突然 想到一些经典台词 as a kind of growing-up.......

 

从现在开始，你只许疼我一个人，要宠我，不能骗我，答应我的每一件事情都要做到，对我讲的每一句话都要真心，不许欺负我骂我，要相信我。别人欺负我，你要在第一时间出来帮我,我开心呢，你要陪着我开心，我不开心呢，你要哄我开心。永远都要觉得我是最漂亮的，梦里面呢也要见到我，在你的心里面只有我!

 

如果有一天我走了，你会象马达那样找我吗？
会呀。
会一直找我吗？
会呀。
会一直找到死吗？
会呀。
你撒谎。

 

 

 

 

 

 

 

 

 

　　   非线性，俗称“蝴蝶效应 ”

　　   蝴蝶效应是气象学家洛伦兹1963年提出来的。

　　   其大意为：美国麻省理工学院气象学家洛伦兹（Lorenz）的发现谈起。为了预报天气，他用计算机求解仿真地球大气的13个方程式，意图是利用计算机的高速运算来提高长期天气预报的准确性。1963年的一次试验中，为了更细致地考察结果，他把一个中间解0.506取出，提高精度到0.506127再送回。而当他到咖啡馆喝了杯咖啡以后回来再看时竟大吃一惊：本来很小的差异，结果却偏离了十万八千里！再次验算发现计算机并没有毛病，洛伦兹（Lorenz）发现，由于误差会以指数形式增长，在这种情况下，一个微小的误差随着不断推移造成了巨大的后果。他于是认定这为：“对初始值的极端不稳定性”，即：“混沌 ”，又称“蝴蝶效应”，亚洲蝴蝶拍拍翅膀，将使美洲几个月后出现比狂风还厉害的龙卷风！

　　   其原因在于：蝴蝶翅膀的运动，导致其身边的空气系统发生变化，并引起微弱气流的产生，而微弱气流的产生又会引起它四周空气或其他系统产生相应的变化，由此引起连锁反应，最终导致其他系统的极大变化。

　　   此效应说明，事物发展的结果，对初始条件具有极为敏感的依赖性，初始条件的极小偏差，将会引起结果的极大差异。

　　   “蝴蝶效应”在社会学界用来说明：一个坏的微小的机制，如果不加以及时地引导、调节，会给社会带来非常大的危害，戏称为“龙卷风”或“风暴”；一个好的微小的机制，只要正确指引，经过一段时间的努力，将会产生轰动效应，或称为“革命”。

　　   线性，指量与量之间按比例、成直线的关系，在空间和时间上代表规则和光滑的运动；而非线性则指不按比例、不成直线的关系，代表不规则的运动和突变。如问：两个眼睛的视敏度是一个眼睛的几倍？很容易想到的是两倍，可实际是 6－10倍！这就是非线性：1＋1不等于2。

　　   激光的生成就是非线性的！当外加电压较小时，激光器犹如普通电灯，光向四面八方散射；而当外加电压达到某一定值时，会突然出现一种全新现象：受激原子好象听到“向右看齐”的命令，发射出相位和方向都一致的单色光，就是激光。

　　   非线性的特点是：横断各个专业，渗透各个领域，几乎可以说是：“无处不在时时有。”

　　   如：天体运动存在混沌；电、光与声波的振荡，会突陷混沌；地磁场在400万年间，方向突变16次，也是由于混沌。甚至人类自己，原来都是非线性的：与传统的想法相反，健康人的脑电图和心脏跳动并不是规则的，而是混沌的，混沌正是生命力的表现，混沌系统对外界的刺激反应，比非混沌系统快。

　　   由此可见，非线性就在我们身边，躲也躲不掉了。

　　   1979年12月，洛伦兹（Lorenz）在华盛顿的美国科学促进会的一次讲演中提出：一只蝴蝶在巴西扇动翅膀，有可能会在美国的德克萨斯引起一场龙卷风。他的演讲和结论给人们留下了极其深刻的印象。从此以后，所谓“蝴蝶效应”之说就不胫而走，名声远扬了。

　　   “蝴蝶效应”之所以令人着迷、令人激动、发人深省，不但在于其大胆的想象力和迷人的美学色彩，更在于其深刻的科学内涵和内在的哲学魅力。

　　   科学家给混沌下的定义是：混沌是指发生在确定性系统中的貌似随机的不规则运动，一个确定性理论描述的系统，其行为却表现为不确定性一不可重复、不可预测，这就是混沌现象。进一步研究表明，混沌是非线性动力系统的固有特性，是非线性系统普遍存在的现象。牛顿确定性理论能够完美处理的多为线性系统，而线性系统大多是由非线性系统简化来的。因此，在现实生活和实际工程技术问题中，混沌是无处不在的。 洛伦茨第一次发现混沌现象，至今，关于混沌的研究一直是科学家、社会学家、人文学家所关注的。研究混沌，其实就是发现无序中的有序，但今天的世界仍存在着太多的无法预测，混沌，这个话题也必将成为全人类性的问题。在此，由于知识有限，我们只是做了极其肤浅的介绍和引入，希望有更多的同学能走进混沌之门，以更深邃的眼光来审视这个世界。今后或许能致力于此方面的研究。

　　   总之，混沌规律只能洞察、揣摩、直觉、推测，而不能揭示、推演和精确描述。我们可以用在西方流传的一首民谣对此作形象的说明。

　　   这首民谣说： 

　　   丢失一个钉子，坏了一只蹄铁；
　　   坏了一只蹄铁，折了一匹战马；
　　   折了一匹战马，伤了一位骑士；
　　   伤了一位骑士，输了一场战斗；
　　   输了一场战斗，亡了一个帝国。

　　   马蹄铁上一个钉子是否会丢失，本是初始条件的十分微小的变化，但其“长期”效应却是一个帝国存与亡的根本差别。这就是军事和政治领域中的所谓“蝴蝶效应”。

　　   有点不可思议，但是确实能够造成这样的恶果。一个明智的领导人一定要防微杜渐，看似一些极微小的事情却有可能造成集体内部的分崩离析，那时岂不是悔之晚矣？ 横过深谷的吊桥，常从一根细线拴个小石头开始。

 

        蝴蝶效应是混沌学理论中的一个概念。它是指对初始条件敏感性的一种依赖现象:输入端微小的差别会迅速放大到输出端...蝴蝶效应在经济生活中比比皆是:中国宣布发射导弹,港台100亿美元流向美国.
       “蝴蝶效应”也可称“台球效应”，它是“混沌性系统”对初值极为敏感的形象化术语，也是非线性系统在一定条件（可称为“临界性条件”或“阈值条件”）出现混沌现象的直接原因．

        蝴蝶效应来源于美国气象学家洛仑兹60年代初的发现．在《混沌学传奇》与《分形论——奇异性探索》等书中皆有这样的描述：“1961年冬季的一天，洛仑兹（E．Lorenz）在皇家麦克比型计算机上进行关于天气预报的计算．为了考察一个很长的序列，他走了一条捷径，没有令计算机从头运行，而是从中途开始．他把上次的输出直接打入作为计算的初值，然后他穿过大厅下楼，去喝咖啡．一小时后，他回来时发生了出乎意料的事，他发现天气变化同上一次的模式迅速偏离，在短时间内，相似性完全消失了．进一步的计算表明，输入的细微差异可能很快成为输出的巨大差别．这种现象被称为对初始条件的敏感依赖性．在气象预报中，称为‘蝴蝶效应’．……”“洛仑兹最初使用的是海鸥效应．”“洛仑兹1979年12月29日在华盛顿的美国科学促进会的演讲：‘可预言性：一只蝴蝶在巴西扇动翅膀会在得克萨斯引起龙卷风吗？’”

        某地上空一只小小的蝴蝶扇动翅膀而扰动了空气，长时间后可能导致遥远的彼地发生一场暴风雨，以此比喻长时期大范围天气预报往往因一点点微小的因素造成难以预测的严重后果．微小的偏差是难以避免的，从而使长期天气预报具有不可预测性或不准确性．这如同打台球、下棋及其他人类活动，往往“差之毫厘，失之千里”、“一着不慎，满盘皆输”．

        长时期大范围天气预报是对于地球大气这个复杂系统进行观测计算与分析判断，它受到地球大气温度、湿度、压强诸多随时随地变化的因素的影响与制约，可想其综合效果的预测是难以精确无误的、蝴蝶效应是在所必然的．我们人类研究的对象还涉及到其他复杂系统（包括“自然体系”与“社会体系”），其内部也是诸多因素交相制约错综复杂，其“相应的蝴蝶效应”也是在所必然的．“今天的蝴蝶效应”或者“广义的蝴蝶效应”已不限于当初洛仑兹的蝴蝶效应仅对天气预报而言，而是一切复杂系统对初值极为敏感性的代名词或同义语，其含义是：对于一切复杂系统，在一定的“阈值条件”下，其长时期大范围的未来行为，对初始条件数值的微小变动或偏差极为敏感，即初值稍有变动或偏差，将导致未来前景的巨大差异，这往往是难以预测的或者说带有一定的随机性．

        所谓复杂系统，是指非线性系统且在临界性条件下呈现混沌现象或混沌性行为的系统．非线性系统的动力学方程中含有非线性项，它是非线性系统内部多因素交叉耦合作用机制的数学描述．正是由于这种“诸多因素的交叉耦合作用机制”，才导致复杂系统的初值敏感性即蝴蝶效应，才导致复杂系统呈现混沌性行为．

        目前，非线性学及混沌学的研究方兴未艾，这标志人类对自然与社会现象的认识正在向更为深入复杂的阶段过渡与进化．

从贬义的角度看，蝴蝶效应往往给人一种对未来行为不可预测的危机感，但从褒义的角度看，蝴蝶效应使我们有可能“慎之毫厘，得之千里”，从而可能“驾驭混沌”并能以小的代价换得未来的巨大“福果”．

 

        一只蝴蝶在巴西煽动翅膀，有可能在美国的得克萨斯洲引起一场龙卷风。

　　   当我再次读到《混沌学》中的这句话时不禁想起了人的命运。究竟是什么因素左右了我们的未来？是不是每个人生命中都有类似的“蝴蝶效应”？

　　    一个微不足道的动作，或许会改变人的一生，这绝不是夸大其辞，可以作为佐证的事例随手便能拈来，美国福特公司名扬天下，不仅使美国汽车产业在世界占居熬头，而且改变了整个美国的国民经济状况，谁又能想到该奇迹的创造者福特当初进入公司的“敲门砖”竟是“捡废纸”这个简单的动作？

　　    那时候福特刚从大学毕业，他到一家汽车公司应聘，一同应聘的几个人学历都比他高，在其他人面试时，福特感到没有希望了。当他敲门走进董事长办公室时，发现门口地上有一张纸，很自然地弯腰把他捡了起来，看了看，原来是一张废纸，就顺手把它扔进了垃圾篓。董事长对这一切都看在眼里。福特刚说了一句话：“我是来应聘的福特”。董事长就发出了邀请：“很好，很好，福特先生，你已经被我们录用了。”这个让福特感到惊异的决定，实际上源于他那个不经意的动作。从此以后，福特开始了他的辉煌之路，直到把公司改名，让福特汽车闻名全世界。

　　   平安保险公司的一个业务员也有与福特相似的惊喜。他多次拜访一家公司的总经理，而最终能够签单的原因，仅仅是他在去总经理办公室的路上，随手捡起了地上的一张废纸并扔进了了垃圾桶。总经理对他说：“我（透过窗户玻璃）观察了一个上午，看看哪个员工会把废纸捡起来，没有想到是你。”而在这次见面总经理之前，他还被“晾”了3个多小时，并且有多家同行在竞争这个大客户。

　　   福特和业务员的收获看似偶然，实则必然，他们下意识的动作出自一种习惯，而习惯的养成来源于他们的积极态度，这正如著名心理学家、哲学家威廉•詹姆士所说：“播下一个行动，你将收获一种习惯；播下一种习惯，你将收获一种性格；播下一种性格，你将收获一种命运。”

　　   事实上，被科学家用来形象说明混沌理论的“蝴蝶效应”，也存在于我们的人生历程中：一次大胆的尝试，一个灿烂的微笑，一个习惯性的动作，一种积极的态度和真诚的服务，都可以出发生命中意想不到的起点，它能带来的远远不止于一点点喜悦和表面上的报酬。

　　   管理启示：

　　   今天的企业，其命运同样受“蝴蝶效应”的影响，因为消费者越来越相信感觉，品牌消费、购物环境、服务态度……这些无形的价值都成为他们选择的因素。所以，只要稍加留意，我们不难看到一些管理规范、运作良好的公司在理念中出现这样的句子：

　　   “在你的统计中，对待100名客户里，只有一位不满意，因此你可骄称只有1%的不合格，但对于该客户而言，他得到的却是100%的不满意。“

　　   “你一朝对客户不善，公司需要10倍甚至更多的努力去补救。”

　　   “在客户眼里，你代表公司”

　　    今天，能够让企业命运发生改变的“蝴蝶”已远不止“计划之手”，随着中国联通加入电信竞争，私营企业承包铁路专列、南京市外资企业参与公交车竞争等新闻的出现，企业坐而无忧的垄断地位日渐势微，开放式的竞争让企业不得不考虑各种影响发展的潜在因素。

　    　精简机构、官员下岗、取消福利房等措施，让越来越多的人远离传统的保障，随之而来的是靠自己决定命运。而组织和个人自由组合的结果就是：谁能捕捉到对生命有益的“蝴蝶”，谁就不会被社会抛弃。 

 

 

英文名：The Butterfly Effect
导演/编剧：埃里克·布雷斯 Eric Bress
　　       J·麦凯伊·格鲁伯 J. Mackye Gruber

主演：艾什顿·库奇 Ashton Kutcher 饰 埃文·泰瑞博
　    艾米·斯马特 Amy Smart 饰 凯勒·米勒
　　  凯文·施密特 Kevin Schmidt 饰 少年兰尼
　　  米罗娜·沃尔特斯 Melora Walters 饰 安德里亚·泰瑞博

类型：剧情/科幻/惊悚
发行：新线New Line Cinema
上映日期：2004年1月23日

 

简介

　 男主角埃文·泰瑞博(艾什顿·库奇饰)是一个平平无奇的大学生，唯一和普通人不同的是从童年时代起，就写日记不停记录他每日生活中的全部细节。某天，埃文忽然读到了那些记录中的一部分，顿时，那些已经被他自己埋葬在内心最深处许多年的黑暗记忆又再次被唤醒，那是改变了他整个少年时代的不堪回首往事。机缘巧合，埃文忽然发现自己可以通过一直搁在床下那些写着当年记录的日记本回到过去，进入自己当年的身体。也许这些落满灰尘的日记本可以让他从此摆脱所有不愉快的记忆，抱着这样的想法，埃文回到过去，力图改写历史，以为这样就可以治愈他受伤的记忆，让他和所爱的人们能从此之后幸福生活。他制定出无懈可击计划，执行起来也小心翼翼。但等他一旦回到现实，却发现一切都已面目全非。他的行为已经造成了损失惨重的改变，而他最亲密的那些朋友的生活已经南辕北辙。特别是他的初恋女友凯勒·米勒(艾米·斯马特饰)，他们是儿时玩伴，在经历了长久的漠然以对之后，发现彼此还是相爱。为了弥补自己的错误，埃文只好一次次的回到过去，但每次总有些小事件在他不注意时层出不穷地发生，之后一连串连锁反应，到底让他和他朋友们的生活更加彻头彻尾的改变。于是埃文一次次尝试，他们的生活也就像高速火车一般刹那间从山顶冲下，树林或者河流在窗外一掠而过。凯勒从女招待到学生会主席再到落魄吸毒者。她的命运和他一样不停改变。

　　据说《蝴蝶效应》的结局有两个：

　　一个是导演加长版的结局：

　　埃文看到的家庭电影是埃文的母亲即将产下埃文，进入历史的埃文决定自己结束这一切，他用双手掐住了脐带，结束了自己刚要开始的生命，现实的生活中没有埃文，凯莉跟汤米被离婚后的母亲监护，远离了她那个有着变态嗜好的父亲，自然也就没有了以后的各种事件。

　　另一个是剧场版的结局：埃文看到的家庭电影是第一次认识凯莉的聚会，回到从前的埃文骂了凯莉，他与凯莉没有成为好朋友，凯莉跟汤米的监护权也由母亲得到，工作后的埃文在街上偶遇凯莉，但却没有相认。最后凯丽和埃文擦肩而过的情节很独特，他们好象有默契的回头张望，而又放弃般的回过头去踏上各自的道路。结尾的似曾相识的迷离结束在oasis的《Stop Crying Your Heart Out》的歌声中久久回响。

 

片名：Chaos
译名：乱战/混乱作战
导演：Tony Giglio 托尼·基格里奥
编剧：Tony Giglio 托尼·基格里奥
主演：瑞安·菲利普 Ryan Phillippe
　　　韦斯利·斯奈普斯 Wesley Snipes
　　　亨利·彻尼 Henry Czerny
　　　杰森·斯坦森 Jason Statham
类型：动作/犯罪/悬疑
片长：98分钟
发行：中影集团/创世星
上映日期：2007年3月9日(内地)

简介

　　西雅图的某个早晨，5名歹徒闯入一家银行劫持了在场的近四十名职员和顾客。尽管他们表现得训练有素，经验老道，但百密一疏，仍被一位银行职员偷偷按响了警报器。警察迅速包围街区后，劫匪的头目洛伦兹要求和相识的警官昆丁•康纳斯对话。康纳斯曾在一次办案过程中直接导致洛伦兹兄弟的死亡，而现在他正因一次严重失误处于停职阶段。在警方的要求下，正在度假的康纳斯勉强接受了上级的任务，和刚踏入警届的新手德克成了搭档。就在银行遭劫的现场，康纳斯凭借丰富的办案经验，很快分析出了警匪双方所面临的严峻形势。于是，在已有一名人质死亡的情况下，他决定无论如何都要进入银行，采取相关措施，才能避免更大的伤亡危险。然而，狡猾的劫匪显然是有备而来，开始折磨人质以阻止警察的秘密靠近，并且在警察试图强攻时， 引爆了他们预先设置的强力炸弹。顿时，银行内外陷入了一片火海之中，受惊的人们四散逃窜。康纳斯他们开始意识到，一场比他们想象中更为激烈的战斗拉开了序幕，正与邪的较量正式开始…

　　科纳斯抓获了在这个劫匪并在他的家中发现了5万元现金，鉴定却发现这笔钱来自警局的证物室。此时匪首劳伦斯居然胆大包天的直接打电话对科纳斯挑衅说，要是明天还抓不到他的话就永远没机会了。

　　科纳斯查出了警局的内鬼，并查出顺藤摸瓜将主嫌围堵在其家中，不幸的是一场爆炸夺去了科纳斯的生命。科纳斯的搭档迪克发现了劳伦斯的行踪并单枪匹马将其毙于抢下，为科纳斯报了仇。当即将结案前，迪克突然又发现了新的线索：10亿美元在抢劫银行的过程中被劫匪神不知鬼不觉地从银行内汇了出去，而劳伦斯已将所有的线索都带入了地下，此时，一个熟悉的声音在迪克电话里传来…

---------------------------------

 

看<Chaos>的时候 我就想到了 蝴蝶效应 没想到 网上一搜索 竟然发现 原来 蝴蝶效应 就是 混沌学理论中的一个例子

 

网上 又看到 这么一段

       就像一位混沌学家说的：It's not the chances that matter, it's the pattern. 一只蝴蝶是可以改变很多，但那是在除了这只蝴蝶其他万物都不运动的前提下。万物都在运动，某一个运动并不能决定未来，而是群体在概率学角度上的总体运动趋势，或者说总体运动方式，pattern, 这才是决定未来的因素。当这个群体的数量越大，该群体就越会具有某种有规律的总体运动趋势。群体中的每个个体的运动方式并不一样，毫无规律，而且会对未来产生影响，但是各个个体的运动合起来成了群体运动，群体运动的方式是可以有规律的，因此他们对未来的影响也就有可能预见了。

 

 

 

深奥额 继续研究ING......

 

 

 

另贴 另外一篇 英文的

 

 

------------------------------------

 

Chaos Theory: A Brief Introduction

==========================
What exactly is chaos? The name "chaos theory" comes from the fact that the systems that the theory describes are apparently disordered, but chaos theory is really about finding the underlying order in apparently random data.

When was chaos first discovered? The first true experimenter in chaos was a meteorologist, named Edward Lorenz. In 1960, he was working on the problem of weather prediction. He had a computer set up, with a set of twelve equations to model the weather. It didn't predict the weather itself. However this computer program did theoretically predict what the weather might be.

 

One day in 1961, he wanted to see a particular sequence again. To save time, he started in the middle of the sequence, instead of the beginning. He entered the number off his printout and left to let it run.

 

When he came back an hour later, the sequence had evolved differently. Instead of the same pattern as before, it diverged from the pattern, ending up wildly different from the original. (See figure 1.) Eventually he figured out what happened. The computer stored the numbers to six decimal places in its memory. To save paper, he only had it print out three decimal places. In the original sequence, the number was .506127, and he had only typed the first three digits, .506. 

 
By all conventional ideas of the time, it should have worked. He should have gotten a sequence very close to the original sequence. A scientist considers himself lucky if he can get measurements with accuracy to three decimal places. Surely the fourth and fifth, impossible to measure using reasonable methods, can't have a huge effect on the outcome of the experiment. Lorenz proved this idea wrong.

This effect came to be known as the butterfly effect. The amount of difference in the starting points of the two curves is so small that it is comparable to a butterfly flapping its wings.

The flapping of a single butterfly's wing today produces a tiny change in the state of the atmosphere. Over a period of time, what the atmosphere actually does diverges from what it would have done. So, in a month's time, a tornado that would have devastated the Indonesian coast doesn't happen. Or maybe one that wasn't going to happen, does. (Ian Stewart, Does God Play Dice? The Mathematics of Chaos, pg. 141)

 

This phenomenon, common to chaos theory, is also known as sensitive dependence on initial conditions. Just a small change in the initial conditions can drastically change the long-term behavior of a system. Such a small amount of difference in a measurement might be considered experimental noise, background noise, or an inaccuracy of the equipment. Such things are impossible to avoid in even the most isolated lab. With a starting number of 2, the final result can be entirely different from the same system with a starting value of 2.000001. It is simply impossible to achieve this level of accuracy - just try and measure something to the nearest millionth of an inch!

 

From this idea, Lorenz stated that it is impossible to predict the weather accurately. However, this discovery led Lorenz on to other aspects of what eventually came to be known as chaos theory.

 

Lorenz started to look for a simpler system that had sensitive dependence on initial conditions. His first discovery had twelve equations, and he wanted a much more simple version that still had this attribute. He took the equations for convection, and stripped them down, making them unrealistically simple. The system no longer had anything to do with convection, but it did have sensitive dependence on its initial conditions, and there were only three equations this time. Later, it was discovered that his equations precisely described a water wheel.

At the top, water drips steadily into containers hanging on the wheel's rim. Each container drips steadily from a small hole. If the stream of water is slow, the top containers never fill fast enough to overcome friction, but if the stream is faster, the weight starts to turn the wheel. The rotation might become continuous. Or if the stream is so fast that the heavy containers swing all the way around the bottom and up the other side, the wheel might then slow, stop, and reverse its rotation, turning first one way and then the other. (James Gleick, Chaos - Making a New Science, pg. 29)

 

The equations for this system also seemed to give rise to entirely random behavior. However, when he graphed it, a surprising thing happened. The output always stayed on a curve, a double spiral. There were only two kinds of order previously known: a steady state, in which the variables never change, and periodic behavior, in which the system goes into a loop, repeating itself indefinitely. Lorenz's equations were definitely ordered - they always followed a spiral. They never settled down to a single point, but since they never repeated the same thing, they weren't periodic either. He called the image he got when he graphed the equations the Lorenz attractor. (See figure 2)

 

In 1963, Lorenz published a paper describing what he had discovered. He included the unpredictability of the weather, and discussed the types of equations that caused this type of behavior. Unfortunately, the only journal he was able to publish in was a meteorological journal, because he was a meteorologist, not a mathematician or a physicist. As a result, Lorenz's discoveries weren't acknowledged until years later, when they were rediscovered by others. Lorenz had discovered something revolutionary; now he had to wait for someone to discover him.

 

Another system in which sensitive dependence on initial conditions is evident is the flip of a coin. There are two variables in a flipping coin: how soon it hits the ground, and how fast it is flipping. Theoretically, it should be possible to control these variables entirely and control how the coin will end up. In practice, it is impossible to control exactly how fast the coin flips and how high it flips. It is possible to put the variables into a certain range, but it is impossible to control it enough to know the final results of the coin toss.

 

A similar problem occurs in ecology, and the prediction of biological populations. The equation would be simple if population just rises indefinitely, but the effect of predators and a limited food supply make this equation incorrect. The simplest equation that takes this into account is the following:

next year's population = r * this year's population * (1 - this year's population)
In this equation, the population is a number between 0 and 1, where 1 represents the maximum possible population and 0 represents extinction. R is the growth rate. The question was, how does this parameter affect the equation? The obvious answer is that a high growth rate means that the population will settle down at a high population, while a low growth rate means that the population will settle down to a low number. This trend is true for some growth rates, but not for every one.

 

One biologist, Robert May, decided to see what would happen to the equation as the growth rate value changes. At low values of the growth rate, the population would settle down to a single number. For instance, if the growth rate value is 2.7, the population will settle down to .6292. As the growth rate increased, the final population would increase as well. Then, something weird happened.  As soon as the growth rate passed 3, the line broke in two. Instead of settling down to a single population, it would jump between two different populations. It would be one value for one year, go to another value the next year, then repeat the cycle forever. Raising the growth rate a little more caused it to jump between four different values. As the parameter rose further, the line bifurcated (doubled) again. The bifurcations came faster and faster until suddenly, chaos appeared. Past a certain growth rate, it becomes impossible to predict the behavior of the equation. However, upon closer inspection, it is possible to see white strips. Looking closer at these strips reveals little windows of order, where the equation goes through the bifurcations again before returning to chaos. This self-similarity, the fact that the graph has an exact copy of itself hidden deep inside, came to be an important aspect of chaos.

 

An employee of IBM, Benoit Mandelbrot was a mathematician studying this self-similarity. One of the areas he was studying was cotton price fluctuations. No matter how the data on cotton prices was analyzed, the results did not fit the normal distribution. Mandelbrot eventually obtained all of the available data on cotton prices, dating back to 1900. When he analyzed the data with IBM's computers, he noticed an astonishing fact:

The numbers that produced aberrations from the point of view of normal distribution produced symmetry from the point of view of scaling. Each particular price change was random and unpredictable. But the sequence of changes was independent on scale: curves for daily price changes and monthly price changes matched perfectly. Incredibly, analyzed Mandelbrot's way, the degree of variation had remained constant over a tumultuous sixty-year period that saw two World Wars and a depression. (James Gleick, Chaos - Making a New Science, pg. 86)

 

Mandelbrot analyzed not only cotton prices, but many other phenomena as well. At one point, he was wondering about the length of a coastline. A map of a coastline will show many bays. However, measuring the length of a coastline off a map will miss minor bays that were too small to show on the map. Likewise, walking along the coastline misses microscopic bays in between grains of sand. No matter how much a coastline is magnified, there will be more bays visible if it is magnified more.

 

One mathematician, Helge von Koch, captured this idea in a mathematical construction called the Koch curve. To create a Koch curve, imagine an equilateral triangle. To the middle third of each side, add another equilateral triangle.  Keep on adding new triangles to the middle part of each side, and the result is a Koch curve. (See figure 4) A magnification of the Koch curve looks exactly the same as the original. It is another self-similar figure.

 

The Koch curve brings up an interesting paradox. Each time new triangles are added to the figure, the length of the line gets longer. However, the inner area of the Koch curve remains less than the area of a circle drawn around the original triangle. Essentially, it is a line of infinite length surrounding a finite area.

 

To get around this difficulty, mathematicians invented fractal dimensions. Fractal comes from the word fractional. The fractal dimension of the Koch curve is somewhere around 1.26. A fractional dimension is impossible to conceive, but it does make sense. The Koch curve is rougher than a smooth curve or line, which has one dimension. Since it is rougher and more crinkly, it is better at taking up space. However, it's not as good at filling up space as a square with two dimensions is, since it doesn't really have any area. So it makes sense that the dimension of the Koch curve is somewhere in between the two.

 

Fractal has come to mean any image that displays the attribute of self-similarity. The bifurcation diagram of the population equation is fractal. The Lorenz Attractor is fractal. The Koch curve is fractal.

 

During this time, scientists found it very difficult to get work published about chaos. Since they had not yet shown the relevance to real-world situations, most scientists did not think the results of experiments in chaos were important. As a result, even though chaos is a mathematical phenomenon, most of the research into chaos was done by people in other areas, such as meteorology and ecology. The field of chaos sprouted up as a hobby for scientists working on problems that maybe had something to do with it.

 

Later, a scientist by the name of Feigenbaum was looking at the bifurcation diagram again. He was looking at how fast the bifurcations come. He discovered that they come at a constant rate. He calculated it as 4.669. In other words, he discovered the exact scale at which it was self-similar. Make the diagram 4.669 times smaller, and it looks like the next region of bifurcations. He decided to look at other equations to see if it was possible to determine a scaling factor for them as well. Much to his surprise, the scaling factor was exactly the same. Not only was this complicated equation displaying regularity, the regularity was exactly the same as a much simpler equation. He tried many other functions, and they all produced the same scaling factor, 4.669.

 

This was a revolutionary discovery. He had found that a whole class of mathematical functions behaved in the same, predictable way. This universality would help other scientists easily analyze chaotic equations. Universality gave scientists the first tools to analyze a chaotic system. Now they could use a simple equation to predict the outcome of a more complex equation.

 

Many scientists were exploring equations that created fractal equations. The most famous fractal image is also one of the most simple. It is known as the Mandelbrot set (pictures of the mandelbrot set). The equation is simple: z=z2+c. To see if a point is part of the Mandelbrot set, just take a complex number z. Square it, then add the original number. Square the result, then add the original number. Repeat that ad infinitum, and if the number keeps on going up to infinity, it is not part of the Mandelbrot set. If it stays down below a certain level, it is part of the Mandelbrot set. The Mandelbrot set is the innermost section of the picture, and each different shade of gray represents how far out that particular point is. One interesting feature of the Mandelbrot set is that the circular humps match up to the bifurcation graph. The Mandelbrot fractal has the same self-similarity seen in the other equations. In fact, zooming in deep enough on a Mandelbrot fractal will eventually reveal an exact replica of the Mandelbrot set, perfect in every detail.

 

Fractal structures have been noticed in many real-world areas, as well as in mathematician's minds. Blood vessels branching out further and further, the branches of a tree, the internal structure of the lungs, graphs of stock market data, and many other real-world systems all have something in common: they are all self-similar.

 

Scientists at UC Santa Cruz found chaos in a dripping water faucet. By recording a dripping faucet and recording the periods of time, they discovered that at a certain flow velocity, the dripping no longer occurred at even times. When they graphed the data, they found that the dripping did indeed follow a pattern.

 

The human heart also has a chaotic pattern. The time between beats does not remain constant; it depends on how much activity a person is doing, among other things. Under certain conditions, the heartbeat can speed up. Under different conditions, the heart beats erratically. It might even be called a chaotic heartbeat. The analysis of a heartbeat can help medical researchers find ways to put an abnormal heartbeat back into a steady state, instead of uncontrolled chaos.

 

Researchers discovered a simple set of three equations that graphed a fern. This started a new idea - perhaps DNA encodes not exactly where the leaves grow, but a formula that controls their distribution. DNA, even though it holds an amazing amount of data, could not hold all of the data necessary to determine where every cell of the human body goes. However, by using fractal formulas to control how the blood vessels branch out and the nerve fibers get created, DNA has more than enough information. It has even been speculated that the brain itself might be organized somehow according to the laws of chaos.

 

Chaos even has applications outside of science. Computer art has become more realistic through the use of chaos and fractals. Now, with a simple formula, a computer can create a beautiful, and realistic tree. Instead of following a regular pattern, the bark of a tree can be created according to a formula that almost, but not quite, repeats itself.

 

Music can be created using fractals as well. Using the Lorenz attractor, Diana S. Dabby, a graduate student in electrical engineering at the Massachusetts Institute of Technology, has created variations of musical themes. ("Bach to Chaos: Chaotic Variations on a Classical Theme", Science News, Dec. 24, 1994) By associating the musical notes of a piece of music like Bach's Prelude in C with the x coordinates of the Lorenz attractor, and running a computer program, she has created variations of the theme of the song. Most musicians who hear the new sounds believe that the variations are very musical and creative.

 

Chaos has already had a lasting effect on science, yet there is much still left to be discovered. Many scientists believe that twentieth century science will be known for only three theories: relativity, quantum mechanics, and chaos. Aspects of chaos show up everywhere around the world, from the currents of the ocean and the flow of blood through fractal blood vessels to the branches of trees and the effects of turbulence. Chaos has inescapably become part of modern science. As chaos changed from a little-known theory to a full science of its own, it has received widespread publicity. Chaos theory has changed the direction of science: in the eyes of the general public, physics is no longer simply the study of subatomic particles in a billion-dollar particle accelerator, but the study of chaotic systems and how they work.