundred years ago, the question of whether cigarette smoking causes a health hazard would have been considered unscientific. The mere mention of the words “cause” or “effect” would create a storm of objections in any reputable statistical journal.
Even two decades ago, asking a statistician a question like “Was it the aspirin that stopped my headache?” would have been like asking if he believed in voodoo. To quote an esteemed colleague of mine, it would be “more of a cocktail conversation topic than a scientific inquiry.” But today, epidemiologists, social scientists, computer scientists, and at least some enlightened economists and statisticians pose such questions routinely and answer them with mathematical precision. To me, this change is nothing short of a revolution. I dare to call it the Causal Revolution, a scientific shakeup that embraces rather than denies our innate cognitive gift of understanding cause and effect.
Side by side with this diagrammatic “language of knowledge,” we also have a symbolic “language of queries” to express the questions we want answers to. For example, if we are interested in the effect of a drug (D) on lifespan (L), then our query might be written symbolically as: P(L|do(D)). In other words, what is the probability (P) that a typical patient would survive L years if made to take the drug? This question describes what epidemiologists would call an intervention or a treatment and corresponds to what we measure in a clinical trial. In many cases we may also wish to compare P(L|do(D)) with P(L |do(not-D)); the latter describes patients denied treatment, also called the “control” patients. The do-operator signifies that we are dealing with an intervention rather than a passive observation; classical statistics has nothing remotely similar to this operator.
We must invoke an intervention operator do(D) to ensure that the observed change in Lifespan L is due to the drug itself and is not confounded with other factors that tend to shorten or lengthen life. If, instead of intervening, we let the patient himself decide whether to take the drug, those other factors might influence his decision, and lifespan differences between taking and not taking the drug would no longer be solely due to the drug. For example, suppose only those who were terminally ill took the drug. Such persons would surely differ from those who did not take the drug, and a comparison of the two groups would reflect differences in the severity of their disease rather than the effect of the drug. By contrast, forcing patients to take or refrain from taking the drug, regardless of preconditions, would wash away preexisting differences and provide a valid comparison.
Mathematically, we write the observed frequency of Lifespan L among patients who voluntarily take the drug as P(L|D), which is the standard conditional probability used in statistical textbooks. This expression stands for the probability (P) of Lifespan L conditional on seeing the patient take Drug D. Note that P(L|D) may be totally different from P(L|do(D)). This difference between seeing and doing is fundamental and explains why we do not regard the falling barometer to be a cause of the coming storm. Seeing the barometer fall increases the probability of the storm, while forcing it to fall does not affect this probability.Judea Pearl. 2018. The Book of Why