<<  英国：地狱机场 | 首 页 | 电脑视觉——轻松识别  >>

  • 群体行为

    2007-08-14

    [2007.07]Swarm behavior

    Swarm Behavior群体行为
    JULY 2007∣By Peter Miller∣2007年7月∣作者：皮特•米勒
    From www.nationalgeographic.com∣摘自美国国家地理杂志网站

    A single ant or bee isn't smart, but their colonies are. The study of swarm intelligence is providing insights that can help humans manage complex systems, from truck routing to military robots.
    单一一个蚂蚁或蜜蜂仅仅是一条虫，可它们群集起来却非同小可。对蜂群或蚁群智慧的研究为人类管理复杂度高的系统提供了一定的洞察力，小到卡车的行程定位，大则对军用机器人的操控。

                     

    I used to think ants knew what they were doing. The ones marching across my kitchen counter looked so confident, I just figured they had a plan, knew where they were going and what needed to be done. How else could ants organize highways, build elaborate nests, stage epic raids, and do all the other things ants do?
    我过去一直以为蚂蚁知道它们在做什么。那些大步阅军式的蚁群穿越我的厨房餐台时是那么地信心十足，我就认为他们有着一个行动计划，并知道自己去向何处与将会做些什么。蚂蚁是如何建立起高速公路，建造自己精心设计的巢穴并上演一出出史诗般的猎杀行动，如此种种的所有事情？

    Turns out I was wrong. Ants aren't clever little engineers, architects, or warriors after all—at least not as individuals. When it comes to deciding what to do next, most ants don't have a clue. "If you watch an ant try to accomplish something, you'll be impressed by how inept it is," says Deborah M. Gordon, a biologist at Stanford University.
    结果是我在这方面有些误解。蚂蚁并不是聪明的小工程师、建筑师或战争的勇士—至少它们在单独行事时无法扮演这些角色。当要决定下一步的行动时，大多数的蚂 蚁都将不知所措。“如果你观察一只想要完成某事的蚂蚁，你会发现它是多么的无能”，斯坦福大学一位名叫德玻拉 M•戈尔顿（Deborah M. Gordon）的生物学家讲道。

    How do we explain, then, the success of Earth's 12,000 or so known ant species? They must have learned something in 140 million years.
    那么，我们又怎样去解释世界上存活的一万二千种已知蚂蚁类型？这些蚂蚁肯定是在一亿四千万年的岁月中学到很多东西。

    "Ants aren't smart," Gordon says. "Ant colonies are." A colony can solve problems unthinkable for individual ants, such as finding the shortest path to the best food source, allocating workers to different tasks, or defending a territory from neighbors. As individuals, ants might be tiny dummies, but as colonies they respond quickly and effectively to their environment. They do it with something called swarm intelligence.
    “蚂蚁并不聪明”，戈尔顿博士讲道，“蚁群才是”。单个蚂蚁百思不得其解的问题能轻易地被蚁群解决，比如说找到最佳食物源头的捷径，又如给工蚁们分配不同 的工作，或者是保卫自己的领地不受邻蚁侵犯。单个蚂蚁可能只是个小小的笨蛋，而群体蚂蚁却能对其生存环境做出快速与有效的反应。他们是通过使用一种称为 “群体智慧”来完成的。

    Where this intelligence comes from raises a fundamental question in nature: How do the simple actions of individuals add up to the complex behavior of a group? How do hundreds of honeybees make a critical decision about their hive if many of them disagree? What enables a school of herring to coordinate its movements so precisely it can change direction in a flash, like a single, silvery organism? The collective abilities of such animals—none of which grasps the big picture, but each of which contributes to the group's success—seem miraculous even to the biologists who know them best. Yet during the past few decades, researchers have come up with intriguing insights.
    这种智慧从何而来提出了自然界一个基本的问题：简单的单个体的行为是如何集合为群体复杂多变的群体行为？成百上千的蜜蜂是如何在多数蜜蜂反对的情况下做出 抛弃蜂巢的紧急决定？又是什么使得一群鲱鱼如此精确地控制自身的行动，以致于它们能在一瞬间改变它们的航向，就像一个单一的银色有机体？这些动物说聚集的 能力简直不可思议，甚至连研究它们的生物学家也如此认为。它们中没有任何的单一体能够做出惊人的决定，但是它们每个单一体都对群体的成功奉献自己的力量。 然而，在过去的几十年中，研究人员们已经得到对此现象引人入胜的深入的理解。

    One key to an ant colony, for example, is that no one's in charge. No generals command ant warriors. No managers boss ant workers. The queen plays no role except to lay eggs. Even with half a million ants, a colony functions just fine with no management at all—at least none that we would recognize. It relies instead upon countless interactions between individual ants, each of which is following simple rules of thumb. Scientists describe such a system as self-organizing.
    比如说，蚁群之所以效率高的原因之一便是没有任何一只蚂蚁会独掌大权。没有蚂蚁将军会命令蚂蚁战士，没有蚂蚁经理会呵斥蚂蚁员工，蚁后除了产卵之外没有任 何用处。甚至对于一个五十万的蚂蚁大军来说，蚁群在没有任何管理安排的情况下也能顺利地完成一切，至少我们发现不了它们所实行的管理。相反，这些全部依赖 的是个体蚂蚁之间无数次的相互影响的作用，即每个蚂蚁都遵循着简单的经验准则。科学家们将这种系统命名为“自我组织”。

    Consider the problem of job allocation. In the Arizona desert where Deborah Gordon studies red harvester ants (Pogonomyrmex barbatus), a colony calculates each morning how many workers to send out foraging for food. The number can change, depending on conditions. Have foragers recently discovered a bonanza of tasty seeds? More ants may be needed to haul the bounty home. Was the nest damaged by a storm last night? Additional maintenance workers may be held back to make repairs. An ant might be a nest worker one day, a trash collector the next. But how does a colony make such adjustments if no one's in charge? Gordon has a theory.
    我们先来看看蚂蚁在工作分配上的问题。在亚利桑那沙漠，德玻拉•戈尔顿对红色收获蚁(Pogonomyrmex barbatus)进行了研究，他每个早上都会计算这个蚁群会安排多少蚂蚁出外寻觅食物。据他观察，因环境的变化，出外觅食的蚂蚁在数量上也会进行变化。 如果最近觅食蚁交到好运发现了一些美味可口的果籽，那更多的蚂蚁就会被召集将这些恩惠的食物拖回蚁巢去。而如果蚁巢在前个夜晚遭至暴风雨的袭击，那么额外 的保全工蚁便会被召回以进行对蚁巢的维修。一只蚂蚁在第一天可能是蚁巢保全工，第二天也许就成了食物拾取工。但是，如果蚁群中没有任何集权的负责工，它们 又如何做出这种战略上的调整？戈尔顿博士有着自己的一套理论。

    Ants communicate by touch and smell. When one ant bumps into another, it sniffs with its antennae to find out if the other belongs to the same nest and where it has been working. (Ants that work outside the nest smell different from those that stay inside.) Before they leave the nest each day, foragers normally wait for early morning patrollers to return. As patrollers enter the nest, they touch antennae briefly with foragers.
    蚂蚁是通过触觉与嗅觉进行交流的。当一只蚂蚁撞击到另一只蚂蚁，它便会用它的触角去用力地嗅以发现这只蚂蚁是否是同一个蚁巢的，与它一直在哪里觅食。（在 外面觅食的蚂蚁身上所散发的气味与在蚁巢内工作的蚂蚁气味不同。）在外勤觅食工离开蚁巢之前，它们通常会等零时的巡逻工回来之后才出去。当巡逻工回来之 后，它们会与觅食工很快地碰碰触角。

    "When a forager has contact with a patroller, it's a stimulus for the forager to go out," Gordon says. "But the forager needs several contacts no more than ten seconds apart before it will go out."
    “当觅食工与巡逻工碰过触角之后，也就意味着它们该出外觅食了”，戈尔顿博士讲道。“但是觅食工在出外之前与巡逻工的触角接触不会超过十秒钟。”

    To see how this works, Gordon and her collaborator Michael Greene of the University of Colorado at Denver captured patroller ants as they left a nest one morning. After waiting half an hour, they simulated the ants' return by dropping glass beads into the nest entrance at regular intervals—some coated with patroller scent, some with maintenance worker scent, some with no scent. Only the beads coated with patroller scent stimulated foragers to leave the nest. Their conclusion: Foragers use the rate of their encounters with patrollers to tell if it's safe to go out. (If you bump into patrollers at the right rate, it's time to go foraging. If not, better wait. It might be too windy, or there might be a hungry lizard waiting out there.) Once the ants start foraging and bringing back food, other ants join the effort, depending on the rate at which they encounter returning foragers.
    为了找出这种现象如何出现，戈尔顿博士与她的同事—来自美国丹佛科罗拉多大学的麦克尔•格林利（Michael Greene）--在一个清晨捕获了许多刚离开蚁巢的巡逻蚁。在等了半个时辰之后，她们模仿出了巡逻蚁的回巢—在正常间隔后将一些玻璃珠放在了蚁巢的洞 口，一些带有巡逻蚁的气味，一些带有保全蚁的气味，而一些没有气味。据观察，只有那些带有巡逻蚁气味的珠子能刺激觅食蚁离开蚁巢。因此戈尔顿博士得出结 论：觅食蚁会依赖与巡逻蚁的接触频率来判定外出的安全性。（如果接触的是巡逻工正确的频率，就可以外出；而如果频率不对，则最好再等等。也许外面风沙大， 或蚁巢外面正有着一只饥饿的蜥蜴在等候。）一旦觅食蚁开始觅食并准备把食物带回来，其它的工蚁便开始加入，而这都依赖回程觅食蚁与其它蚂蚁的接触频率。

    "A forager won't come back until it finds something," Gordon says. "The less food there is, the longer it takes the forager to find it and get back. The more food there is, the faster it comes back. So nobody's deciding whether it's a good day to forage. The collective is, but no particular ant is."
    “一只觅食蚁在发现食物之前是不会返回蚁巢的”，戈尔顿博士讲道。“外面的食物越少，觅食蚁就会花越多的时间寻找食物并将食物带回蚁巢。外面的食物越多，它便回来得越快。因此任何蚂蚁都无法决定外出的一天将是收成的一天。蚁群可以决定，但单个蚂蚁不行。”

    That's how swarm intelligence works: simple creatures following simple rules, each one acting on local information. No ant sees the big picture. No ant tells any other ant what to do. Some ant species may go about this with more sophistication than others. (Temnothorax albipennis, for example, can rate the quality of a potential nest site using multiple criteria.) But the bottom line, says Iain Couzin, a biologist at Oxford and Princeton Universities, is that no leadership is required. "Even complex behavior may be coordinated by relatively simple interactions," he says.
    这就是“群体智慧”的工作原理：简单的生物群遵循简单的法则，每个单个生物都根据局部信息执行工作。没有任何一只蚂蚁可以看到整个任务计划，也没有蚂蚁能 知道其它蚂蚁在做什么。有些种类的蚂蚁可能会比其它种类的蚂蚁将此能力运用得更娴熟。（比如学名为“Temnothorax albipennis”的蚂蚁能够通过多重标准来评估一个潜在的蚁巢地点的安全度。）但是一位兼任牛津大学与普林斯顿大学的生物学家莱恩•楚金（Iain Couzin）讲道，执行该原理的底线是不需要任何的领导。他讲道，“即使是复杂的行为也可能只是通过相对简单的互相接触来完成。”

    Inspired by the elegance of this idea, Marco Dorigo, a computer scientist at the Université Libre in Brussels, used his knowledge of ant behavior in 1991 to create mathematical procedures for solving particularly complex human problems, such as routing trucks, scheduling airlines, or guiding military robots.
    受到该构想的精确简明的启发，布鲁塞尔比利时自由大学（Université Libre in Brussels）的一位名叫马克•多日葛的计算机科学家于1991年将自己对蚂蚁行为的认知进行了运用，他为解决特别复杂的人类问题创建了一些数学程 序，比如说卡车的行程定位、航线的安排与军用机器人的操控。

    In Houston, for example, a company named American Air Liquide has been using an ant-based strategy to manage a complex business problem. The company produces industrial and medical gases, mostly nitrogen, oxygen, and hydrogen, at about a hundred locations in the United States and delivers them to 6,000 sites, using pipelines, railcars, and 400 trucks. Deregulated power markets in some regions (the price of electricity changes every 15 minutes in parts of Texas) add yet another layer of complexity.
    比如在美国休斯顿城，一个美国气体制造公司就一直在使用蚂蚁策略管理一个复杂的商业问题。这个公司制造工业用气与医疗用气，主要产品是氮气、氧气与氢气， 该公司在美国有大约100多个工厂并通过管道、铁路货车与四百俩卡车向六千多个用气点送气。在一些地区由于能源市场被解除管制，如得克萨斯州的部分地区的 电价每十五分钟就会改变，而这就增添了更多的复杂性。

    "Right now in Houston, the price is $44 a megawatt for an industrial customer," says Charles N. Harper, who oversees the supply system at Air Liquide. "Last night the price went up to $64, and Monday when the cold front came through, it went up to $210." The company needed a way to pull it all together.
    “现在在休斯敦，工业用户的电价为每兆瓦是44美元”，在气体制造公司监管供应系统的查尔斯 N•哈普尔（Charles N. Harper）讲道。“昨晚电价升到64美元，而周一冷锋来临的时候，电价更是升到了210美元。”该公司需要一种共同努力的方法。

    Working with the Bios Group (now NuTech Solutions), a firm that specialized in artificial intelligence, Air Liquide developed a computer model based on algorithms inspired by the foraging behavior of Argentine ants (Linepithema humile), a species that deposits chemical substances called pheromones.
    美国Air Liquide公司与一家专业研究人工智能且名为NuTech Solutions（原Bios Group）的公司进行了合作，并开发出了一套基于运算法则的计算机模式，这个法则是受到阿根廷蚂蚁（学名Linepithema humile）的觅食行为的启发—这种蚂蚁会储备一种叫做信息素的化学物质。

    "When these ants bring food back to the nest, they lay a pheromone trail that tells other ants to go get more food," Harper explains. "The pheromone trail gets reinforced every time an ant goes out and comes back, kind of like when you wear a trail in the forest to collect wood. So we developed a program that sends out billions of software ants to find out where the pheromone trails are strongest for our truck routes."
    “当这些蚂蚁将食物带到蚁巢时，它们会在途中留下追踪信息素，这样其它蚂蚁也能去找到更多的食物”，哈普尔解释道。“而这种追踪信息素在每次觅食蚁出去再 回来便会增强，有点像你在森林为了收集木材而做标记。因此我们开发出一种程序，这个能派遣出上十亿的软件蚂蚁去发现哪里是找到卡车行程的最强的追踪信息 素。”

    Ants had evolved an efficient method to find the best routes in their neighborhoods. Why not follow their example? So Air Liquide combined the ant approach with other artificial intelligence techniques to consider every permutation of plant scheduling, weather, and truck routing—millions of possible decisions and outcomes a day. Every night, forecasts of customer demand and manufacturing costs are fed into the model.
    蚂蚁已经发展出一种找到邻蚁最佳路线的有效方法，我们人类为什么不采取它们的方法？所以美国气体制造公司将蚂蚁法则与其它人工智能技术进行了综合，以考虑 每一次企业工作制度的变化、客户需求以及卡车路线的变化—这包含了一天中所有可能的决策与最终结果。每天晚上，客户需求与制造成本的预测就被输入到了模式 之中。

    "It takes four hours to run, even with the biggest computers we have," Harper says. "But at six o'clock every morning we get a solution that says how we're going to manage our day."
    “即使我们拥有最大型的计算机，这个过程也需要四个小时来完成”，哈普尔先生讲道。“但是，在每天早上的六点钟我们就能得到一个解决方案，它会告诉我们我们如何管理接下了的一天。”

    For truck drivers, the new system took some getting used to. Instead of delivering gas from the plant closest to a customer, as they used to do, drivers were now asked to pick up shipments from whichever plant was making gas at the lowest delivered price, even if it was farther away.
    对卡车司机而言，新的工作系统还需要进一步去适应。不像以前从离客户最近的工厂向客户送气，新的系统要求司机从任何一个能将运费保持最低的制造工厂提货，甚至那个工厂路途遥远。

    "You want me to drive a hundred miles? To the drivers, it wasn't intuitive," Harper says. But for the company, the savings have been impressive. "It's huge. It's actually huge."
    “你要我驾车一百英里去提货吗？对司机来说，这不是直观的想法”，哈普尔先生说道。但是对公司来说，节省的费用一直都很不平常。“节省了很大的成本，确实很大。”

    Other companies also have profited by imitating ants. In Italy and Switzerland, fleets of trucks carrying milk and dairy products, heating oil, and groceries all use ant-foraging rules to find the best routes for deliveries. In England and France, telephone companies have made calls go through faster on their networks by programming messages to deposit virtual pheromones at switching stations, just as ants leave signals for other ants to show them the best trails.
    其他的许多公司在模仿蚂蚁时也都受益匪浅。在意大利与瑞士，运输牛奶与乳制品、民用燃料油与日用杂货的车队都采用了蚂蚁觅食法则，以找到送货的最佳行程。 在英格兰与法国，电话公司为了让电话在他们的网络上更快捷，他们也通过对信息编程以在中转站上存储虚拟信息素，就像觅食蚁为其它蚂蚁留下标记，以让它们找 到最佳的踪迹。

    In the U.S., Southwest Airlines has tested an ant-based model to improve service at Sky Harbor International Airport in Phoenix. With about 200 aircraft a day taking off and landing on two runways and using gates at three concourses, the company wanted to make sure that each plane got in and out as quickly as possible, even if it arrived early or late.
    在美国，西南航空公司已经测试了一种基于蚂蚁准则的模式，以提高在菲尼克斯市空港国际机场的服务质量。这个机场每天都有大约二百驾飞机在两条跑道起飞与降 落，并使用三个中央大厅的登机口，西南航班因此想要保证每辆飞机都能尽可能快地降落并起飞，甚至是在飞机早到或晚点地情况下。

    "People don't like being only 500 yards away from a gate and having to sit out there until another aircraft leaves," says Doug Lawson of Southwest. So Lawson created a computer model of the airport, giving each aircraft the ability to remember how long it took to get into and away from each gate. Then he set the model in motion to simulate a day's activity.
    “人们并不喜欢待在离登机口五百码的地方，并在那里等到另一辆飞机起飞”，西南航空公司的道格•罗逊（Doug Lawson）讲道。因此，罗逊为机场创建了一种计算机模式，这样每辆飞机就有了一种特殊的能力，即它们能够记住进入登机口与离开登机口所需要的时间。接 着，罗逊先生为这个模式的运转进行了设定，以模拟一天的活动。

    "The planes are like ants searching for the best gate," he says. But rather than leaving virtual pheromones along the way, each aircraft remembers the faster gates and forgets the slower ones. After many simulations, using real data to vary arrival and departure times, each plane learned how to avoid an intolerable wait on the tarmac. Southwest was so pleased with the outcome, it may use a similar model to study the ticket counter area.
    “飞机就像寻找最佳登机口的蚂蚁”，罗逊先生讲道。但是该模式并不是把虚拟信息素放在路上，而是每辆飞机记住速度最快的登机口并忘掉登记速度稍慢的登机 口。在进行了许多次的模拟之后，他们使用了真实的数据进行了测试，如使用各种飞机到达时间与起飞时间，在这次的试验中，飞机学会了如何在停机坪避免难耐的 等待。西南航空公司对最终试验结果很是满意，他们可能还会将同类似的模式运用于机票售票区。

    WHEN IT COMES TO SWARM intelligence, ants aren't the only insects with something useful to teach us. On a small, breezy island off the southern coast of Maine, Thomas Seeley, a biologist at Cornell University, has been looking into the uncanny ability of honeybees to make good decisions. With as many as 50,000 workers in a single hive, honeybees have evolved ways to work through individual differences of opinion to do what's best for the colony. If only people could be as effective in boardrooms, church committees, and town meetings, Seeley says, we could avoid problems making decisions in our own lives.
    涉及到群体智能，蚂蚁并不是唯一一种让人类受启发的昆虫。在缅因州南海岸的一个微风四起的小岛上，来自康奈尔大学的生物学家托马斯•斯利（Thomas Seeley）一直在研究根据蜜蜂离奇的能力来做出决定。一个蜂窝里能容上五万之多的工蜂，蜜蜂也发展了一种在单个蜜蜂存在意见分歧下为蜂群谋最大利益的 方法。如果人类可以像在会议室、教堂委员会与城镇会议中一样有效地行动的话，那么我们就能在自己的生活中避免犯决策性的错误。

    During the past decade, Seeley, Kirk Visscher of the University of California, Riverside, and others have been studying colonies of honeybees (Apis mellifera) to see how they choose a new home. In late spring, when a hive gets too crowded, a colony normally splits, and the queen, some drones, and about half the workers fly a short distance to cluster on a tree branch. There the bees bivouac while a small percentage of them go searching for new real estate. Ideally, the site will be a cavity in a tree, well off the ground, with a small entrance hole facing south, and lots of room inside for brood and honey. Once a colony selects a site, it usually won't move again, so it has to make the right choice.
    在过去的十年里，斯利博士、里弗赛德加州大学的科克•维斯奇尔与其它人员一直在研究各种蜂群（学名Apis mellifera）的行为，以观察它们如何选择一个新的蜂巢。晚春时分，一个蜂巢总是会非常拥挤，通常情况下蜂群就会分裂；蜂后、雄蜂与几乎一半的工蜂 便会短距离地分行并簇拥于一个树枝。蜜蜂群在那里露宿，而一小部分的蜜蜂会出外寻找新的扎根地点。从理想的角度讲，新家的地点将在一棵树的内部，离地面要 有一定的距离，蜂巢座北朝南，蜂巢里也要有大量可以生育并储存蜂蜜的空间。一旦一个蜂群选择了一个地点，蜂群便不再移动，因此必须做出非常正确的决定。

    To find out how, Seeley's team applied paint dots and tiny plastic tags to identify all 4,000 bees in each of several small swarms that they ferried to Appledore Island, home of the Shoals Marine Laboratory. There, in a series of experiments, they released each swarm to locate nest boxes they'd placed on one side of the half-mile-long (one kilometer) island, which has plenty of shrubs but almost no trees or other places for nests.
    为了发现蜂群如何做出决定，斯利博士的科研组使用了油漆的点与小巧的塑料标签，以标识他们带到浅滩海洋实验室（Shoals Marine Laboratory）驻地阿普尔多尔岛（Appledore Island）的几个小蜂群的所有四千只蜜蜂。他们在那里进行了一系列的试验，他们将每个蜂群都放飞，这样蜜蜂就可以找到他们在小岛一端半英里长的地方布 置的蜂巢盒子，在那个地方有茂密的灌木丛，但几乎没有数也没有其它可作为蜂巢的地方。

    In one test they put out five nest boxes, four that weren't quite big enough and one that was just about perfect. Scout bees soon appeared at all five. When they returned to the swarm, each performed a waggle dance urging other scouts to go have a look. (These dances include a code giving directions to a box's location.) The strength of each dance reflected the scout's enthusiasm for the site. After a while, dozens of scouts were dancing their little feet off, some for one site, some for another, and a small cloud of bees was buzzing around each box.
    在一次试验中，他们布置了五个蜂巢盒，四个是不够大的，只有一个近乎完美。侦察蜂很快就在这五个蜂巢盒上出现了。当它们返回自己的蜂群时，每个侦察蜂都表 演了一段来回摆动的舞蹈，表示要求其它侦察蜂也去看看。（这些舞蹈包括了指明蜂巢盒地点方向的代码。）每次舞蹈强劲有力，这表明了侦察蜂对蜂巢盒的热望。 过了一会儿，数十只侦察蜂便开始疯狂地跳起舞来，有的是表示倾向一个地点的蜂巢盒，有的却倾向另一个地方的，然而在那里的每一个盒子的附近都出现了大群的 蜜蜂，嗡声满天。

    The decisive moment didn't take place in the main cluster of bees, but out at the boxes, where scouts were building up. As soon as the number of scouts visible near the entrance to a box reached about 15—a threshold confirmed by other experiments—the bees at that box sensed that a quorum had been reached, and they returned to the swarm with the news.
    决定的时刻并没有发生在主要的蜂群里，而是那些巡逻蜂数量增加的盒子。在蜂巢盒入口处的巡逻蜂的可见数量一达到十五只（其它试验也能证明的上限值），这些在盒子附近的巡逻蜂就感觉到“法定人数”已经具备，接着它们就将这个消息带回给了蜂群。

    "It was a race," Seeley says. "Which site was going to build up 15 bees first?"
    “这就是个竞赛”，斯利先生讲道，“那么哪个地点的盒子首先达到了十五只巡逻蜂？”

    Scouts from the chosen box then spread through the swarm, signaling that it was time to move. Once all the bees had warmed up, they lifted off for their new home, which, to no one's surprise, turned out to be the best of the five boxes.
    接着，从被选定的蜂巢盒飞回的巡逻蜂就会在蜂群传递这个决定，暗示着大家是迁移的时候了。一旦所有的蜜蜂准备就绪，它们就会起飞直抵新家，而正如意料之中，它们选择了五个盒子中最好的那一个。

    The bees' rules for decision-making—seek a diversity of options, encourage a free competition among ideas, and use an effective mechanism to narrow choices—so impressed Seeley that he now uses them at Cornell as chairman of his department.
    蜜蜂做出决策的法则是先寻找到多种可选择的食物，然后激励起大家在不同想法中进行自由竞争，最终使用一种有效的办法来使选择最小化。这种法则让斯利先生印象深刻，以致于现在以部门主席的身份在康奈尔大学使用着这种法则。

    "I've applied what I've learned from the bees to run faculty meetings," he says. To avoid going into a meeting with his mind made up, hearing only what he wants to hear, and pressuring people to conform, Seeley asks his group to identify all the possibilities, kick their ideas around for a while, then vote by secret ballot. "It's exactly what the swarm bees do, which gives a group time to let the best ideas emerge and win. People are usually quite amenable to that."
    “我将我从蜜蜂身上学到的知识运用于开展教员会议”，他讲道。为了避免在开会之前就有了决定，或只愿听到自己想听的东西，甚至给参会人施压以得到服从，现 在斯利先生要求参会人员识别出所有的可能性，随意提出自己的想法或意见，然后通过秘密投票来得出结果。“这是蜂群具体所做的事情，这样让所有人都有时间从 而得出最好的想法并赢得成功。人们通常对这种做法会积极地响应。”

    In fact, almost any group that follows the bees' rules will make itself smarter, says James Surowiecki, author of The Wisdom of Crowds. "The analogy is really quite powerful. The bees are predicting which nest site will be best, and humans can do the same thing, even in the face of exceptionally complex decisions." Investors in the stock market, scientists on a research project, even kids at a county fair guessing the number of beans in a jar can be smart groups, he says, if their members are diverse, independent minded, and use a mechanism such as voting, auctioning, or averaging to reach a collective decision.
    事实上，几乎任何遵循蜜蜂法则的团体都将让自己更加明智，《大众智慧》的作者詹姆斯•苏洛维尔奇（James Surowiecki）讲道。“这种类推法则真的非常强而有力。蜜蜂预测着哪个蜂巢地点最合适，那人类也能够做到这样，甚至面临着极为复杂的决定。”他接 着讲道，股票市场上的投资者们、从事研究项目的科学家们，甚至是在郡集市上猜测罐里豆子数量的小孩都可以做出正确的集体决策—如果他们的成员数量足够多、 思想独立，并使用诸如投票、审核与求平均值之类的办法的话。

    Take bettors at a horse race. Why are they so accurate at predicting the outcome of a race? At the moment the horses leave the starting gate, the odds posted on the pari-mutuel board, which are calculated from all bets put down, almost always predict the race's outcome: Horses with the lowest odds normally finish first, those with second lowest odds finish second, and so on. The reason, Surowiecki says, is that pari-mutuel betting is a nearly perfect machine for tapping into the wisdom of the crowd.
    拿马赛中的赌博人做比方。为甚们他们在预测赛事结果时会如此精确？从各个马匹从起跑门开跑之时，胜败比值就在赛马赌金计算器板上显示出来—这是从下注的赌 金计算出来的，而这种机率几乎总是能预测出赛事的结果。比值最低的马匹通常为冠军，比值第二低的则为亚军，依此类推。苏洛维尔奇先生讲道，其中的原因是赛 马赌金计算器是一个近乎完美并能获知大众智慧的机器。

    "If you ever go to the track, you find a really diverse group, experts who spend all day perusing daily race forms, people who know something about some kinds of horses, and others who are betting at random, like the woman who only likes black horses," he says. Like bees trying to make a decision, bettors gather all kinds of information, disagree with one another, and distill their collective judgment when they place their bets.
    “如果你查其究竟，你就能发现赌博人确实是一个多样化的群体—有成天分析日常赛事本质的专家，有对某些品种的马匹较为深究的人，还有一些只是随意赌钱的 人，如有些女性只喜欢黑色马匹”，苏洛维尔奇讲道。就像蜜蜂试图做出决定，赌博者们也收集着各种信息，相互否认，当他们下注的那一刻便集合了大众的判断。

    That's why it's so rare to win on a long shot.
    那就是为什么成功的机会微乎其微。

    THERE'S A SMALL PARK near the White House in Washington, D.C., where I like to watch flocks of pigeons swirl over the traffic and trees. Sooner or later, the birds come to rest on ledges of buildings surrounding the park. Then something disrupts them, and they're off again in synchronized flight.
    在华盛顿特区的白宫附近有一个小型的公园，我喜欢在那里观看鸽群在人流与树丛上盘旋。这些鸽子迟早都会在环绕公园的建筑物的边崖上歇息。接着碰到某物干扰的时候，这些鸽子又会同时在受惊之后飞离。

    The birds don't have a leader. No pigeon is telling the others what to do. Instead, they're each paying close attention to the pigeons next to them, each bird following simple rules as they wheel across the sky. These rules add up to another kind of swarm intelligence—one that has less to do with making decisions than with precisely coordinating movement.
    这些鸽子没有领袖。没有鸽子会告诉其它鸽子该怎样去做。相反，它们任何一个都对其身边的鸽子保持着细心的注意，每只鸽子在空中盘旋时也遵循着简单的法则。这些法则集合起来便成了另一种群体智能—这种法则与做出决策没有多大联系，但正好关系到同步的行动。

    Craig Reynolds, a computer graphics researcher, was curious about what these rules might be. So in 1986 he created a deceptively simple steering program called boids. In this simulation, generic birdlike objects, or boids, were each given three instructions: 1) avoid crowding nearby boids, 2) fly in the average direction of nearby boids, and 3) stay close to nearby boids. The result, when set in motion on a computer screen, was a convincing simulation of flocking, including lifelike and unpredictable movements.
    克雷格•雷诺兹（Craig Reynolds）是一位计算机图形研究员，他非常好奇这些都是些什么法则。因此在1986年，他创建了一种叫做伯德（boids）的模拟转向程序。在这 种模拟环境下，普通似鸟的每个物体（或称为boids）会给予三个指令：第一种为避免与邻近的伯德聚集；第二种为与邻近的伯德保持一致的飞行方向；第三种 则为与邻近的伯德保持接近。当在计算机屏幕上进行运作时，结果是一个让人信服的群集模拟。

    At the time, Reynolds was looking for ways to depict animals realistically in TV shows and films. (Batman Returns in 1992 was the first movie to use his approach, portraying a swarm of bats and an army of penguins.) Today he works at Sony doing research for games, such as an algorithm that simulates in real time as many as 15,000 interacting birds, fish, or people.
    那时，雷诺兹曾寻求着真正在电视与电影上描述动物的方法。（1992年的“蝙蝠侠归来”是使用他的方法的第一部电影，电影中刻画了蝙蝠群与企鹅大军。）今天，雷诺兹在索尼从事游戏的研究，比如一种能够实时模拟一万五千鸟群、鱼群或人群的运算法则。

    By demonstrating the power of self-organizing models to mimic swarm behavior, Reynolds was also blazing the trail for robotics engineers. A team of robots that could coordinate its actions like a flock of birds could offer significant advantages over a solitary robot. Spread out over a large area, a group could function as a powerful mobile sensor net, gathering information about what's out there. If the group encountered something unexpected, it could adjust and respond quickly, even if the robots in the group weren't very sophisticated, just as ants are able to come up with various options by trial and error. If one member of the group were to break down, others could take its place. And, most important, control of the group could be decentralized, not dependent on a leader.
    雷诺兹通过证明“自我组织模式”的力量能模拟群体行为，他同样为机器人工程技术指明了道路。一组能够共同行动的机器人就像鸟群一样，它们是能够为单一机器 人提供非常有用的优势。在一个大区域里展开后，一个群体的作用如同一个强大的移动传感网，它能够收集到那里所出现的各种信息。如果意外地遭遇某些东西，它 们能够迅速地调整与反应，即使是群体里机器人的智能化程度不高，这就如同蚂蚁能够通过反复地试探与犯错误来应对各种选择一样。如果群体里一个机器人将被打 垮，那其它机器人就会来接替它的位置。并且更为重要的是，对群体机器人的控制能够通过分权来完成，而不是依赖于一个领袖。

    "In biology, if you look at groups with large numbers, there are very few examples where you have a central agent," says Vijay Kumar, a professor of mechanical engineering at the University of Pennsylvania. "Everything is very distributed: They don't all talk to each other. They act on local information. And they're all anonymous. I don't care who moves the chair, as long as somebody moves the chair. To go from one robot to multiple robots, you need all three of those ideas."
    “在生物学里，如果你去观察那些有着很多成员的群体，它们中极少有中心领导的情况”，美国宾夕法尼亚大学机械工程学教授维嘉•库马（Vijay Kumar）讲道。“一切都进行了分布：它们相互之间没有对话，它们根据局部信息行动。并且它们都是没有名称的。只要有人移动了椅子，我便不在乎是谁移动 的。从一个机器人发展到多重机器人，我们需要所有这三个构想。”

    Within five years Kumar hopes to put a networked team of robotic vehicles in the field. One purpose might be as first responders. "Let's say there's a 911 call," he says. "The fire alarm goes off. You don't want humans to respond. You want machines to respond, to tell you what's happening. Before you send firemen into a burning building, why not send in a group of robots?"
    库马希望在五年内能将一个机器人控制的网络小组应用到战场上。一个目的便是这些机械部队可能称为第一反应部队。“比方说我们收到一个911报警电话”，他 讲道，“火警警报突然拉响。你不希望人去做出反应，而是需要机器设备做出反应，以告诉你到底发生了什么事情。那么在派遣一批火警人员进入火海似的大楼之 前，为什么不派遣一组机器人进去呢？”

    Taking this idea one step further, Marco Dorigo's group in Brussels is leading a European effort to create a "swarmanoid," a group of cooperating robots with complementary abilities: "foot-bots" to transport things on the ground, "hand-bots" to climb walls and manipulate objects, and "eye-bots" to fly around, providing information to the other units.
    为了进一步贯彻这个构想，驻于布鲁塞尔的马克•多日葛团队正领导着一项欧洲科研活动以创建一个“swarmanoid”，这是一组有着互补能力的合作型机 器人。它们有在地面上快速移动搬运物品的能力，还有爬越墙壁操控目标物的能力，甚至还有四处飞行、将信息提供给其它机器人单位的能力。

    The military is eager to acquire similar capabilities. On January 20, 2004, researchers released a swarm of 66 pint-size robots into an empty office building at Fort A. P. Hill, a training center near Fredericksburg, Virginia. The mission: Find targets hidden in the building.
    军方非常期盼能获得有如此能力的装备。二零零四年一月二十日，研究人员将一个共六十六个机器人群放入了一个位于Fort A. P. Hill空荡的写字楼，这是维吉尼亚州弗雷德里克斯堡附近的一个训练中心。此次试验的任务是找到大楼里隐藏的目标。

    Zipping down the main hallway, the foot-long (0.3 meter) red robots pivoted this way and that on their three wheels, resembling nothing so much as large insects. Eight sonars on each unit helped them avoid collisions with walls and other robots. As they spread out, entering one room after another, each robot searched for objects of interest with a small, Web-style camera. When one robot encountered another, it used wireless network gear to exchange information. ("Hey, I've already explored that part of the building. Look somewhere else.")
    一英尺高的红色机器人群快速地沿着主要走廊移动着，它们靠着下面的三轮转脚移动，就像一群大型昆虫。每个机器人上装有声波导航和测距装置，这也使得它们不 会碰撞到墙壁与其它的机器人。随后他们分散开来，进入一个又一个的房间，每个机器人都通过装有的小型且快捷的摄像头搜索着有利的目标物。当一个机器人遇到 另一个机器人时，它会使用配备的无限网络装置交换信息。（“嘿，大楼的那个区域我已经搜索过了。你去到别的地方去看看吧。”）

    In the back of one room, a robot spotted something suspicious: a pink ball in an open closet (the swarm had been trained to look for anything pink). The robot froze, sending an image to its human supervisor. Soon several more robots arrived to form a perimeter around the pink intruder. Within half an hour, all six of the hidden objects had been found. The research team conducting the experiment declared the run a success. Then they started a new test.
    在一个房间的后面，一个机器人发现了可疑物—敞开的壁橱里有一个粉红色的圆球（这群机器人被训练要寻找任何粉红色的物品）。接着机器人进入静止状态，它要 把此物品的图片发送到人类管理员。很快好几个机器人也到达这个房间，并对这个粉红色入侵物进行全尺寸定位。不到半个小时，所有六个隐藏的目标物都被发现 了。进行该试验的研究小组宣布此试验完美成功。接着他们进行了一次新的测试。

    The demonstration was part of the Centibots project, an investigation to see if as many as a hundred robots could collaborate on a mission. If they could, teams of robots might someday be sent into a hostile village to flush out terrorists or locate prisoners; into an earthquake- damaged building to find victims; onto chemical-spill sites to examine hazardous waste; or along borders to watch for intruders. Military agencies such as DARPA (Defense Advanced Research Projects Agency) have funded a number of robotics programs using collaborative flocks of helicopters and fixed-wing aircraft, schools of torpedo-shaped underwater gliders, and herds of unmanned ground vehicles. But at the time, this was the largest swarm of robots ever tested.
    这个实证是中心机器人系统（Centibots）工程项目的一部分，此项目是一个调查，即研究一百个机器人是否能相互协作共同完成一个任务。如果它们有此 般能力的话，那么机器人小组可能就会在不久的将来被派遣到敌方村落以消灭恐怖主义者或定位在逃囚徒；还可能被派遣到遭至地震的大楼以找到受难者；到化学物 遗漏地点去检查分析危险的废弃物；或者派遣到边境去监控入侵者。像国防高级研究项目机构（简称DARPA）之类的军事机构就已经为许多机器人操控项目提供 了研发资金，这些项目的研究方向是直升机与固定羽翼飞机的合作群，鱼雷状的海底滑翔机群，与无人驾驶的地面车辆群。但是在当时，这些是所测试的最大型的机 器人群。

    "When we started Centibots, we were all thinking, this is a crazy idea, it's impossible to do," says Régis Vincent, a researcher at SRI International in Menlo Park, California. "Now we're looking to see if we can do it with a thousand robots."
    “当我们开启Centibots项目时，我们一致认为这是个非常疯狂的想法，并且是不可能完成的”，美国加利福尼亚州门楼公园（Menlo Park）的斯坦福研究院（SRI International）研究员雷吉斯•文森特（Régis Vincent）讲道。“现在我们却开始研究能否用一千个机器人进行相互合作来完成任务。”

    IN NATURE, OF COURSE, animals travel in even larger numbers. That's because, as members of a big group, whether it's a flock, school, or herd, individuals increase their chances of detecting predators, finding food, locating a mate, or following a migration route. For these animals, coordinating their movements with one another can be a matter of life or death.
    当然，从本质上讲，动物会以更大的数量四处迁移。那是因为，动物一个大群的（不管是羊群、鱼群还是兽群）的成员们中每个单一体都会发现强食动物、找寻食物、择偶或跟随迁移路线上自我提高自身的成功机率。对于这些动物而言，自我的行为与其它动物的相互配合是关系生死存亡。

    "It's much harder for a predator to avoid being spotted by a thousand fish than it is to avoid being spotted by one," says Daniel Grünbaum, a biologist at the University of Washington. "News that a predator is approaching spreads quickly through a school because fish sense from their neighbors that something's going on."
    “对掠食鱼来说，避免被一条鱼发现轻而易举，可要避免被一千条鱼发现就难上加难了”，华盛顿大学的生物学家丹尼尔•格朗鲍姆（Daniel Grünbaum）讲道。“有新闻讲道一条掠食鱼通常会快速将一个鱼群弄散，是因为鱼能从邻伴那里得知将要发生的事情。”

    When a predator strikes a school of fish, the group is capable of scattering in patterns that make it almost impossible to track any individual. It might explode in a flash, create a kind of moving bubble around the predator, or fracture into multiple blobs, before coming back together and swimming away.
    当一条掠食鱼攻击一个鱼群时，鱼群能够按照各种特定的方式四处逃散，这样掠食鱼几乎不可能跟踪到任何一条鱼。鱼群可能在一瞬间就四散，在掠食鱼四周制造出一种游动的泡泡，或者分裂为多重模糊的斑点，之后才会重新游到一起并游走。

    Animals on land do much the same, as Karsten Heuer, a wildlife biologist, observed in 2003, when he and his wife, Leanne Allison, followed the vast Porcupine caribou herd (Rangifer tarandus granti) for five months. Traveling more than a thousand miles (1,600 kilometers) with the animals, they documented the migration from winter range in Canada's northern Yukon Territory to calving grounds in Alaska's Arctic National Wildlife Refuge.
    陆地上的动物也有着非常相似的行为，正如野生动植物学家卡斯顿•赫乌尔（Karsten Heuer）在二零零三年所观察到的。当时他与他的妻子黎恩•埃利森（Leanne Allison）一起花了五个月的时间追踪了巨大的豪猪驯鹿群。他们跟随这些动物步行达一千多英里（一千六百公里），并记录下了这些动物从加拿大北部育空 地区的冬季迁移到美国阿拉斯加的北极国家野生动植物避难所的全过程。

    "It's difficult to describe in words, but when the herd was on the move it looked very much like a cloud shadow passing over the landscape, or a mass of dominoes toppling over at the same time and changing direction," Karsten says. "It was as though every animal knew what its neighbor was going to do, and the neighbor beside that and beside that. There was no anticipation or reaction. No cause and effect. It just was."
    “很难用言语来描述，但当兽群移动的时候，它们就好像是穿越大地的一块云层的阴影，或是这些兽群受到多米诺效应的影响，突然改变了方向”，卡斯顿讲道， “就好像每只动物都知道邻友将要做什么，邻友也知道邻友该做什么，依此类推。没有任何的预知或反应，也没有因没有果，就这样发生了。”

    One day, as the herd funneled through a gully at the tree line, Karsten and Leanne spotted a wolf creeping up. The herd responded with a classic swarm defense.
    一天，兽群正在缓慢穿越林木线的一条沟渠，卡斯顿与黎恩看到了一头狼正爬行过来。兽群的反应便是一个典型的群体防御。

    "As soon as the wolf got within a certain distance of the caribou, the herd's alertness just skyrocketed," Karsten says. "Now there was no movement. Every animal just stopped, completely vigilant and watching." A hundred yards (90 meters) closer, and the wolf crossed another threshold. "The nearest caribou turned and ran, and that response moved like a wave through the entire herd until they were all running. Reaction times shifted into another realm. Animals closest to the wolf at the back end of the herd looked like a blanket unraveling and tattering, which, from the wolf's perspective, must have been extremely confusing." The wolf chased one caribou after another, losing ground with each change of target. In the end, the herd escaped over the ridge, and the wolf was left panting and gulping snow.
    “当狼走到离驯鹿很近的地方时，兽群的警惕心猛然地增强”，卡斯顿讲道，“兽群已经不再移动，每个动物都停止不前，它们完全警醒着并观望着。”狼离驯鹿群 只有一百英尺了（九十米），接着狼又向前移动了一些距离。“离狼最近的驯鹿马上转身奔跑，那种反应就好像在整个鹿群里掀起了一阵波浪，最后整个鹿群就开始 奔跑。反应时间一下子就达到了另一个范围。离狼最近且在鹿群末端的鹿跑起来就像一张被拆碎的地毯四处而逃，而对狼来说，这种现象肯定是非常让它不知所 措。”狼追逐一只以后又追逐另一只，每次在更改目标后都失去前一个目标。最终，整个鹿群都逃离到了山脊之上，而狼却被甩在了后面气喘不止，只好大口地吞咽 积雪。

    For each caribou, the stakes couldn't have been higher, yet the herd's evasive maneuvers displayed not panic but precision. (Imagine the chaos if a hungry wolf were released into a crowd of people.) Every caribou knew when it was time to run and in which direction to go, even if it didn't know exactly why. No leader was responsible for coordinating the rest of the herd. Instead each animal was following simple rules evolved over thousands of years of wolf attacks.
    对每只驯鹿来讲，这绝对是危急时刻，然而鹿群在逃避的过程中没有引起恐慌而是极为精确地脱逃。（想象一下，如果一头饿狼被放入到人群之中，那将会引起什么 样的恐慌。）每只驯鹿都知道什么时候该跑以及往哪个方向跑，甚至它们并不知道它们为什么要那样。没有处于领导地位的驯鹿会去负责安排剩下的鹿群该往何处 跑。相反，每只动物都是在遵循数千年在对此饿狼攻击的过程中所培养出来的简单法则。

    That's the wonderful appeal of swarm intelligence. Whether we're talking about ants, bees, pigeons, or caribou, the ingredients of smart group behavior—decentralized control, response to local cues, simple rules of thumb—add up to a shrewd strategy to cope with complexity.
    那就是群体智慧的非凡吸引力。不管我们是在谈论蚂蚁、蜜蜂、鸽子或驯鹿，这些聪明群体的种种行为—分权控制、根据局部情况做出反应与自身经验的简单法则—这些都为人类提供了解决复杂问题的精明策略。

    "We don't even know yet what else we can do with this," says Eric Bonabeau, a complexity theorist and the chief scientist at Icosystem Corporation in Cambridge, Massachusetts. "We're not used to solving decentralized problems in a decentralized way. We can't control an emergent phenomenon like traffic by putting stop signs and lights everywhere. But the idea of shaping traffic as a self-organizing system, that's very exciting."
    “我们并不知道还有什么动物能为我们给予启发”，剑桥的艾柯系统公司（Icosystem）里一位复杂性理论家与首席科学家艾里克•玻拿鲍（Eric Bonabeau）讲道，“我们并不习惯使用分权的方式解决分权型的问题。我们无法通过到处放置停车指示牌与指示灯来控制诸如交通之类紧急事件，但是将交 通看作为一个自我组织的系统，这是激奋人心的。”

    Social and political groups have already adopted crude swarm tactics. During mass protests eight years ago in Seattle, anti-globalization activists used mobile communications devices to spread news quickly about police movements, turning an otherwise unruly crowd into a "smart mob" that was able to disperse and re-form like a school of fish.
    现在许多社会与政治团体已经采取了最原始的群体战术。在八年前出现在西雅图的群众抗议中，反全球化的活动家们使用了移动交流装置很快散布了警方将采取强硬措施的消息，这样就使得难驾驭的人群成为了一个“智慧群体”，并能够随时分散并重新组合，就像鱼群那样。

    The biggest changes may be on the Internet. Consider the way Google uses group smarts to find what you're looking for. When you type in a search query, Google surveys billions of Web pages on its index servers to identify the most relevant ones. It then ranks them by the number of pages that link to them, counting links as votes (the most popular sites get weighted votes, since they're more likely to be reliable). The pages that receive the most votes are listed first in the search results. In this way, Google says, it "uses the collective intelligence of the Web to determine a page's importance."
    最大的变化可能出现在英特网上。我们来看看谷歌网络公司是如何利用群体智慧来找到用户所寻找的东西。当你在搜索框里敲入特定字符时，谷歌就会在其索引服务 器上的数十亿网页上进行搜索，以识别出最相近的内容。接着它会根据与搜索字符相衔接的页面数量进行相关网页的排序，衔接数就相当于投票（那些最热门的网站 都进行重要性投票，因为它们将更加可靠）。而那些得到最多投票的网页将会被列于搜索结果之首。谷歌公司讲道，通过这种方式，他们“利用了万维网的集体智慧 来决定一个网页的重要性。”

    Wikipedia, a free collaborative encyclopedia, has also proved to be a big success, with millions of articles in more than 200 languages about everything under the sun, each of which can be contributed by anyone or edited by anyone. "It's now possible for huge numbers of people to think together in ways we never imagined a few decades ago," says Thomas Malone of MIT's new Center for Collective Intelligence. "No single person knows everything that's needed to deal with problems we face as a society, such as health care or climate change, but collectively we know far more than we've been able to tap so far."
    一个称为“维基百科”的免费合作型的百科全书同样也证明了是一个大的成功。维基百科有着用二百多种语言解释的数百万文章，天底下的一切都包含于此，任何用 户都可以对任何一个主题给出自己的解释或任何解释都可被任何人编辑。“现在无数人都可能能够用几十年前不可能的方式一起进行思考”，麻省理工学院新建集体 智慧研究中心的托马斯•马隆（Thomas Malone）说道，“没有一个人能知道我们解决社会问题所需要的所有知识，如健康保险与气候变化问题，但是我们作为一个集体，我们却比目前我们能够完成 的事情懂得更多。”

    Such thoughts underline an important truth about collective intelligence: Crowds tend to be wise only if individual members act responsibly and make their own decisions. A group won't be smart if its members imitate one another, slavishly follow fads, or wait for someone to tell them what to do. When a group is being intelligent, whether it's made up of ants or attorneys, it relies on its members to do their own part. For those of us who sometimes wonder if it's really worth recycling that extra bottle to lighten our impact on the planet, the bottom line is that our actions matter, even if we don't see how.
    这些思想都强调了一个关于群体智慧的重要真理，即只有在个体成员行动负责并能做出自己的决定的条件下，群体才能更具智慧。如果一个群体的成员只是互相模 仿，不知所谓地追求时尚，或总是等着别人告诉他如何行动的话，那么这个群体就不可能是智慧的群体。一个智慧的群体，不管是有蚂蚁或律师组成，它都依赖着群 体中的成员完成其自己的责任。我们中有些人时而会思考回收额外玻璃瓶来减轻其对地球的影响力是否值得，然而关键是即使我们不知道如何做，但我们只要行动了 就一定能成功。

    Think about a honeybee as she walks around inside the hive. If a cold wind hits the hive, she'll shiver to generate heat and, in the process, help to warm the nearby brood. She has no idea that hundreds of workers in other parts of the hive are doing the same thing at the same time to the benefit of the next generation.
    让我们思考一下：如果一只蜜蜂正在蜂巢里四处行走，突然一阵寒风打到了蜂巢，这只蜜蜂就会浑身颤抖来发出更多的热量，并在这个过程中为邻近的新孵出的蜜蜂带来温暖。而这只蜜蜂并不知道在蜂巢另一端的成百上千的工蜂们也同时在这样行动，以造福于下一代。

    "A honeybee never sees the big picture any more than you or I do," says Thomas Seeley, the bee expert. "None of us knows what society as a whole needs, but we look around and say, oh, they need someone to volunteer at school, or mow the church lawn, or help in a political campaign."
    “一只蜜蜂永远不会像人类这样通了大局”，蜜蜂专家托马斯•斯利讲道，“我们没有人知道整个社会需要什么，但是你向周围看一下便多少有所了解，哦，学校需要有人做义工，或需要有人义务到教堂的草坪去铲草，或政治游说活动需要人帮助。”

    If you're looking for a role model in a world of complexity, you could do worse than to imitate a bee.
    如果你在这个复杂的世界里寻找一个模范人物，那么你就该学学蜜蜂。

    [ 本帖最后由 jerrywhitt 于 2007-8-13 11:07 编辑 ]

    随机文章：


    收藏到：Del.icio.us




    引用地址：
    发表于16:59 | 编辑 | 继续话题 | 转发 | 分享　0

    评论

  • njduhkfregf rhjbkk jhgbl cniygfby grgy cfygo78go ggg g gyg8ggf8r7tgyu8g yrtyg6i ytyigb ygrggfyrgig
    123321 | 发表于2008-01-12 08:06:17 [回复]
  • 〈Swarm Behavior 群体行动〉一文非常引人入胜，讲述了全新的的思维理念，美中不足的是翻译的太蹩脚了。
    Cong Qilun | 发表于2007-08-15 17:06:03 [回复]