Level 5 Reading 8A

Reading 8A

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THE GENIUS OF SWARMS A single ant or bee isn't smart, but ant and bee colonies are. The reason, as writer Peter Miller discovers, is something called swarm intelligence. I used to think ants knew what they were doing. The ones marching across my kitchen counter looked so confident. I figured they had a coherent plan, knew where they were going, and what needed to be done. How else could ants create highways, build elaborate nests, organize epic raids, and do all the other things ants do? It 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. 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. "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. They do it with something called swarm intelligence. 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? One key to an ant colony is that no one s in charge. No generals command ant warriors, no managers boss around ant workers, and the queen plays no role except to lay eggs. Even with half a million ants, a colony operates as a single entity without any management at all— at least none that we would recognize. Such a system—described by scientists as self-organizing— relies upon countless interactions between individual ants, each of which is following simple rules. That's how swarm intelligence works: simple creatures following simple protocols, each one acting on local information. No ant sees the big picture, or tells any other ant what to do. Some ant species may go about this with more sophistication than others.

蜂群的天才 一只蚂蚁或蜜蜂并不聪明，但蚂蚁和蜜蜂的群体是聪明的。 正如作家彼得·米勒所发现的那样，原因是一种叫做群体智能的东西。 我过去认为蚂蚁知道自己在做什么。 走过我厨房柜台的那些人看起来很自信。 我认为他们有一个连贯的计划，知道他们要去哪里，需要做什么。 否则，蚂蚁怎么能修建公路，建造精致的巢穴，组织史诗般的突袭， 蚂蚁做的其他事情呢？ 原来我错了。 蚂蚁毕竟不是聪明的小工程师、建筑师或战士——至少不是个体。 当谈到决定下一步做什么时，大多数蚂蚁都不知道。 “如果你看到一只蚂蚁试图完成某事，你会对它的无能印象深刻” 斯坦福大学生物学家Deborah M.Gordon说。 那么，我们如何解释地球上约12000种已知蚂蚁的成功呢？ 他们一定在1.4亿年里学到了一些东西。 戈登说：“蚂蚁不聪明。”。“蚁群是。” 蚁群可以解决单个蚂蚁无法想象的问题， 例如找到到达最佳食物来源的最短路径， 将工人分配到不同的任务中，或保卫一片领土免受邻居的侵扰。 他们用一种叫做群体智能的东西来做到这一点。 这种智慧来自哪里，这在自然界中提出了一个基本问题： 个人的简单行为如何与群体的复杂行为相加？ 蚁群的一个关键是没有人负责。 没有将军指挥蚂蚁战士，没有经理在蚂蚁工人周围发号施令， 女王除了下蛋外，什么也不做。 即使有50万只蚂蚁，一个蚁群也作为一个单一的实体运作，根本没有任何管理-- 至少没有一个是我们认识的。 这样一个系统——科学家们称之为自组织-- 依赖于个体蚂蚁之间无数的相互作用，每种相互作用都遵循简单的规则。 这就是群体智能的工作原理： 简单的生物遵循简单的协议，每个生物都根据本地信息行事。 没有蚂蚁看到全局，也没有蚂蚁告诉其他蚂蚁该做什么。 一些蚂蚁物种可能比其他蚂蚁更老练。

(Temnothorax albipennis, for example, can rate the quality of a potential nest site using multiple criteria.) But in each case, says Iain Couzin, a biologist at Oxford and Princeton Universities, no leadership is required. "Even complex behavior may be coordinated by relatively simple interactions," he says. Inspired by this idea, computer scientists have been using swarm behavior to create mathematical procedures for resolving complex human problems, such as routing trucks and scheduling airlines. In Houston, for example, a company named American Air Liquide has been using ant-based guidelines to manage a complex business problem. The company produces industrial and medical gases at about a hundred locations in the United States and delivers them to 6,000 sites, using pipelines, railcars, and 400 trucks. Air Liquide developed a computer model inspired by the foraging behavior of Argentine ants (Linepithema humile). The species, like any other ant species, deposits chemical substances called pheromones. "When these ants bring food back to the nest, they lay a pheromone trail that tells other ants to go get more food," says Charles N. Harper, who oversees the supply system at Air Liquide. "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." Air Liquide used the ant approach 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. "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." Other companies have also profited by imitating ants. In Italy and Switzerland, fleets of trucks now use ant-foraging rules to find the best routes for bulk deliveries. In England and France, telephone companies improved their network speed by having messages deposit virtual pheromones at switching stations, just as ants leave signals for other ants to show them the best trails. But ants are not the only insects with something useful to teach us. On a small island off the southern coast of Maine, biologist Thomas D.Seeley of Cornell University has been studying how colonies of honeybees (Apis mellifera) choose a new home. In late spring, when a hive gets too crowded, a colony normally splits. The queen, some drones, and about half the workers migrate a short distance to cluster on a tree branch, while a small number of scouts go searching for a new site for the colony. To find out how, Seeley's team put out five nest boxes— four that weren't quite big enough and one that was just about perfect—and released colony of bees. Scout bees soon appeared at all five boxes. When they returned to the main group, each scout 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. 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 retuned to the swarm with the news. "It was a race," Seeley says. "Which site was going to build up 15 bees first?" 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 to secure their new home. To no one's surprise, it turned out to be the best of the five boxes. 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. "I've applied what I've learned from the bees to run faculty meetings," he says. He wants to avoid going into a meeting with his mind made up, hearing only what he wants to hear, and pressuring people to conform. Therefore, Seeley asks his group to identify all the possibilities, discuss their ideas for a while, and 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." 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. That is, if their members are diverse, independent-minded, and use a mechanism such as voting, auctioning, or averaging to reach a collective, aggregate decision. That's the wonderful appeal of swarm intelligence. Whether we're talking about ants, bees, or humans, the ingredients of smart group behavior— decentralized control, response to local cues, simple rules of thumb— add up to an effective strategy to cope with complexity. Consider the way an Internet search engine like Google uses group smarts to find what you're looking for. When you type in a search query, the engine 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.

（例如，Temnothorax albipennis可以使用多种标准对潜在巢穴的质量进行评级。） 但Iain Couzin说，在每种情况下， 牛津大学和普林斯顿大学的生物学家，不需要领导。 他说：“即使是复杂的行为也可能通过相对简单的交互来协调。”。 受到这一想法的启发，计算机科学家一直在使用群体行为 创建用于解决复杂人类问题的数学程序， 例如安排卡车路线和安排航空公司。 例如，在休斯顿，一家名为美国液化空气集团的公司 一直在使用基于蚂蚁的指导方针来管理复杂的业务问题。 该公司在美国约100个地点生产工业和医用气体 并使用管道、轨道车和400辆卡车将它们运送到6000个地点。 液化空气公司开发了一个受阿根廷蚂蚁（Linepithema humile）觅食行为启发的计算机模型。 这种蚂蚁和其他蚂蚁一样，会沉积一种叫做信息素的化学物质。 “当这些蚂蚁把食物带回巢穴时， 它们留下信息素踪迹，告诉其他蚂蚁去获取更多的食物，”查尔斯·N·哈珀说， 他负责液化空气集团的供应系统。 “每次蚂蚁外出和回来，信息素的踪迹都会得到加强， 有点像你在森林里走一条小径去捡木头。 所以，我们开发了一个程序，可以发送数十亿只软件蚂蚁 找出我们卡车路线中信息素痕迹最强的地方。" 液化空气公司使用蚂蚁方法来考虑工厂调度的每一种排列， 天气和卡车路线——每天有数百万个可能的决定和结果。 每天晚上，客户需求和制造成本的预测都会被输入到模型中。 哈珀说：“即使我们有最大的电脑，也需要四个小时才能运行。”， “但每天早上六点，我们都会得到一个解决方案，说明我们将如何管理我们的一天。” 其他公司也通过模仿蚂蚁获利。 在意大利和瑞士，卡车车队现在使用蚂蚁觅食规则来寻找散装货物的最佳路线。 在英国和法国，电话公司提高了网络速度 通过让消息在交换站存储虚拟信息素， 就像蚂蚁给其他蚂蚁留下信号，让它们看到最好的足迹一样。 但蚂蚁并不是唯一能教给我们有用东西的昆虫。 在缅因州南部海岸外的一个小岛上，康奈尔大学的生物学家托马斯·D·西利 一直在研究蜜蜂群体（Apis mellifera）如何选择新家。 在春末，当蜂巢过于拥挤时，蜂群通常会分裂。 女王、一些无人机和约一半的工人迁移了很短的距离，聚集在树枝上， 而少数侦察兵则在为殖民地寻找新的地点。 为了找出原因，Seeley的团队拿出了五个巢箱-- 四只不够大，一只近乎完美，并释放了蜂群。 侦察蜂很快出现在所有五个箱子里。 当他们回到主小组时，每个侦察员都跳了一段摇摆舞，催促其他侦察员去看看。 这些舞蹈包括一个指示盒子位置的代码； 每支舞的力度都反映了侦察员对场地的热情。 决定性的时刻不是发生在主要的蜜蜂群中，而是发生在盒子里， 侦察兵正在那里集结。 当盒子入口附近可见的侦察兵数量达到约15人时-- 其他实验证实的阈值-- 那个包厢里的蜜蜂感觉到已经达到了法定人数，于是带着这个消息回到蜂群里。 “这是一场比赛，”Seeley说。 “哪个网站会先建立15只蜜蜂？” 然后，来自选定盒子的侦察兵在蜂群中散开，发出行动的信号。 等所有的蜜蜂都暖和起来，它们就出发去保护它们的新家。 不出所料，这是五个盒子中最好的。 蜜蜂的决策规则-- 寻求多样化的选择，鼓励思想之间的自由竞争， 并使用有效的机制来缩小选择范围-- 西利印象深刻，现在他在康奈尔大学担任系主任。 他说：“我把从蜜蜂身上学到的东西应用到了召开教师会议上。”。 他想避免下定决心参加会议， 只听他想听的，并迫使人们顺从。 因此，Seeley要求他的团队确定所有的可能性， 讨论他们的想法一段时间，然后进行无记名投票。 “这正是蜂群所做的，它给了一个团队时间，让最好的想法出现并获胜。” 事实上，几乎任何遵循蜜蜂规则的群体都会变得更聪明， 《人群的智慧》一书的作者詹姆斯·索罗维奇说。 “这个类比真的很有力： 蜜蜂正在预测哪个巢穴位置最好，人类也可以做同样的事情， 即使面对异常复杂的决策。" 股票市场的投资者，研究项目的科学家， 他说，即使是在县集市上猜罐子里豆子的数量的孩子，也可能是聪明的群体。 也就是说，如果他们的成员是多元化的、思想独立的， 并使用投票、拍卖或平均等机制来达成集体、汇总决策。 这就是群体智能的魅力所在。 无论我们谈论的是蚂蚁、蜜蜂还是人类，聪明群体行为的要素-- 分散控制、对局部线索的反应、简单的经验法则-- 加起来就是应对复杂性的有效策略。 想想像谷歌这样的互联网搜索引擎是如何利用群体智能来找到你要找的东西的。 当您键入搜索查询时， 该引擎调查其索引服务器上的数十亿个网页，以确定最相关的网页。 然后，它根据链接到它们的页面数量对它们进行排名，将链接视为投票。

The most popular sites get weighted votes since they're more likely to be reliable, and the pages that receive the most votes are listed first in the search results. With free collaborative encyclopedias available to anyone online, "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 the Massachusetts Institute of Technology (MIT) 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." Such thoughts underline an important truth about collective intelligence: Crowds are wise only if individual members act responsibly and make their own decisions. A group won't be smart if its members imitate one another, unthinkingly 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 its really worth recycling that extra bottle to lighten our impact on the planet, the fact is that our actions matter, even if we don't see how. "A honey bee never sees the big picture anymore than you or I do," says Thomas D. 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." If you're looking for a role model in a world of complexity, you could do worse than to imitate an ant or a bee.

最受欢迎的网站会获得加权投票，因为它们更有可能是可靠的， 获得最多投票的页面在搜索结果中排名第一。 任何人都可以在网上免费使用协作百科全书， “现在，大量的人可以以几十年前我们从未想象过的方式共同思考，” 麻省理工学院集体智能中心的托马斯·马龙说。 “没有一个人知道处理我们作为一个社会面临的问题所需的一切， 例如医疗保健或气候变化，但总的来说，我们所知道的远远超过我们迄今为止所能利用的。" 这些想法突显了集体智慧的一个重要事实： 只有当个体成员负责任地行事并做出自己的决定时，群体才是明智的。 如果一个团体的成员互相模仿，这个团体就不会聪明， 不假思索地追随潮流，或者等待别人告诉他们该做什么。 当一个群体变得聪明时，无论是蚂蚁还是律师， 它依靠其成员各司其职。 对于我们这些有时想知道是否真的值得回收多余的瓶子来减轻我们对地球的影响的人来说， 事实是，我们的行为很重要，即使我们不知道如何做到。 “蜜蜂永远不会像你我那样看到大局。” 蜜蜂专家托马斯·D·西利说。 “我们都不知道整个社会需要什么，但我们环顾四周，说：， 哦，他们需要有人在学校做志愿者，或者修剪教堂的草坪， 或者在政治竞选中提供帮助。" 如果你正在复杂的世界中寻找一个榜样， 你可以做得比模仿蚂蚁或蜜蜂更糟糕。

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