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经济学人双语科技新闻在线试听:集体的"智商"部分取决于成员的无知

Lily85 于2013-01-04发布 l 已有人浏览
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人类常常自诩为最聪明的动物。事实真的如此吗?美国普林斯顿大学Iain Couzin认为人类需要向其他生物学习,学习他们的集体行为。正如他在华盛顿美国科学促进会的会议上所说,动物群体往往会做出明智的决定,即便群体中的大多对所发生的事情一无所知。
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Science and Technology Collective behaviour
科技 集体行为

A group's "intelligence" depends in part on its members' ignorance
集体的"智商"部分取决于成员的无知

HUMAN beings like to think of themselves as the animal kingdom's smartest alecks. It may come as a surprise to some, therefore, that Iain Couzin of Princeton University believes they have something to learn from lesser creatures that move about in a large crowd. As he told the AAAS meeting in Washington, DC, groups of animals often make what look like wise decisions, even when most of the members of those groups are ignorant of what is going on.
人类常常自诩为最聪明的动物。事实真的如此吗?美国普林斯顿大学Iain Couzin认为人类需要向其他生物学习,学习他们的集体行为。正如他在华盛顿美国科学促进会的会议上所说,动物群体往往会做出明智的决定,即便群体中的大多对所发生的事情一无所知。

Coming to that conclusion was not easy. Before lessons can be drawn from critters perched on the lower rungs of the evolutionary ladder, their behaviour must first be understood. One way to do this is to tag them with devices that follow them around—motion-capture sensors, radio transmitters or global-positioning-system detectors that can put a precise figure on their movements.
得出这个结论并不容易。想要从低等生物上获得研究结果,首先得了解他们的行为。一种做法就是用仪器来捕捉他们的行动,如:动作捕捉感应器,无线电发射器或全球定位系统探测器,这些仪器可以把他们的动作准确记录下来。

Unfortunately, it is impossible to tag more than a few individuals in a herd, flock or swarm.Researchers have therefore tended to extrapolate from these few results by using various computer models. Dr Couzin has done quite a bit of this himself. Most recently, he has modelled the behaviour of shoals of fish. He posited that how they swim will depend on each individual's competing tendencies to stick close to the others (and thus move in the same direction as them) while not actually getting too close to any particular other fish. It turns out that by fiddling with these tendencies, a virtual shoal can be made to swirl spontaneously in a circle, just like some real species do.
不幸的是,要想标记出一大群动物的行为几乎是不可能的。研究人员因此倾向于通过使用各种计算机模型来推断这些鲜为人知的结论。Couzin博士身先士卒。最近,他用计算机模仿了鱼群的行为。他设想,鱼群的游动主要取决于每条鱼之间的相互挤碰(因此鱼群会向着一个方向游动),而实际上并不会挤碰旁边的其他鱼,只是一种趋势而已。事实证明,鱼群内部像这样彼此间的相互挤碰,不自然的就会是鱼群形成螺旋状。

That is a start. But real shoals do not exist to swim in circles. Their purpose is to help their members eat and avoid being eaten. At any one time, however, only some individuals know about—and can thus react to—food and threats. Dr Couzin therefore wanted to find out how such temporary leaders influence the behaviour of the rest.
表面如此,但是真正的鱼群是不会螺旋状游动的。他们的真正目的其实是相互帮助觅食和躲避掠食者。然而,任何时候只有鱼群中的少数才会会对食物和威胁作出反应。因此,Couzin博士十分想弄明白这些所谓的"领导"是如何影响鱼群的行为的?

He discovered that leadership is extremely efficient. The larger a shoal is, the smaller is the proportion of it that needs to know what is actually going on for it to feed and avoid predation effectively. Indeed, having too many leaders with conflicting opinions results in confusion. At least, that is true in the model. He is now testing it in reality.
他发现,领导力至关重要。鱼群越大,花在捕食和躲避掠食者方面的精力就越少。而事实上,领导太多,意见相左,就会陷入混乱,至少在模拟试验中是这样。现在,他要在真实的环境中开始测试了。

Tracking individual fish in a shoal is hard. Fortunately, advances in pattern-recognition software mean it is no longer impossible. Systems designed to follow people are now clever enough not only to track a person in a crowd, but also to tell in which direction his head is turned. Since, from above, the oval shape of a human head is not unlike the oblong body of a fish, this software can, with a little tweaking, follow piscine antics, too.
追踪鱼群中的个体十分的困难,幸运的是,外形识别软件的进步意味着它不再成为不可能。人类行为识别系统已经十分的智能化了,不仅能在人群中追踪个体,而且还可以告诉他的头正朝向哪里。从上面看,既然人类头颅的椭圆形状与鱼类长圆型的体型没区别不大,那么,这个软件只需稍加调整便可识别鱼了。

Robo fish
机器鱼

Dr Couzin has been using a program developed by Colin Twomey, a graduate student at his laboratory, to track individual fish in a tank. The result is not just a model of shoaling fish, but a precise numerical representation of their actual movements and fields of vision. That means it is possible to investigate whether real-life fishy leaders have the same effect on a group as their virtual kin.
Couzin博士一直在使用研究生科林图梅开发的一个程序,该程序能追踪鱼缸里的鱼的行为。研究结果不仅能反映整个鱼群的活动,并且能用数字精确地表示出鱼群的确切行动和视野。这意味着它可以研究出是否鱼群中的"领导"对整个鱼群有影响。

Alas, merely observing a shoal does not make it clear which individuals lead and which follow. Instead, Dr Couzin has built a biddable robot three-spined stickleback. A preliminary study of a shoal of ten flesh-and-blood sticklebacks shows that they do indeed mingle with the robot and that they follow its leadership cues as predicted. He is now making a robot predator to see how the shoal reacts to less benign intruders.
唉,只观察鱼群不能说明鱼群中谁是领导,谁是追随者。相反,Couzin博士已经设计了一条机器三刺鱼。通过把机器三刺鱼跟10条真正的三刺鱼混在一起对进行研究——正与先前的预测一样,他们的确听其号令。现在,博士正在做一个机器捕食者,看看鱼群对这些凶恶入侵者将会会做出如何反映。

If the models are anything to go by, the best outcome for the group—in this case, not being eaten—seems to depend on most members' being blissfully unaware of the world outside the shoal and simply taking their cue from others. This phenomenon, Dr Couzin argues, applies to all manner of organisms, from individual cells in a tissue to (rather worryingly) voters in the democratic process. His team has already begun probing the question of voting patterns. But is ignorance really political bliss? Dr Couzin's models do not yet capture what happens when the leaders themselves turn out to be sharks.
如果这些模型能够说明问题,要得出最好的结果——没有被吃掉,似乎取决于鱼群中的大多数对周围所发生的情况一无所知,而只是默默地接受其他鱼的暗示。这种现象,Couzin博士认为在所有生物体中都存在,无论是单个细胞生命,还是在民主进程中(令人担忧的)的选民。博士的研究小组已经着手开始使用这一成果来研究投票形式的问题了,可是,无知真的会带来政治上的福音吗?Couzin博士的模型没能证明,如果鱼群中的"领导"变成鲨鱼会怎样?

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