订阅（按：本文的主要观点得到了我朋友zhangiii的启发，记得两年前那次深夜长谈中，当他做出那个摇晃筛子的手势时，我发现自己的许多想法一下子贯通了。）
向女王道歉背后隐藏的自大
辉格
2009年8月4日
近日，英国多位知名经济学家联名致信伊丽莎白女王，为“没能预测到国际金融危机的到来”而道歉，并称“没能预测出这次危机的时间、幅度和严重性，是许多智慧人士的集体失误”；此事缘于去年底女王对伦敦经济学院的一次访问，当时加里卡诺教授运用图表和数字向她仔细解释了这次危机是如何发生的，女王在听完之后随口问了一句：“为何当初没人注意到？”；看来这个问题把教授噎住了，经过半年多的反思和斟酌之后，他们终于给出了一个正式而慎重的回答。
有人说，相比那些满嘴大话、事先勇当风水师事后从不认错的专家学者们，这些懂得反思、勇于认错的经济学家，表现出了其谦逊和负责任，值得赞赏。然而，透过这封道歉信所表现的个人谦逊，我却恰恰看到了经济学家这个学术团体依然如故的自大；当他们说apologise时，其暗设的前提是：现有的经济学理论，已经为经济学家预见经济危机提供了足够的知识和分析工具，如果恰当运用它，人们便应该预见到“危机的时间、幅度和严重性”，而这一次经济学家们没有预见到，只能归结于他们的能力不足或缺乏审慎；在我看来，暗示这一前提表明了经济学界普遍存在的理性自大。
事实上，至少以目前的知识水平、模型建构能力和数据采集与处理能力而言，经济学家远远不能可信的预测到经济周期的时间、走向和幅度，历史上也从未有过任何理论和模型能作出这样的预测。的确有些人“说中”了一两次危机的大致时间，但这些通常都是长期唱衰派，危机发生之后，他会翻出自己一两年前的文章说：瞧，我早说过了；但如果你留心翻出他五六年甚至十几年来的文章，你会发现他们其实一直都在唱衰，做这样的预言家很容易，一旦经济进入繁荣期就开始作盛世危言，总有一天你会成为下一个克鲁格曼；然而真正有价值的预测必须给出其所基于的规范化理论和定量模型，说明在何种条件下，危机会在何时以何种深度和广度发生，显然，目前还不存在这样的东西。
如果说自以为有能力预见到经济周期还只是自大和狂妄，那么，意图借助这种预见来避免经济的波动和危机的发生，则是自不量力和自取其扰了；整个二十世纪，经济学家都在构思和试验各种反周期政策，结果却给经济带来了更多的混乱和伤害，他们之所以沉溺于此，屡败屡战，是因为他们总是将经济周期视为贻害无穷的洪水猛兽，却没有认识到，经济的周期性波动，是大量创新得以涌现、产业体系实现新旧更替的必要途径，也是经济系统从简单向复杂的演化得以进行的方式；这一洞见，熊彼特早在上世纪三十年代便已提出，但长期受到冷落，如果我们对比生物进化的历史，或许更容易看清其中的道理。
生物进化过程是渐变且没有远见的，在特定环境条件下，经由自然选择，物种在该环境下的生存能力会沿着小步改进的途径，趋向并最终达到一个局部最优状态，就像一个山谷里的旅行者，如果他每一步都往高处跨，最终会到达离他最近的一个山峰，用数学语言说：他找到了他所在单调区间的最大值；一旦同一环境下的各物种都达到局部最优点，生态系统便进入了均衡态；然而，如果这是进化史的全部故事，我们今天决不可能看到如此复杂多样的生物，实际上，进化史上更重要的故事是：环境条件及其所对应的均衡，总是会被各种不可预见的事件反复打破，而均衡的打破总是伴随着大规模物种灭绝和新物种大量涌现，然后是新一轮的渐进优化，最终导致新的均衡。
渐进进化的问题是，系统会被锁入到局部最优状态中：一旦你爬上附近的山头，尽管远处的山头更高，你却过不去，因为再跨出任何一步都是“退步”，进化上，退步意味着生存能力的下降和被自然选择淘汰的命运；经济系统也是如此，在均衡点上，每个企业都被锁入在盈亏平衡点附近，向任何方向作出改变都可能导致亏损甚至破产，一些在远期能带来丰厚收益的改良和创新，受到短期财务约束而无法实施，理论上，均衡点上严苛的财务约束使得任何创新都不可能发生；那么，现实中的创新和增长是如何发生的呢？答案是泡沫。
在景气期乐观氛围和宽松信贷条件下，企业财务约束被大大放松，银行家大手大脚四处撒钱，许多原本没有机会的创新项目得以启动，大量奇思妙想天马行空的主意因得到风投支持而走出车库。这一过程制造了大量的泡沫，一旦景气翻转，其中95%都会以失败和破产告终，而资本家们也会为当初的头脑发热而懊悔不迭；然而事实上，正是他们的无知和狂热在推动着经济的长期发展（正如他们的敏锐和精明推动了趋向均衡的短期增长），回顾历史，我们会发现许多产业新星都诞生于上一轮或上上轮泡沫，正是这5%的幸运儿在为经济不断注入活力，打破已有的均衡，创造新的产业格局并掀起新一轮竞赛，这便是所谓的创造性毁灭。
正如算法专家用振荡法解决非单调空间的寻优问题，大自然用环境变动打破生态平衡来推动进化，经济系统的内生景气萧条周期，是推动经济长期增长的基本动力机制；繁荣期的洪水会填满沟壑，让近处山坡上的人们跨过他们原本无法跨越的荒凉险恶之境，走向远处人迹未至的山头，正如气候变迁曾迫使我们的灵长类祖先走出他们依恋的森林，来到东非稀树草原，而冰川期裸露的大陆架，让我们的智人祖先穿越漫长的印度洋南岸，来到南亚、东亚和澳洲；第一轮互联网泡沫曾令大批ISP破产，却给第二轮繁荣中的网络公司和用户提供了近乎免费的带宽，正是从当初的废墟之中，走出了谷歌、亚马逊和阿里巴巴，他们不仅改变了整个信息产业，也正在改变着我们的生活方式和文明结构。
tcya @ 2011-12-24, 11:23
辉总说了泡沫的作用，但似乎还是没有说为什么会有泡沫啊。最后一段大自然推动进化这种有点群体选择味道的不能算理由吧
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辉格 回复:
12月 25th, 2011 at 22:52
对，是没说，不过在别处说过一些，我认为泡沫是一种事后落空了的、经由自我强化而膨胀的集体信念，由于人的信念容易被旁人的相同信念强化，因而一个群体起初稍稍偏向乐观的情绪，容易发展成集体的狂热。
最后一段跟群选择无关，因为涉及的可能是很多物种，这是个关于生态系统的观点。
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tcya @ 2011-12-26, 00:35
我的意思是看起来创新对采取这一策略的个体基本有害，（都破产了），好处是推动整个群体的进步，那它得以进化的理由看起来就很群体选择，所以我好奇创新和泡沫是怎么出现的
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辉格 回复:
1月 26th, 2013 at 00:04
失败率很高，但成功的收益很大，预期收益率可能不低。不过，据熊彼特说，创新总的来说对个人是不合算的，所以他认为“企业家精神”是一种独特的文化现象。
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辉格 @ 2012-07-29, 18:54
Daniel C. Dennett – Darwin’s Dangerous Idea, Ch.8, S.2, p.192
The idea of a fitness landscape was introduced by Sewall Wright (1932), and it has become a standard imagination prosthesis for evolutionary theorists. It has proven its value in literally thousands of applications, including many outside of evolutionary theory. In Artificial Intelligence, economics, and other problem-solving domains, the model of problem-solving by incremental hill-climbing (or “gradient ascent”) has been deservedly popular. It has even been popular enough to motivate theorists to calculate its limitations, which are severe. For certain classes of problems—or, in other words, in certain types of landscape—simple hill-climbing is quite impotent, for an intuitively obvious reason: the climbers get stuck on local second-rate summits instead of finding their way to the global summit, the Mount Everest of perfection. (The same limitations beset the method of simulated annealing.) The Local Rule is fundamental to Darwinism; it is equivalent to the requirement that there cannot be any intelligent (or “far-seeing” ) foresight in the design process, but only ultimately stupid opportunistic exploitation of whatever lucky lifting happens your way.
What Eigen has shown is that this simplest Darwinian model of steady improvement up a single slope of fitness to the optimal peak of perfection just doesn’t work to describe what goes on in molecular or viral evolution. The rate of adaptation by viruses (and also of bacteria and other pathogens) is measurably faster than the “classical” models predict—so fast that it seems to involve illicit “look-ahead” by the climbers. So does this mean that Darwinism must be abandoned? Not at all, for what counts as local depends (not surprisingly) on the scale you use.
Eigen draws our attention to the fact that when viruses evolve, they don’t go single-file; they travel in huge herds of almost identical variants, a fuzzy-edged cloud in the Library of Mendel that Eigen calls a “quasi-species.” We already saw the unimaginably large cloud of Moby Dick variants in the Library of Babel, but any actual library is likely to have more than one or two variant editions of a book on its shelves, and in the case of a really popular book like Moby Dick it is also likely to have multiple copies of the same edition. Like actual Moby Dick collections, then, actual viral clouds include multiple identical copies but also multiple copies of minor typographical variants, and this fact has some implications, according to Eigen, that have been ignored by “classical” Darwinians. It is the shape of the cloud of variants that holds the key to the speed of molecular evolution.
A classical term among geneticists for the canonical version of a species (analogous to the canonical text of Moby Dick) is the wild type. It was often supposed by biologists that among the many different genotypes in a population, the pure wild type would predominate. Analogous would be the claim that in any library collection of copies of Moby Dick, most copies will be of the received or canonical edition—if there is one! But this doesn’t have to be the case for organisms any more than for books in libraries. In fact, the wild type is really just an abstraction, like the Average Taxpayer, and a population may contain no individuals at all that have exactly “the” wild-type genome. (Of course, the same is true of books—scholars might debate for years over the purity of a particular word in a particular text, and until such debates were resolved, nobody could say exactly what the canonical or wild-type text of that work was, but the identity of the work would hardly be in jeopardy. James Joyce’s Ulysses would be a good case in point.)
Eigen points out that this distribution of the “essence” over a variety of nearly identical vehicles turns out to make that essence much more movable, much more adaptable, especially in “rugged” fitness landscapes, with multiple peaks and few smooth slopes. It permits the essence to send out efficient scouting parties into the neighboring hills and ridges, ignoring wasteful exploration of the valleys, and thereby vastly (not Vastly, but enough to make a huge difference) enhancing its capacity to find higher peaks, better optima, at some distance from its center, where the (virtual) wild type sits.
The reasons it works are summarized by Eigen as follows:
Functionally competent mutants, whose selection values come close to that of the wild type (though remaining below it), reach far higher population numbers than those that are functionally ineffective. An asymmetric spectrum of mutants builds up, in which mutants far removed from the wild type arise successively from intermediates. The population in such a chain of mutants is influenced decisively by the structure of the value landscape. The value landscape consists of connected plains, hills, and mountain ranges. In the mountain ranges, the mutant spectrum is widely scattered, and along ridges even distant relatives of the wild type appear with finite [that is, not infinitesimal] frequency. It is precisely in the mountainous regions that further selectively superior mutants can be expected. As soon as one of these turns up on the periphery of a mutation spectrum the established ensemble collapses. A new ensemble builds up around the superior mutant, which thus takes over the role of the wild type___This causal chain results in a kind of ‘mass action’, by which the superior mutants are tested with much higher probability than inferior mutants, even if the latter are an equal distance away from the wild type. [Eigen 1992, p. 25.]
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小橘子 回复:
1月 25th, 2013 at 22:19
for what counts as local depends (not surprisingly) on the scale you use.
这话透彻的。丹尼特真是哲学家。
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小橘子 回复:
1月 25th, 2013 at 23:06
这个尺度（scale）在渐进进化的语境中就是“物种”定义选取的宽窄。如果物种的定义选得宽，这个尺度就大，选得窄，尺度就小。在较大的尺度下，局部（local）的范围较大，物种的变异的可能性较广，进化的速度较快。以登山类比，较大的局部包含的高峰更高，在再次爬升前需要下降的高度更大。实际上，这个“再次爬升”“暂时下降”是一个较小局部的视角。在较大局部的视角上，登山者一直在爬升，就像在爬一座山峰时，即使在途中走进一个小坑（在较小局部上就可视为下降了），仍然可以说一直是在往上爬山。
在特定经济系统的语境中，这个尺度是时间尺度。即使不谈经济周期，经济系统也有短期均衡和长期均衡的不同尺度。在长期均衡的尺度上，经济系统就要偏离短期最优点。把这个尺度进一步拉大到包含多个经济周期，那么偏离的幅度以原来尺度的眼光看，就更大了。这个旧视角下的更大偏离，就被称为“泡沫”。
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小橘子 回复:
1月 25th, 2013 at 23:14
对旧尺度均衡点的更大偏离不只有泡沫，还有衰退。
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辉格 回复:
1月 25th, 2013 at 23:38
对，面临绝境时也会做些平时不可能做的事情
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辉格 回复:
1月 25th, 2013 at 23:37
嗯，是这样
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二逼 回复:
10月 26th, 2015 at 21:08
听了您的评论我自己脑补出来的景象更像是股票的K线图以及它不同级别的背离等等…
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海德沙龙（HeadSalon） » Blog Archive » [微言]国家与创新 @ 2012-10-16, 00:22
[…] @茶博未：请教@whigzhou 读了《向女王道歉背后隐藏的自大》末2段让我联想到印度政府对IT教育强力注资扶持，培养出过剩的IT专才，让全球尤其美国获得很多便宜好用的程序员。再考虑从军方apanet进化来的internet。政府比企业家更擅长大手笔制造泡沫。政府因此也是创新的一个重要发生器？ […]
小橘子 @ 2013-01-25, 21:48
好牛的文章，居然还是饭文。
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辉格 @ 2013-02-03, 01:59
p.58, ch.2 of Gregory Cochran and Henry Harpending: The 10,000 Year Explosion: How Civilization Accelerated Human Evolution
Paths on Fitness Landscapes
Another point: Ongoing natural selection in two populations can allow evolutionary events to occur that would be impossible in a single well-mixed population, since it allows for simultaneous exploration of divergent paths. Natural selection is short-sighted: Alleles increase in frequency because of their current advantage, not because they might someday be useful. Think of various possible solutions of some problem as hills, with higher hills corresponding to better solutions. Natural selection climbs up the first hill it chances upon; it can’t see that another solution has greater possibilities in the long run. Not only that: Since the environmental conditions of Europe and Africa were significantly different, evolution could try solutions in Europe that couldn’t be explored in Africa, because the initial step along that path had negative payoffs in Africa. In Europe, for example, you had to worry about staying warm enough, whereas Africans faced heat stress: These issues were important considerations in the evolution of larger brains. It may be that the relative unimportance of heat stress in Europe opened up some evolutionary pathways that had greater long-term possibilities than the ones that developed in Africa.
Consider an analogy from the history of technology. Somewhere back in late classical times, the use of the camel was perfected—a better saddle was developed, for example, one that allowed camels to carry heavy loads efficiently. Throughout most of the Middle East and North Africa, camels were (after those developments) a superior means of land transportation: They were cheaper than ox-drawn wagons and not dependent upon roads. Over a few centuries, people in areas where camels were available abandoned wheeled vehicles and roads almost entirely.19 You can still see the effects in the oldest sections of some cities in the Arab world, where the alleys are far too narrow to have ever passed a cart or wagon. Europeans, not having camels, had to stick with wheeled vehicles, which were clearly more expensive, given the infrastructure they required. But as it turned out, wheeled vehicles—in fact, the whole road/wheeled vehicle system—could be improved. Back then, when camels seemed so much better, who knew that someday there would be horse collars and nailed horseshoes, then improved bridge construction, suspensions that reduced road shock, macadamized roads, steam power, internal combustion engines, and ultimately the nuclear Delorean. The motto here is that sometimes the apparently inferior choice has a better upgrade path: Evolution can’t know this, and we aren’t particularly good at recognizing it ourselves. On the genetic level, it translates as follows: Natural selection may solve the same problems differently in different populations, and what appears to be the most elegant solution at the time may not in fact turn out to be the one that works best in the long run. The seemingly inferior choice may come out on top down the road. It is easy to think of plausible cases: Imagine, for example, that excess heat production limited the trend toward larger brains in Africa, while in the climate of Europe heat was not much of a problem. Later, as evolution fine-tuned the physiology of large brains, much of the heat problem was solved—and so the new brain could then spread in Africa as well.
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