问答题
"Wisdom of the Crowd": The Myths and Realities

Are the many wiser than the few? Phil Ball explores the latest evidence on what can make groups of people smarter—but can also make them wildly wrong.
Is The Lord of the Rings the greatest work of literature of the 20th Century? Is The Shawshank Redemption the best movie ever made? Both have been awarded these titles by public votes. You don"t have to be a literary or film snob to wonder about the wisdom of so-called "wisdom of the crowd",
In an age routinely denounced as selfishly individualistic, it"s curious that a great deal of faith still seems to lie with the judgment of the crowd, especially when it can apparently be far off the mark. Yet there is some truth underpinning the idea that the masses can make more accurate collective judgments than expert individuals. So why is a crowd sometimes right and sometimes disastrously wrong?
The notion that a group"s judgement can be surprisingly good was most compellingly justified in James Surowiecki"s 2005 book The Wisdom of Crowds , and is generally traced back to an observation by Charles Darwin"s cousin Francis Galton in 1907. Galton pointed out that the average of all the entries in a "guess the weight of the ox" competition at a country fair was amazingly accurate—beating not only most of the individual guesses but also those of alleged cattle experts. This is the essence of the wisdom of crowds: their average judgment converges on the right solution.
Still, Surowiecki also pointed out that the crowd is far from infallible. He explained that one requirement for a good crowd judgement is that people"s decisions are independent of one another. If everyone let themselves be influenced by each other"s guesses, there"s more chance that the guesses will drift towards a misplaced bias. This undermining effect of social influence was demonstrated in 2011 by a team at the Swiss Federal Institute of Technology (ETH) in Zurich.
They asked groups of participants to estimate certain quantities in geography or crime, about which none of them could be expected to have perfect knowledge but all could hazard a guess—the length of the Swiss-Italian border, for example, or the annual number of murders in Switzerland. The participants were offered modest financial rewards for good group guesses, to make sure they took the challenge seriously.
The researchers found that, as the amount of information participants were given about each other"s guesses increased, the range of their guesses got narrower, and the centre of this range could drift further from the true value. In other words, the groups were tending towards a consensus, to the detriment of accuracy.
This finding challenges a common view in management and politics that it is best to seek consensus in group decision making. What you can end up with instead is herding towards a relatively arbitrary position. Just how arbitrary depends on what kind of pool of opinions you start off with, according to subsequent work by one of the ETH team, Frank Schweitzer, and his colleagues. They say that if the group generally has good initial judgement, social influence can refine rather than degrade their collective decision.
No one should need warning about the dangers of herding among poorly informed decision-makers: copycat behaviour has been widely regarded as one of the major contributing factors to the financial crisis, and indeed to all financial crises of the past.
The Swiss team commented that this detrimental herding effect is likely to be even greater for deciding problems for which no objectively correct answer exists, which perhaps explains how democratic countries occasionally elect such astonishingly inept leaders.
There"s another key factor that makes the crowd accurate, or not. It has long been argued that the wisest crowds are the most diverse. That"s a conclusion supported in a 2004 study by Scott Page of the University of Michigan and Lu Hong of Loyola University in Chicago.
They showed that, in a theoretical model of group decision-making, a diverse group of problem-solvers made a better collective guess than that produced by the group of best-performing solvers.
In other words, diverse minds do better, when their decisions are averaged, than expert minds.
In fact, here"s a situation where a little knowledge can be a dangerous thing. A study in 2011 by a team led by Joseph Simmons of the Yale School of Management in New Haven, Connecticut found that group predictions about American football results were skewed away from the real outcomes by the over-confidence of the fans" decisions, which biased them towards alleged "favourites" in the outcomes of games.
All of these findings suggest that knowing who is in the crowd, and how diverse they are, is vital before you attribute to them any real wisdom.
Could there also be ways to make an existing crowd wiser? Last month, Anticline Davis-Stober of the University of Missouri and his co-workers presented calculations at a conference on Collective Intelligence that provide a few answers.
They first refined the statistical definition of what it means for a crowd to be wise—when, exactly, some aggregate of crowd judgments can be considered better than those of selected individuals.
This definition allowed the researchers to develop guidelines for improving the wisdom of a group. Previous work might imply that you should add random individuals whose decisions are unrelated to those of existing group members. That would be good, but it"s better still to add individuals who aren"t simply independent thinkers but whose views are "negatively correlated"—as different as possible—from the existing members. In other words, diversity trumps independence.
If you want accuracy, then, add those who might disagree strongly with your group. What do you reckon of the chances that managers and politicians will select such contrarian candidates to join them? All the same, armed with this information I intend to apply for a position in the Cabinet of the British government. They"d be wise not to refuse.
【正确答案】
【答案解析】
“群体智慧”:假设与现实

人越多越有智慧吗?菲尔·鲍尔(Phil Ball)提供的最新证据揭示了导致群体更具智慧但又可能大错特错的因素。
《指环王》是20世纪最伟大的文学作品吗?《肖申克的救赎》是有史以来最好的影片吗?至少公众投票给出了这样的结论。即便你对文学作品或者电影作品不那么挑剔,也不禁会对所谓的“群体智慧”产生疑问。
我们生活的这个年代通常被视为是自私自利、个人主义盛行的年代,但是人们很大程度上依然相信群体判断,特别是当这种群体判断明显偏颇之时,这令人匪夷所思。但是,认为群体判断比专家的个人判断更为准确也确有道理。那么,为什么群体判断有时准确无误有时却又谬以千里呢?
有关群体判断相当准确的看法在詹姆斯·索罗维基(James Surowiecki) 2005年出版的《群体智慧》( The Wisdom of Crowds )一书中得到了最有力的诠释,人们通常认为这种看法最早可以追溯到1907年查尔斯·达尔文(Charles Darwin)的堂兄弗朗西斯·高尔顿(cousin Francis Galton)所作的评论。高尔顿指出,在乡村集市上举行的“猜牛体重”比赛中人们所给答案的平均值相当准确,令人啧啧称奇,相比较之下,个人答案大部分都错了,就连那些所谓的养牛专家们也甘拜下风。这就是群体智慧的本质所在:群体判断的平均值就是正确答案。
尽管如此,索罗维基同时指出,群体判断与准确无误相去甚远。他解释称,准确的群体判断有一个前提条件,那就是个人判断彼此独立,没有相互影响。如果个体受到彼此判断的影响,那么群体判断出现偏差的可能性就增加了。为了证明人们之间相互影响产生的负面效应,2011年研究人员在苏黎世的瑞士联邦理工学院(Swiss Federal Institute of Technology)进行了群体实验。
研究人员要求参与实验的人预估有关地理或犯罪率的具体数值,这些信息没有哪个参与者能了如指掌,但是大家都可以做出猜测,比如:瑞士与意大利的边境线有多长,瑞士每年的谋杀案有多少起等。如果群体猜测结果较为接近,参与者可以获得一点现金奖励,这样可以保证大家认真参与这个实验。
研究人员发现,当向实验参与者提供越来越多有关其他人的猜测结果后,参与者的猜测范围缩小了,猜测的中间值也越来越偏离真实数值。换言之,大家的答案逐渐趋向一致,但是离正确答案却渐行渐远。
在管理和政治领域存在一种共识,即最好的决策方法是集体协商一致,因此,上述研究结果与这种普遍共识存在冲突。瑞士联邦理工学院的一组研究人员法兰克·施威茨(Frank Schweitzer)和他的同事得出的结论是,集体协商一致的结果是大家达成一个相对武断的结论,偏离的程度要看最开始得到的群体判断,如果这个群体通常有高质量的初步判断能力,那么成员间的相互影响会提高而不是降低群体决策的准确性。
与信息闭塞的决策者在一起的危险尽人皆知,毋庸讳言:人们普遍认为,仿冒行为是造成这次金融危机的主要原因,其实历史上所有的金融危机也概莫能外。
瑞士联邦理工学院的研究人员认为,这种从众心理十分有害,对那些客观上不存在正确答案的决策问题危害就更大了。这也许可以解释为什么民主国家有时选出的领导人比较昏庸无能。
左右群体决策正确与否还有一大因素。长期以来人们一直认为最具智慧的群体意见最不统一。密歇根大学的斯科特·佩奇(Scott Page)和芝加哥洛约拉大学的卢红(音译,Lu Hong)2004年所做的研究验证了这一结论。
研究显示,在群体决策的理论模型中,解决问题能力不一的群体所做的集体决策要优于个体解决问题能力较强的群体。
换言之,普通大众所做的决策取中间值后要优于专家决策。
事实上,还有一种情形就是有所了解也会出错。2011年康涅狄格州纽黑文市耶鲁大学管理学院的约瑟夫-西蒙斯(Joseph Simmons)牵头进行的一项研究显示,人们对橄榄球比赛的预测结果与实际结果并不吻合,主要是因为球迷们过于相信自己看好的球队获胜,所以预测时难免带有偏见。
所有这些研究结果都表明,在你做出判断时,一定要清楚群体的人员构成和多元化程度,这至关重要。
有没有让现有群体做出更准确判断的方法呢?上个月,密苏里大学的安迪克莱恩·戴维斯-施托贝尔(Anticline Davis-Stober)与同事在有关群体智慧的大会上展示了相关数据,从中可以找到一些答案。
他们首先对群体智慧的定义进行了统计学上的完善,即集体判断何时可以被视作优于个体判断。
根据这一统计学定义,研究人员可以为改善群体智慧出谋划策。这当然是好事,不过最好是能随机将群体外一些个人的意见考虑在内。这些随机个体不仅仅有独立的思考,他们的观点也与现有的群体成员大相径庭,是“负相关”。换言之,多样性比独立性更重要。
如果想提高准确性,那就把那些可能与你所在群体的观点迥异的人考虑在内进行统计。你认为企业高管和政客们允许与自己观点相左的人加入他们阵营的概率有多大呢?不管怎么样,了解了这一信息后,我想在英国政府内阁中谋求一个职位,如果他们明智的话就不要拒绝我。