系统理论(Systemtheorie)

系统理论是上个世纪50年代兴起的一门跨学科理论,它的本质是一种认识模型(Erkenntnismodell)。所以又属于super theory的范畴,就是说它提供了一个对这个世界认识的基本思路。

可以追溯到生物学家Ludwig von Bertalanffy,他主要结合了控制论(Cybernetics)(罗伯特 维纳Norbert Wiener, William Ross Ashby为代表人物),和信息学(Informatics)(申农Claude Shannon, Warren Weaver)的一些基本理论,创立了一般性系统理论。在此基础上衍生出了大量的子系统学科和理论,比如自生系统(Autopoiesis),自组织系统(Selbstorganisation),社会学系统理论(Sozialsystemtheorie)(创始人Niklas Luhmann),结构耗散理论(disipative structure),协同学(Synergetik),混沌(chaos system),分形(fractal)等等。

系统理论的研究对象就是系统和系统的复杂性。通过理论模型的建立分析系统的复杂性,预测系统的走向。

[ 本帖最后由 fussfun 于 2007-3-24 19:13 编辑 ]
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系统论参考书目

Dirk Baecker 是 社会学系统理论创始人Niklas Luhmann的学生,也是当今德国仅有的几个社会学系统理论家之一。

下面是Dirk Baecker教授在维藤大学系统理论SeminarReader:
斜体是Thema,接着是作者,书名,出版usw.


————————————————————————————————————————

Einführung:

Niklas Luhmann,
Soziale Systeme: Grundriss einer allgemeinen Theorie.
Frankfurt am Main: Suhrkamp, 1984, Vorwort & Zur Einführung

Purpose:

Arturo Rosenblueth, Norbert Wiener, und Julian Bigelow,
Behavior, Purpose and Teleology
In: Philosophy of Science 10 (1943), S.18-24

Complexity:

Warren Weaver, Science and Complexity,
In: American Scientist 36(1948)
S. 536-544

Communication
Norbert Wiener,
Cybernetics, or Control and communication in the Animal and the Machine. 2. Aufl.,Vambridge, Mass,: MIT Pr.1961, Introduction und Kap.1

Information

Claude E. Shannon,
The Mathematical Theory of Communication, in: ders. Und Warren Weaver, The Mathematical Theory of Communicaton. Urbana, III: Illinois UP, 1963,29-125

Control
W.Ross Ashby,
Requisite Variety and Its Implicaitons for the Control of Complex Systems, in: Cybernetica 1 (1958), S 83-99

Mind

Gregory Bateson,
Form, Substance, and Difference, in: ders., Steps to an Ecology of Mind. Reprint: Chicago: Chicago UP,2000, S.454-471

Construction

Heinz von Foerster,
Über das Konstruieren von Wirklichkeiten, in: ders., Wissen und Gewissen: Versuch einer Brücke. Frankfurt am Main.

Autopoiesis

Humberto R. Maturana, Autopoiesis
In: Milan Zeleny Atuopoiesis: A Theory of Living Organizations. New York: Northholland, 1981, S21-32

Black Box

Ranulph Glanville,
Inside Every White Box There Are Two Black Boxes Trying To Get Out, in: Behavioral Science 27 (1982), S.1-11

Non-Trivial Machine

Heinz von Foerster,
Prinzipien der Selbstorganisation im sozialen und betriebswirtschaftlichen Bereich, in: ders., Wissen und Gewissen: Versuch einer Brücke. Frankfurt am Mai: Surhkamp,1993, S.233-268

Distinction

Geprge Spencer-Brown,
Gesetze der Form.Dt.Lübeck: Bohmeier, 1997

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小结得不错——某台湾网站摘抄的

Luhmann的社會系統理論--摘譯與整理

什麼是Luhmann的社會系統理論?

還是用Luhmann自己的嘴巴說吧!不過當然是經過我打字的手了。接下來我
打算先從一篇Luhmann的小文章開始,一九八八年他在「水星雜誌」(稍微
介紹一下,這是一本德國知識菁英寫給社會大眾看的文化月刊,Habermas
發跡前也常在上面寫作)上發表了一篇短文,介紹系統理論的(較)新發
展(N. Luhmann, Neuere Entwicklungen in der Systemtheorie, in:
Merkur 42, S. 292-300)。此時他的大部分理論架構已經接近完成,距離
巨著「社會系統」一書的出版已經四年了,可以說這是一篇他自己為自己
的理論所寫的簡介,距今雖然已經十年了,不過仍具可看性。  以下主要摘譯與整理自該篇文章:

1.系統論聞世三十年(現在四十年了)來的發展,已經使得這個概念的輪
廓模糊了,由於生物一般系統論、Parsons一般行動系統論、操控學、計畫
理論等理論的多層次快速發展,已經很難用一個共同的名稱來指稱這些學
科。值得一提的是,相對於這些領域的快速發展,社會學在五零年代與六
零年代初期的百花齊放後,卻給人家停滯不前的印象,不是一直抱著大師
的大腿不放,就是生產沒什麼理論意義的資料。 系統論是從一個區別(Unterscheidung)出發,這個區別就是「系統與環境
的區別」,
它使用這個獨特的差異(Differenz)來掌握世界,換言之,在系
統論中所有出現在面前的都被掌握了,只是此時必須給定何為系統何為環
境。系統論是一個同時具有普遍性又具有特殊性的理論程式,一方面作為
世界理論(Welttheorie)掌握所有出現的現象,另一方面卻又使用一個特定
的區分—特定的系統指涉,因為相對於特定的系統才有特定的環境。更抽
象的說,系統論是差異理論,其基礎是系統與環境的差異,無可掌握的世
界整體就解離在這個差異中。當然人們也可以從其他的區別出發,從善∕惡
或是男∕女的區別出發,然而這麼作的同時,人們就建構不同的對象、談論
不同的事實、觀察不同的現象了。問題是藉由哪一種區分可以達到較高的複
雜性?

2.總體而言,以系統與環境的差異為對象的研究,比只以客體-系統作為
對象的研究,來的有收穫。這可以在一些例子上說明。 第一個例子Heinz von Foerster常舉的是「一般機械」 (Trivialmaschinen)
與「自我指涉機械」 (selbstreferentielle Maschinen)的區別,此處
「機械」代表的是一種轉換功能,不一定是真正的機器,也可以是大腦或
是數學運算。「一般機械」以相同的方式將輸入轉換為輸出,通常在沒有
發生錯誤的情況,相同的輸入會產生相同的輸出。「自我指涉機械」的輸
出則取決於它當時的狀態,即使是相同輸入,根據不同的內部狀態會產生
不同的輸出,可以說「自我指涉機械」是歷史機械,具有偶然的創造性。 這種區別明顯的跟系統與環境的連結方式有關,可以用Francisco Varela
建議的區分作更清楚的表達:「透過輸入∕輸出的連結」 (Kopplung durch
Input/Output)與「透過其內部操作封閉性或循環性的連結」 (Kopplung
durch Geschlossenheit oder Zirkularitat),前者很容易理解,可是後
者,或更進一步來說:「透過隔開內部操作的連結」就容易引起疑問了,
這樣的表達形式或許並不成熟,卻是理論創新的重點。 第二個例子:在腦研究的領域裡早就得知,腦與其環境幾乎沒有接觸,它
使用一種以電流為基礎的語言,在環境中並不存在這種語言的對等物,換
言之,腦的編碼與其環境無關。令人訝異的是,哲學的認識論對此並不注
意,當然於此涉及的是經驗知識而非經驗知識的先驗條件,當代的認識論
(應)使用「系統∕環境的區別」來取代「經驗∕先驗的區分」,回頭看
來,知識論的先驗主義雖然在當時是逃脫窘境的出路,然而在較新的理論
與經驗知識的灼照之下,已經是多餘的了。 其後果自然是有夠令人眩目的:所有能進行認識的系統作為實在的系統在
實在的世界裡操作(運作),這對生物體、對意識、對人類社會的溝通而
言都適用。然而其認識性操作、其觀察、其知覺卻建立在與現實解除連結
的基礎上,換言之,我們能夠認識外在世界,僅僅是因為通往外在世界的
通道堵塞了;認識並不是在系統內複製環境,而是(系統)本身的建構,
建立(系統)本身的複雜性,這種建構並不是由環境設計的,也不由其決
定,環境的作用在於擾亂(irritieren)。
可以很弔詭的說,認知系統作為
具有環境開放性的系統能夠運作,是因為(weil)而且僅當(soweit)這樣的
系統自我指涉的也就是封閉的運作著。  

3.第三個例子是「自我再製」概念的提出,其作者是智利的神經生理學
Maturana,這個概念指涉的是自我再生產的循環性。其實在六零年代就
已經在討論「自我組織」的概念,但是這個概念僅涉及系統自己建立自
己的結構,「自我再製」概念則將結構自我再生產的想法轉移到系統元
素的層面,簡單說,自我再製系統就是能夠自己生產所有維持其操作所
需單元的系統。應該說明的是,自我再製系統當然是在一個世界裡操作
,沒有這個世界它就不可能存在,而且所有它的操作都預設了在任何時
刻裡與這個世界的「結構性連結」(strukturelle Kopplung),此外這
種連結與系統本身的操作並不處在同一個層面。 可以說,生命系統的生命並不由其環境輸入,更具革命性的論點是,即
使是資訊處理系統,只要它是自我再製系統,其資訊也是系統內部的建
構而非來自其環境。這樣的理論其實就是「自我指涉封閉系統」的理論
,它與「建構主義」知識論的關係也很密切。  

4.第四個例子是George Spencer Brown的邏輯,這種邏輯算不算是邏輯並
非毫無疑義,至少可以確定的是它具有演算形式(Form eines Kalkuls)
,它處理的並非具有真假值的句子,而是進行區分(Unterscheidung)與
標示(Bezeichnung/Markierung)的「操作」
,這種邏輯裡時間扮演重要角色。
它的出發點是,為了標示必須進行區分,而且任何區分都可以,然而這麼作
時卻產生弔詭(Paradox),因為一開始所使用的區分必須自我區分卻又
不能自我區分,否則無法開始或是不成其為形式。所以這種邏輯也可以
說是為了進行處理弔詭的演算,或是視為建立一個複雜秩序的程序,在
其中讓作為起始的隱形的弔詭可以顯現,這個弔詭將會以「重複輸入」
(Re-entry)的方式顯現,亦即將起始的區分重複輸入於透過它所區分
者。這個區分會出現兩次,首先是作為使標示成為可能的形式(Form),
其次是作為形式中的形式,它既是同一個又是不同一個區分!

[ 本帖最后由 fussfun 于 2007-4-8 16:14 编辑 ]
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社会学系统理论的基础前提——世界的自我观察性

1. 世界的自我觀察:盧曼所依據的基礎就是Spencer-Brown的區別理論,所以對這個理論的討論我放在第一點。

所有的觀察都是世界的自我觀察

盧曼說,「即便物理世界也會創造出它的物理學家,以便將自己觀察為一個客體,而這個客體不同於其他任何客體。」

這個例子直接源自Spencer-Brown的理論。

他的理論核心在於,不要去看所看見的東西,而要去看「看」這個動作。

這很類似於von Foerster所說的模控學的模控學,「…如果我們向X先生展示一幅畫,

而他直呼淫穢的話,那麼關於X先生,我們知道得很多,但關於那幅畫,卻知道得很少。」
世界能夠進行「看」,也就是「觀察」,而且這個觀察,只能是對自己的觀察,不論是社會學家、化學家、還是天文學家,都只是世界的自我觀察者。不存在著世界之外的觀察者(這樣的觀察者是語義層次上的東西,例如中世紀的「上帝」),所有對世界的觀察都只能更擴大這個世界,因此世界沒有outside。如果用Spencer-Brown的符號表示,即為:
  
世界為了觀察自己,會將自己切成兩個部分,一個部分是觀察者,另一個部分是被觀察者,

換句話說,世界必須將自己與自己區別開來。

觀察者在進行觀察的時候只能看見被觀察者,而無法看見自己

亦即,觀察者無法觀察到自己正在進行觀察。如果能同時觀察到被觀察者以及進行觀察的自己,就會導致弔詭:進行觀察的自己就是進行觀察時的被觀察者,區別的兩邊被同一起來。

不同等於同,A等於非A。「世界始終部分地排除自己」;「世界是可觀察的,因為世界是不可觀察的。」這個引文中最後一句話有兩個涵義:

第一、被排除掉的自己無法被自己看見;

第二、因為第一個涵義,所以觀察者與被觀察者之間的差異無法被觀察者觀察到。這個差異的統一就是世界。

因此,觀察者在進行觀察時,原則上具有兩個盲點:觀察者自己、作為差異統一的世界。觀察者看不見自己看不見的東西,甚至,他也看不見自己看不見自己看不見的東西。von Foerster說,盲點如果要被看見,需要藉助二階治療,也就是二階觀察。

Spencer-Brown對世界自我觀察的描述,找出了這些盲點,因此他一開始便站在二階觀察的立場,而這種二階觀察的立場也使盧曼對對象的提問方式由「什麼」轉變為「如何」。
另外,關於世界還有一點必須說明,世界是無中心的

這意味著,世界在任何地方都可以是中心,都可以是切入點,換句話說,可以從任何的地方切割出一個作為世界的觀察者(計算機、細胞、組織、社會系統、哲學家、生物學家、社會學家、皇帝、恆溫器),和一個作為世界的被觀察者。觀察的面向始終是偶連的。不過這並不表示,世界可以「任意」觀察自己,這樣的說法忽略了歷史與結構的層面,並將世界視為完全的熵狀態。觀察一直都是在結構中進行的:「觀察/描述不過意味著關連到具有『限制性』前提的一組差異,也就是說,關連到位於一個其他也是有可能的區別領域中的差異。」

顯然,這還欠缺一種明晰的觀察概念。什麼叫不同的切入點、受限的切入點?什麼叫觀察?

為什麼盧曼作為世界自我觀察者,說出有系統存在,有環境存在,而不是有其他東西存在,例如石頭?這涉及到區別理論另外一個核心概念:區別。

Laws of Form的一開頭,Spencer-Brown就說:「當一個空間被切割或分開時,一個領域就可以存在。」這意味著做出區別,是事物存在的充分條件。另外,區別意味著兩件事,

一、一個空間被區別切割成兩個部分,而且只有兩個部分;

二、藉由區別,標示才得以可能,但這並不表示標示一定會出現,不過如果我們要認識事物,

就必須要進行標示:「如果沒有做出區別,我們就不能進行標示」、「讓statemark認識。」

所謂標示,就是將一個空間切割開來後,對其中一邊進行命名,而不對另外一邊進行命名。「描述、標示、命名和指令相當於同一件事。」而命名就是盧曼所說的觀察:

觀察意味著做出一個區別並標示區別的其中一邊,而不是另外一邊。這個另外一邊可以是完全unmarked的。」

命名可以使用語言,但也可以使用眼神、手勢或其他符號。

接下來,讓我把注意力拉回到世界的自我觀察。觀察者進行觀察的時候,必須將自己與被觀察者區別開來,而同時不能觀察到自己以及這個區別。

另外,觀察者使用某組區別去觀察被觀察者,因此,被觀察者中有一部份會被觀察者標示出來,另外一部份不會被標示出來,也就是不會被觀察(unmarked state)。

除此之外,還有一個不會被觀察的是這組區別本身。一個東西不能同時既是A又是非A。如果同時觀察到區別的兩邊,會使得這兩邊既不同,又相同,形成弔詭。

因此,當這組區別不被觀察時,這組區別就沉落到unmarked state那裡。

盧曼承襲Spencer-Brown的語彙,使用re-entry來表示這個弔詭的情況:一組區別如果re-enter到區別的其中一邊,也就是,在這一邊裡頭,區別重新出現,那麼,再進入的區別兩邊就會相同,也就是弔詭。

因此,如果觀察要順利進行,必須解決弔詭的問題。

解決這個問題的一般原則只有一個,就是用另外一組區別來將形成弔詭的區別展開。

對此,Spencer-Brown提示了一種解開弔詭的方法,即,時間:先/後。

利用時間,弔詭的兩邊可以先後出現,於是弔詭就被不對稱化了。

時間這組區別是歷史學家以及社會學家常用的解決之道。

總結一下,世界自我觀察時,觀察者不能觀察到的事情有幾個,

一、「被觀察者/觀察者」的統一(世界);
二、觀察者;
三、unmarked state
四、觀察者使用的區別。(Dirk Baecker形容後面兩者為兩種未規定性。)

這導出了一件很重要的事,即,觀察者無法將自己使用的區別和被觀察者那裡出現的區別加以區別,也就是說,觀察者將觀察到的東西視為「如其所是」的東西

「我們現在看見第一區別、mark、以及觀察者不僅是可以互換的,而且在形式中也是同一的。」

被觀察者是不是?“真的“具有被標示出的那些東西,觀察者無法知道,而且也無法提出這樣的追問;觀察者可以知道的是,他所觀察到的那些東西就是他所觀察到的樣子。

這裡可以用re-entry再說一次:對於對觀察(亦即,一階觀察者)進行觀察的觀察者(亦即,二階觀察者)來說,在一階觀察者那裡,「觀察者/被觀察者」這組區別re-enter到觀察者之中。這個表述方式只要將觀察者與被觀察者換成系統與環境,就成了盧曼系統理論的一般陳述:「系統/環境」這組區別re-enter到系統中,成為「自我指涉/異己指涉」這組區別,因而使觀察得以可能。

觀察使用的區別是偶連的,因此,世界的自我觀察切入點就是偶連的:盧曼利用「『系統/環境』的統一/unmarked state」這組區別將自己與世界其餘部分區別開來,並標示了「系統/環境」這組差異的統一,而不是石頭。

這組統一被當成既與,如其所是的在世界中展現,因此,既然盧曼看不到自己,那麼,整個世界就只能是系統與環境的統一。這個統一便是世界自我觀察的切入點。盧曼隱身起來了。接下來,盧曼假設系統具有觀察能力(因為系統/環境這組區別只能re-enter到系統,而非環境;而這個re-enter乃是起因於系統為了解決純粹自我指涉所導致的套套邏輯問題),因此,世界的自我觀察者就是系統。系統看不見自己,也看不見盧曼。

最後,觀察所用的命名可區分出兩個不同的東西:名字和state

這組區別是Spencer-Brown的前提。區別不只是像VarelaGlanville所說的,以markvalue為前提,也不只是像盧曼所說的,以區別/標示為前提。

區別還以另外一個區別為前提,即名字/state。這些前提全都隱而未顯,然而都是「做出一個區別」這個指令的可能性條件。名字可以被偶連地選擇,state也可以被偶連地選擇,根據Luhmann轉引自Bateson對於訊息的定義:「一“位元“的訊息可以被定義為製造差異的差異。」

由於選擇是在差異中選取一邊,因此state就是訊息;而名字,在盧曼那裡,則表現為告知。


2. 二階觀察。

觀察以兩個區別為前提,不過它們都不能被那個觀察看見,否則會出現弔詭,終止觀察。

補償之道是藉由另外一個觀察來觀察原本的觀察。這就是二階觀察。二階觀察不是什麼東西都觀察,它只觀察觀察。

它可以與一階觀察同屬一個觀察者,或是分屬不同的觀察者。
由於二階觀察對觀察進行觀察,因此無可避免地,會觀察到一階觀察者如何進行觀察,如何使用區別,如何避開弔詭。

二階觀察不只觀察到一階觀察的對象,也同時觀察到一階觀察所使用的區別兩邊,即marked state、unmarked state,而這個同時性在一階觀察那裡只會形成re-entry的弔詭。

因此,二階觀察可以知道一階觀察所使用的區別其實是一階觀察者做出來的,如果一階觀察者跨越區別的界線而到unmarked state,讓這個unmarked state變成marked state,並加以標示的話,那麼他將看到完全不一樣的東西;
甚至,他也可以在跨越區別的界線後,使用另外一組區別。
如此一來,二階觀察便能觀察到一階觀察標示的state以及使用的區別是偶連的,可以有其它的標示方式、觀察方式;

不僅如此,二階觀察也可以將一階觀察重新弔詭化,然後以功能對等項的方式,尋找、比較各種解決弔詭的方法。

在一階觀察者看來如其所是的世界,在二階觀察者的眼中,則是被區別所構成,於是,構成問題便從現象學領域移轉到二階觀察領域;
例如,盧曼站在二階觀察的立場說明了一階觀察構成客體同一性的過程:熟悉的世界不過是在時間中持續使用同樣的區別去標示同樣的一邊。然而,二階觀察也是觀察,它也具有所有一階觀察的特徵:四個盲點、對象如其所是的展現等等。它的弔詭、偶連性、世界性,可以經由另外一個觀察來發現。

3. 人是心理系統和有機系統結構耦合的結果

系統作為世界的自我觀察者,切割出自己與環境。這種切割方式有很多種,社會系統與其環境是一種,神經系統與其環境是另外一種,等等。

這一段落打算談社會系統的首要可能性條件,因此,所關乎的不是神經系統、社會系統,而是心理系統與有機系統。盧曼在談這兩個系統時,同時論題化了兩組系統環境區別,對此,盧曼使用「環境/環境中的諸系統」這組區別來加以安置:

環境不但是系統本身所不是的東西,它本身還包含了其他系統,因此,心理系統與有機系統互為對方的環境。之所以用這組區別來安置,乃是因為兩組系統指涉作為差異的兩邊,不能同時被差異的使用者觀察到,如果要被同時觀察到,只有一個辦法,就是使用另外一組差異,亦即去弔詭化。

盧曼在這裡選擇了「環境/環境中諸系統」這組差異作為弔詭問題的解決之道。接下來,我就在「心理系統與有機系統互為環境」這個層次上來談心理系統、有機系統,並在最後,將這個關係擴大到社會系統。首先來說說心理系統。

心理系統是一個自我生產的系統。系統由不可進一步分解的元素所組成,但是這些元素不如過去系統論者所主張,在同一時間內並存著,反而,盧曼在這裡使用了現象學著名的區別來定義元素:實現性/可能性。系統擁有許多元素,而且也只由自己的元素組成,但是系統一次只能實現一部份的元素,其他的元素必須作為可能性,等待以後被選取出來實現。

這意味著:1) 系統的元素只能關連到系統的元素。

每個元素都是系統底下的元素,而且每個系統都擁有(也只擁有)專屬自己的元素類型,系統不能涉足到環境中選取其他系統的元素,環境也不能跨到系統中選取系統的元素,環境始終只是系統的可能性條件而已。

在這個意義下,自我生產的「自我」指的就是元素,而自我生產指的就是元素生產元素(此即基本自我指涉)。

這導致了系統在運作上的封閉性:系統在運作時,無法接觸環境。

2) 自我生產過程需要時間。系統是複雜的,它無法「同時」將元素關連到所有元素,這必須透過時間來得到補償。

雖然每個元素都是一出現就消失,一出現就立即銜接到其他元素,不過這僅僅表示每個元素不可能一部份出現一部份消失,元素始終是一個全有全無的事態。

事實上,元素、元素與元素間的銜接都需要時間。因此複雜性就是時間化的複雜性。3) 自我生產系統是偶連的。系統擁有時間化的複雜性,這個複雜性導致了選擇(盧曼的用語是:關係化),而選擇則意味著其他的選擇也有可能,因此系統在運作層次上是偶連的。

心理系統是一個使用元素再生產自己的系統,而它的元素就是思想:思想銜接到下一個思想。

因此,心理系統也是封閉的自我指涉系統。不過如果心理系統僅只是自我指涉系統的話,那麼它將會面臨純粹套套邏輯的問題:它只關連到自己而且完完全全關連到自己。

自我指涉始終只是系統裡頭一併進行的自我指涉,因為如果系統不從環境那裡取得額外的訊息,就無法進行自我指涉:「自我指涉必須讓自己成為不對稱,以便在運作上變得有生產性。」
他者指涉(也就是訊息)始終是自我指涉的可能性條件。

這意味著套套邏輯與去套套邏輯是每個自我生產系統必須面對的問題。然而前面我已經討論過,自我生產系統是封閉的系統,它不可能與環境有所接觸,因此,訊息不會直接自環境那裡進到系統中:

環境始終必須與系統區別開來。

系統要獲得訊息,只有一種辦法,就是觀察,亦即,將「系統/環境」這組差異re-enter到系統之中。

這個re-enter進來的差異,在系統中呈現為「自我指涉/他者指涉」這組差異。

他者指涉意味著系統觀察環境時所得出的訊息(或更精確的說,觀察所建構出來的環境訊息),而這種訊息是系統視之為「如其所是」的東西。

與他者指涉區別開來的自我指涉則表現為系統的自我觀察。前面所討論的封閉性只能推導出環境訊息為系統內部的建構,而不能推導出系統自我觀察時,被觀察到的自我亦為系統內部的建構。

對此,盧曼使用另外一個策略:系統始終是遞迴的自我生產系統,亦即,元素生產出元素。

這種遞迴性會使系統的動線變得極端曲折,最後系統就無法在當下運作時得知自己過去任一時刻的真實狀態。
既然無可達及自己的過去,那麼自我描述作為對自己過去狀態的具體或抽象描述,也就無法碰觸到真正的過去。

除此之外,系統也不能得知自己的未來,因為系統元素的選擇徹底來說是依靠機率原則,而不依靠自己的意志。
機率意味著總有可能發生處於自己意料之外的事情,因此系統無法得知自己真正的未來。據此,系統對於自己來說,也是一個不透明(或封閉)的東西

然而,儘管如此,系統仍可以使用一些觀察圖式,去描述自己,去獲取關於自己的訊息,因為系統仍必須依靠訊息才能挑選出下一刻的元素。
就此看來,這個訊息仍是系統在當下的內在建構。此即為與他者指涉區別開來的自我指涉或自我觀察。這種自我觀察所取得的訊息如同環境訊息一般,被系統視為「如其所是」的東西。只有在幾個運作所觀察到的訊息互相矛盾時,這種如其所是才會受到質疑。
總之,封閉性迫使開放性出現。在心理系統那裡,「自我指涉/他者指涉」就表現為胡賽爾所說的「意識/現象」,現象即為思想所取得的訊息。

不過這裡有一點必須注意,就是心理系統在進行觀察時,不一定使用命名,它可以單純的將某物(現象)與其他東西區別開來,藉此將它直接選取出來。心理系統只有在試圖間接認識某物時,才會使用命名。
關於觀察,還有一點要說的是複雜性。

心理系統將「系統/環境」這組差異re-enter到自己之中,成為「自我指涉/他者指涉」這組差異,這意味著環境和系統對系統本身來說都是不透明的。

心理系統對此有許多回應方式,其中一個就是將環境把握為複雜難測的,亦即,系統本身缺乏能「充分把握以及描述它的環境(環境複雜性)或自己(系統複雜性)」的訊息。

這些訊息的缺乏有可能被系統稱作複雜性。很明顯,這個複雜性不是運作上的複雜性,而是系統使用的概念。

系統可以觀察到(系統內部的建構!),環境常常會發生不可預測的變化(颱風忽然轉向,直撲台灣),即便連自己,也會出現難以控制的情況(腦子裡忽然跑出一堆奇怪的想法、晚上的夢常常天馬行空、莫名的哀傷)。對於這種複雜性問題,解決之道可以是用二元圖式來加以化約,例如,將其他人看成朋友或是敵人;另外,記憶與擺盪也是彌補複雜性的方法。

因此,解決之道始終是偶連的。

心理系統之外是環境,這個環境裡頭包含了許許多多的系統,例如,其他的心理系統、有機系統,如此等等。不過,心理系統的首要環境是神經系統,如果神經系統沒有觀察到環境發生的變化的話,心理系統就無法意識到環境。

不過這兩個系統都是封閉的自我生產系統,它們都以自己特有的元素來進行運作。與環境的實際接觸始終是不可能的。另外,透過神經系統的媒介,心理系統可以觀察到身體的再生產過程(例如,發出聲音、拿起一隻筆、往右移動兩步)。

這三個系統都是自我生產的封閉系統,從結構耦合的角度來看,這三個系統藉由相互觀察環境中的對方,來調節自己元素的再生產方式(結構),

因此,在一個外部觀察者的觀察中,這些系統之間有著連結或因果關係:某甲在開車的時候,藉由神經系統的媒介,他忽然意識到一個小孩從右前方竄出,神經系統緊急「命令」身體用腳踩住煞車。

不過盧曼也談及有機系統,亦即生命系統,這個系統也是自我生產的封閉系統。心理系統與有機系統的相互結構耦合所產生的統一,就是人。

至於有機系統與身體、神經系統的關係,盧曼沒有詳談,所以我在這兒也就暫時擱置不議。

[ 本帖最后由 fussfun 于 2007-4-6 18:33 编辑 ]
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Spencer Brown's Laws of form:

其实主要就是两个定理:
1。 命名定理
2。 划叉定理(或者是标记定理)

命名定理:对于一个命名的再次命名还是这个命名

也就是说对于一个观察对象作出区分给予第一次命名后,当这个命名被再次使用的时候,仍是基于对观察对象的区分和命名,因此在这里是相等的。

交叉定理:在一次划叉内的再一次划叉等于没有划叉

也就是说对于一个观察对象作出区分给予一个标记(划叉),表示出被观察对象和其环境的界限。当作出区别,划出界限的同时,观察者无法区分他作出的这个区别和被观察者自己的区别的区别,因此如果再从该区别的内部再作区别时,实际上外在的区别又变成了unmarked space,所以再一次的划叉等于没有划叉。
也就是说,实际上从内还是从外做区别,只能选择其中一种。
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因为当观察者作观察的时候,同时 1.他无法观察到观察者自身,2.也无法观察到观察者和被观察者的区别/统一;
3.另外他观察到了一面,就不能观察到另一面(unmarked space)
,4.以及他作观察时使用的区别(这个区别决定了另外一部分unmarked space),就是说他观察的时候只能用一种区别,而不能同时使用两种区别。

所以第二个划叉定理,就是这个Paradoxie的Darstellung。
当观察者使用了一个区别,若还要再此区别上再作区别,则原来的区别消失,就是说外在的Markierung消失。

如果1,2或者3,4同时存在的话,就会产生paradoxie。如何能解决这个paradoxie,看re-entry。

[ 本帖最后由 fussfun 于 2007-4-8 17:11 编辑 ]
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系统科学中文书目

N.维纳《控制论》,郝季仁译,科学出版社,1962。
H.格林尼斯基《控制论简述》,科学出版社,1963。
W.R.艾什比《控制论导论》,张理京译,科学出版社,1965。
[奥]埃尔温·薛定谔《生命是什么?》上海人民出版社,1973。
N.维纳《人有人的用处》,陈步译,商务印书馆,1978。
冯.诺意曼《计算机和人脑》,甘子玉译,商务印书馆,1979。
W.B.坎农《躯体的智慧》,范岳年 魏有仁译,商务印书馆,1980。
[苏]А.Я.列尔涅尔《控制论基础》,刘定一译,科学出版社,1980。
涂序彦 潘华 郭江 黄秉宪 编《生物控制论》,科学出版社,1980。
奥斯卡·兰格《经济控制论导论》,杨小凯译,中国社会科学出版社,1981。
M.A.阿尔贝勃《大脑、机器和数学》,朱熹豪 金观涛译,商务印书馆,1982。
P.卡洛《生物机器--研究生命的控制论途径》,科学出版社,1982。,
湛垦华 沈小峰等编《普里高津与耗散结构理论》。,陕西科学技术出版社,1982。
金观涛 华国凡《控制论和思想方法论》,科学普及出版社,1983。
魏宏森《系统科学方法论导论》,人民出版社,1983。
金观涛 刘青峰《兴盛与危机》,湖南人民出版社,1984。
H.哈肯《协同学--引论 物理学、化学和生物学中的非平衡相变和自组织》,徐锡申等译,原子能出版社,1984。
A.F.G.汉肯《控制论和社会》,黎鸣译,商务印书馆,1984。
瓦·尼·萨多夫斯基《一般系统论原理》,贾泽林等译,人民出版社,1984。
黄麟雏 李继宗 邹珊刚《系统思想与方法》,陕西人民出版社,1984。
赫伯特·A·西蒙《关于人为事物的科学》,杨砾译,解放军出版社,1985。
[美]E.拉兹洛《用系统论的观点看世界》,闵家胤译,上国社会科学出版社,1985。
伊·普里戈金《从存在到演化》,曾庆宏等译,上海科学技术出版社,1986。
尼科利斯 普里戈京合著《非平衡系统的自组织》,徐锡申等译,科学出版社,1986。
尼柯里斯 普利高津合著《探索复杂性》,罗久星等译,四川教育出版社,1986。
王雨田主编《控制论、信息论、系统科学和哲学》,中国人民大学出版社,1986。
李如生编著《非平衡态热力学和耗散结构》,清华大学出版社,1986。
[美]拉·迈尔斯主编《系统思想》,杨志信、葛明浩译,四川人民出版社,1986。
[美]司马贺(Herbert A.Simon)《人类的认知--思维的信息加工理论》,荆其诚 张厚粲译,科学出版社,1986。
宋毅 何国祥编著《耗散结构论》,中国展望出版社,1986。
杨士尧编著《系统科学导论》,农业出版社,1986.
路·冯·贝塔朗菲《一般系统论--基础、发展、应用》,秋同、袁嘉新译,社会科学文献出版社,1987。
[美]冯·贝塔朗菲《一般系统论--基础、发展和应用》,林康义等译,清华大学文献出版社,1987。
E.拉兹洛《系统、结构和经验》,李创同译,上海译文出版社,1987。
普里戈金 斯唐热合著《从混沌到有序》,曾庆宏 沈小峰译,上海译文出版社,1987。
金观涛《整体的哲学》,四川人民出版社,1987。
邹珊刚、黄麟雏等《系统科学》,上海人民出版社,1987。
沈小峰 胡岗 姜璐 编著《耗散结构论》,上海人民出版社,1987。
杰里米·里夫金《熵:一种新的世界观》,吕明 袁舟译,上海译文出版社,1987。
H.哈肯《协同学——自然成功的奥秘》,载呜钟译,上海科学普及出版社,1988。
霍绍周编著《系统论》,科学技术文献出版社,1988。
拉兹洛《进化——广义综合理论》,社会科学文献出版社,1988。
金观涛《我的哲学探索》,上海人民出版社,1988。
勒内·托姆《突变论:思想和应用》,周仲良译,上海译文出版社,1989。
陈禹《关于系统的对话--现象、启示与探讨》,中国人民大学出版社,1989。
马清健《系统和辩证法》,求实出版社,1989。
[美]詹姆斯·格莱克《混沌--开创新科学》,张淑誉译,上海译文出版社,1990。
[英]P.切克兰德《系统论的思想与实践》,左晓斯 史然译,华夏出版社,1990。
苗东升编著《系统科学原理》,中国人民大学出版社,1990。
艾什比《大脑设计:适应性行为的起源》,乐秀成 朱熹豪等译,商务印书馆,1991。
卢侃、孙建华编绎《混沌学传奇》,上海翻译出版公司,1991。
[苏]阿诺尔德《突变理论》,陈军译,商务印书馆,1992。
埃里克·詹奇《自组织的宇宙观》,曾国屏等译,中国社会科学出版社,1992。
金观涛,刘青峰《开放中的变迁--再论中国社会超稳定结构》,香港中文大学出版社,1993。
彼得·圣吉《第五项修炼》,郭进隆译,三联书店,1994。
[加]格拉斯、麦基《从摆钟到混沌——生命的节律》,上海远东出版社,1994。
[德]福尔迈《进化认识论》,舒远招 译,武汉大学出版社,1994。
王诺《系统思想的轮回》,大连理工学院出版社,1994。
[英]伊恩·斯图尔特《上帝掷骰子吗?——混沌之数学》,上海远东出版社,1995。
吴祥兴、陈忠等编著《混沌学导论》,上海科学技术文献出版社,1996。
刘华杰《浑沌之旅》,山东教育出版社,1996。
梁美灵 王则柯《童心与发现--混沌与均衡纵横谈》,三联书店,1996。
E.N.洛伦茨《混沌的本质》,气象出版社,1997。
E.拉兹洛《决定命运的选择》,李吟波等译,三联书店,1997。
米歇尔·沃尔德罗普《复杂--诞生于秩序与混沌边缘的科学》,陈玲译,三联书店,1997。
J.布里格斯\F.D.皮特《湍鉴--浑沌理论与整体性科学导论》,刘华杰、潘涛译,商务印书馆,1998。
郑维敏《正反馈》,清华大学出版社,1998。
苗东升《系统科学靖要》,中国人民大学出版社,1998。
王颖《大系统思维论》,中国青年出版社,1998。
[奥]路德维希·冯·贝塔朗菲《生命问题——现代生物学思想评价》,吴晓江 译,商务印书馆,1999。
闵家胤《进化的多元论》,中国社会科学出版社,1999。
成思危主编《复杂性科学探索》(论文集),民主与建设出版社,1999.
克拉默《混沌与秩序——生物系统的复杂结构》,上海科技教育出版社,2000。
许国志主编《系统科学》,上海科技教育出版社,2000。
许国志主编《系统科学与工程研究》,上海科技教育出版社,2000。
黄润生编著《混沌及其应用》,武汉大学出版社,2000。
肖纪美《梳理人、事、物的纠纷--问题分析方法》,清华、暨南大学出版社,2000。
沈禄赓编著《系统科学概要》,北京广播学院出版社,2000。
张锡纯《二熵——源事理》,北京航空航天出版社,2000。
钱学森《创建系统学》,山西科学技术出版社,2001。
[美]约翰·霍兰《涌现——从混沌到有序》,上海科学技术出版社,2001。
迪亚库、霍尔姆斯《天遇--混沌与稳定性的起源》,上海科技教育出版社,2001。
大卫·吕埃勒《机遇与混沌》,上海科技教育出版社,2001。
[美]马克·布查纳《临界》,吉林人民出版社,2001。
[美]C.格里博格、J.A.约克《混沌对科学和社会的冲击》,湖南科学技术出版社,2001。
[香港]陈天机、许倬云、关子尹主编《系统视野与宇宙人生》,商务印书馆,2002年增订版
[美]欧阳莹之《复杂系统理论基础》,田宝国、周亚、樊瑛译,上海科技教育出版社,2002。
[美]约翰·C·泰勒《自然规律中蕴蓄的统一性》,暴永宁译,北京理工大学出版社,2004。
凡是真精gg的帖子一定要顶!

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一些英文经典系统论论文(在Prof.Baecker的Reader里均有收藏)

Purpose:

Arturo Rosenblueth, Norbert Wiener, und Julian Bigelow,
Behavior, Purpose and Teleology
In: Philosophy of Science 10 (1943), S.18-24





[ 本帖最后由 fussfun 于 2008-1-9 15:13 编辑 ]

Behavior, Purpose and Teleology.pdf (54.34 KB)

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Complexity:

Warren Weaver, Science and Complexity,
In: American Scientist 36(1948)
S. 536-544
http://www.ceptualinstitute.com/genre/weaver/weaver-1947b.htm

SCIENCE AND COMPLEXITY

By WARREN WEAVER
Rockefeller Foundation, New York City

"Science and Complexity", American Scientist, 36: 536 (1948).
Based upon material presented in Chapter 1' "TheScientists Speak," Boni & Gaer Inc.,1947. All rights reserved.

SCIENCE  has led to a multitude of results that affect men's lives. Some of these results are  embodied in mere conveniences of a relatively trivial sort. Many of them, based on science  and developed through technology, are essential to the machinery of modern life. Many  other results, especially those associated with the biological and medical sciences, are  of unquestioned benefit and comfort. Certain aspects of science have profoundly influenced  men's ideas and even their ideals. Still other aspects of science are thoroughly awesome.
  How can we get a view of the function that science should have in the developing future  of man? How can we appreciate what science really is and, equally important, what science  is not? It is, of course, possible to discuss the nature of science in general  philosophical terms. For some purposes such a discussion is important and necessary, but  for the present a more direct approach is desirable. Let us, as a very realistic  politician used to say, let us look at the record. Neglecting the older history of  science, we shall go back only three and a half centuries and take a broad view that tries  to see the main features, and omits minor details. Let us begin with the physical  sciences, rather than the biological, for the place of the life sciences in the  descriptive scheme will gradually become evident.
  

Problems of Simplicity

  
Speaking roughly, it may be said that the seventeenth,  eighteenth, and nineteenth centuries formed the period in which physical science learned  variables, which brought us the telephone and the radio, the automobile and the airplane,  the phonograph and the moving pictures, the turbine and the Diesel engine, and the modern  hydroelectric power plant.
  The concurrent progress in biology and medicine was also impressive, but that was of a  different character. The significant problems of living organisms are seldom those in  which one can rigidly maintain constant all but two variables. Living things are more  likely to present situations in which a half-dozen, or even several dozen quantities are  all varying simultaneously, and in subtly interconnected ways. Often they present  situations in which the essentially important quantities are either non-quantitative, or  have at any rate eluded identification or measurement up to the moment. Thus biological  and medical problems often involve the consideration of a most complexly organized whole.  It is not surprising that up to 1900 the life sciences were largely concerned with the  necessary preliminary stages in the application of the scientific method-preliminary  stages which chiefly involve collection, description, classification, and the observation  of concurrent and apparently correlated effects. They had only made the brave beginnings  of quantitative theories, and hardly even begun detailed explanations of the physical and  chemical mechanisms underlying or making up biological events.
  To sum up, physical science before 1900 was largely concerned with two-variable problems  of simplicity; whereas the life sciences, in which these  problems of simplicity are not so often significant, had not yet become highly  quantitative or analytical in character.
  
  

Problems of Disorganized Complexity

  
Subsequent to 1900 and actually earlier, if one includes  heroic pioneers such as Josiah Willard Gibbs, the physical sciences developed an attack on  nature of an essentially and dramatically new kind. Rather than study problems which  involved two variables or at most three or four, some imaginative minds went to the other  extreme, and said: "Let us develop analytical methods which can deal with two billion  variables." That is to say, the physical scientists, with the mathematicians often in  the vanguard, developed powerful techniques of probability theory and of statistical  mechanics to deal with what may he called problems of disorganized complexity.
This last phrase calls for explanation. Consider first a  simple illustration in order to get the flavor of the idea. The classical dynamics of the  nineteenth century was well suited for analyzing and predicting the motion of a single  ivory ball as it moves about on a billiard table. In fact, the relationship between  positions of the ball and the times at which it reaches these positions forms a typical  nineteenth-century problem of simplicity. One can, but with a surprising increase in  difficulty, analyze the motion of two or even of three balls on a billiard table. There  has been, in fact, considera~e study of the mechanics of the standard game of billiards.  But, as soon as one tries to analyze the motion of ten or fifteen balls on the table at  once, as in pool, the problem becomes unmanageable, not because there is any theoretical  difficulty, but just because the actual labor of dealing in specific detail with so many  variables turns out to be impracticable.
  Imagine, however, a large billiard table with millions of balls rolling over its  surface, colliding with one another and with the side rails. The great surprise is that  the problem now becomes easier, for the methods of statistical mechanics are applicable.  To be sure the detailed history of one special ball can not be traced, but certain  important questions can be answered with useful precision, such as: On the average how  many balls per second hit a given stretch of rail? On the average how far does a ball move  before it is hit by some other ball? On the average how many impacts per second does a  ball experience?
  Earlier it was stated that the new statistical methods were applicable to problems of  disorganized complexity. How does the word "disorganized" apply to the large  billiard table with the many balls? It applies hecu~ise the methods of statistical  mechanics are valid only when they are distributed, in their positions and motions, in a  helter-skelter, that is to say a disorganized, way. For example, the statistical methods  would not apply if someone were to arrange the balls in a row parallel to one side rail of  the table, and then start them all moving in precisely parallel paths perpendicular to the  row in which they stand. Then the balls would never collide with each other nor with two  of the rails, and one would not have a situation of disorganized complexity.
  From this illustration it is clear what is meant by a problem of disorganized  complexity. It is a problem in which the number of variables Is very large, and one in  which each of the many variables has a behavior which is individually erratic, or perhaps  totally unknown. However, in spite of this helter-skelter, or unknown, behavior of all the  individual variables, the system as a whole possesses certain orderly and analyzable  average properties.
  A wide range of experience comes under the label of disorganized complexity. The method  applies with increasing precision when the number of variables increases. It applies with  entirely useful precision to the experience of a large telephone exchange, in predicting  the average frequency of calls, the probability of overlapping calls of the same number,  etc. It makes possible the financial stability of a life insurance company. Although the  company can have no knowledge whatsoever concerning the approaching death of any one  individual, it has dependable knowledge of the average frequency with which deaths will  occur.
  This last point is interesting and important. Statistical techniques are not restricted  to situations where the scientific theory of the individual events is very well known, as  in the billiard example where there is a beautifully precise theory for the impact of one  ball on another. This technique can also be applied to situations, like the insurance  example, where the individual event is as shrouded in mystery as is the chain of  complicated and unpredictable events associated with the accidental death of a healthy  man.
  The examples of the telephone and insurance companies suggests a whole array of  practical applications of statistical techniques based on disorganized complexity. In a  sense they are unfortunate examples, for they tend to draw attention away from the more  fundamental use which science makes of these new techniques. The motions of the atoms  which form all matter, as well as the motions of the stars which form the universe, come  under the range of these new techniques. The fundamental laws of heredity are analyzed by  them. The laws of thermodynamics, which describe basic and inevitable tendencies of all  physical systems, are derived from statistical considerations. The entire structure of  modem physics, our present concept of the nature of the physical universe, and of the  accessible experimental facts concerning it rest on these statistical concepts. Indeed,  the whole question of evidence and the way in which knowledge can be inferred from  evidence are now recognized to depend on these same statistical ideas, so that probability  notions are essential to any theory of knowledge itself.
  

Problems of Organized Complexity

  
This new method of dealing with disorganized complexity,  so powerful an advance over the earlier two-variable methods, leaves a great field  untouched. One is tempted to oversimplify, and say that scientific methodology went from  one extreme to the other-from two variables to an astronomical number — and left  untouched a great middle region. The importance of this middle region, moreover, does not  depend primarily on the fact that the number of variables involved is moderate —  large compared to two, but small compared to the number of atoms in a pinch of salt. The  problems in this middle region, in fact, will often involve a considerable number of  variables. The really important characteristic of the problems of this middle region,  which science has as yet little explored or conquered, lies in the fact that these  problems, as contrasted with the disorganized situations with which statistics can cope,  show the essential feature of organization. In  fact, one can refer to this group of problems as those of organized complexity.
What makes an evening primrose open when it does? Why does  salt water fail to satisfy thirst? Why can one particular genetic strain of microorganism  synthesize within its minute body certain organic compounds that another strain of the  same organism cannot manufacture? Why is one chemical substance a poison when another,  whose molecules have just the same atoms but assembled into a mirror-Image pattern, is  completely harmless? Why does the amount of manganese in the diet affect the maternal  instinct of an animal? What is the description of aging in biochemical terms? What meaning  is to be assigned to the question:
  Is a virus a living organism? What is a gene, and how does the original genetic  constitution of a living organism express itself in the developed characteristics of the  adult? Do complex protein molecules "know how" to reduplicate their pattern, and  is this an essential clue to the problem of reproduction of living creatures? All these  are certainly complex problems, but they are not problems of disorganized complexity, to  which statistical methods hold the key. They are all problems which involve dealing  simultaneously with a sizable number of factors which are interrelated into an  organic whole. They are all, in the language here proposed,  problems of organized complexity.
On what does the price of wheat depend?This too is a  problem of organized complexity. A very substantial number of relevant variables is  involved here, and they are all interrelated in a complicated, but nevertheless not in  helter-skelter, fashion.
  How can currency be wisely and effectively stabilized? To what extent is it safe to  depend on the free interplay of such economic forces as supply and demand? To what extent  must systems of economic control be employed to prevent the wide swings from prosperity to  depression? These are also obviously complex problems, and they too involve analyzing  systems which are organic wholes, with their parts in close interrelation.
  How can one explain the behavior pattern of an organized group of persons such as a  labor union, or a group of manufacturers, or a racial minority? There are clearly many  factors involved here, but it is equally obvious that here also something more is needed  than the mathematics of averages. With a given total of national resources that can be  brought to bear, what tactics and strategy will most promptly win a war, or better: what  sacrifices of present selfish interest will most effectively con-tribute to a stable,  decent. and peaceful world?
  These problems-and a wide range of similar problems in the biological, medical,  psychological, economic, and political sciences-are just too complicated to yield to the  old nineteenth~century techniques which were so dramatically successful on two-, three-,  or four-variable problems of simplicity. These new problems, moreover, cannot be handled  with the statistical techniques so effective in describing average behavior in problems of  disorganized complexity.
  These new problems, and the future of the world depends on many of them, requires  science to make a third great advance, an advance that must be even greater than the  nineteenth~century conquest of problems of simplicity or the twentieth~century victory  over problems of disorganized complexity. Science must, over the next 50 years, learn to  deal with these problems of organized complexity.
  Is there any promise on the horizon that this new advance can really be accomplished?  There is much general evidence, and there are two recent instances of especially promising  evidence. The general evidence consists in the fact that, in the minds of hundreds of  scholars all over the world, important, though necessarily minor, progress is already  being made on such problems. As never before, the quantitative experimental methods and  the mathematical analytical methods of the physical sciences are being applied to the  biological, the medical, and even the social sciences. The results are as yet scattered,  but they are highly promising. A good illustration from the life sciences can be seen by a  comparison of the present situation in cancer research with what it was twenty-five years  ago. It is doubtless true that we are only scratching the surface of the cancer problem,  but at least there are now some tools to dig with and there have been located some spots  beneath which almost surely there is pay-dirt. We know that certain types of cancer can be  induced by certain pure chemicals. Something is known of the inheritance of susceptibility  to certain types of cancer. Million-volt rays are available, and the even more intense  radiations made possible by atomic physics. There are radioactive isotopes, both for basic  studies and for treatment. Scientists are tackling the almost incredibly complicated story  of the biochemistry of the aging organism. A base of knowledge concerning the normal cell  is being established that makes it possible to recognize and analyze the pathological  cell. However distant the goal, we are now at last on the road to a successful solution of  this great problem.
  In addition to the general growing evidence that problems of organized complexity can  be successfully treated, there are at least two promising bits of special evidence. Out of  the wickedness of war have come two new developments that may well be of major importance  in helping science to solve these complex twentieth-century problems.
  The first piece of evidence is the wartime development of new types of electronic  computing devices. These devices are, in flexibility and capacity, more like a human brain  than like the traditional mechanical computing device of the past. They have memories in  which vast amounts of information can be stored. They can be "told" to carry out  computations of very intricate complexity, and can be left unattended while they go  forward automatically with their task. The astounding speed with which they proceed is  illustrated by the fact that one small part of such a machine, if set to multiplying two  ten-digit numbers, can perform such multiplications some 40,000 times faster than a human  operator can say 'Jack Robinson." This combination of flexibility, capacity, and  speed makes it seem likely that such devices will have a tremendous impact on science.  They will make it possible to deal with problems which previously were too complicated,  and, more importantly, they will justify and inspire the development of new methods of  analysis applicable to these new problems of organized complexity.
  The second of the wartime advances is the "mixed-team" approach of operations  analysis. These terms require explanation, although they are very familiar to those who  were concerned with the application of mathematical methods to military affairs.
  As an illustration, consider the over-all problem of convoying troops and supplies  across the Atlantic. Take into account the number and effectiveness of the naval vessels  available, the character of submarine attacks, and a multitude of other factors, including  such an imponderable as the dependability of visual watch when men are tired, sick, or  bored. Considering a whole mass of factors, some measurable and some elusive, what  procedure would lead to the best over-all plan, that is, best from the combined point of  view of speed, safety, cost, and so on? Should the convoys be large or small, fast or  slow? Should they zigzag and expose themselves longer to possible attack, or dash in a  speedy straight line? How are they to be organized, what defenses are best, and what  organization and instruments should be used for watch and attack?
  The attempt to answer such broad problems of tactics, or even broader problems of  strategy, was the job during the war of certain groups known as the operations analysis  groups. Inaugurated with brilliance by the British, the procedure was taken over by this  country, and applied with special success in the Navy's anti-submarine campaign and in the  Army Air Forces. These operations analysis groups were, moreover, what may be called mixed  teams. Although mathematicians, physicists, and engineers were essential, the best of the  groups also contained physiologists, biochemists, psychologists, and a variety of  representatives of other fields of the biochemical and social sciences. Among the  outstanding members of English mixed teams. for example, were an endocrinologist and an  X-ray crystallographer. Under the pressure of war, these mixed teams pooled their  resources and focused all their different insights on the common problems. It was found,  in spite of the modern tendencies toward intense scientific specialization, that members  of such diverse groups could work together and could form a unit which was much greater  than the mere sum of its parts. It was shown that these groups could tackle certain  problems of organized complexity, and get useful answers.
  It is tempting to forecast that the great advances that science can and must achieve in  the next fifty years will be largely contributed to by voluntary mixed teams, somewhat  similar to the operations analysis groups of war days, their activities made effective by  the use of large, flexible, and highspeed computing machines. However, it cannot be  assumed that this will be the exclusive pattern for future scientific work, for the  atmosphere of complete intellectual freedom is essential to science. There will always,  and properly, remain those scientists for whom intellectual freedom is necessarily a  private affair. Such men must, and should, work alone. Certain deep and imaginative  achievements are probably won only in such a way. Variety is, moreover, a proud  characteristic of the American way of doing things. Competition between all sorts of  methods is good. So there is no intention here to picture a future in which all scientists  are organized into set patterns of activity. Not at all. It is merely suggested that some  scientists will seek and develop for themselves new kinds of collaborative arrangements;  that these groups will have members drawn from essentially all fields of science; and that  these new ways of working, effectively instrumented by huge computers, will contribute  greatly to the advance which the next half century will surely achieve in handling the  complex, but essentially organic, problems of the biological and social sciences.
  

The Boundaries of Science

  
Let us return now to our original questions. What is  science? What is not science? What may be expected from science?
  Science clearly is a way of solving problems-not all problems, but a large class of  important and practical ones. The problems with which science can deal are those in which  the predominant factors are subject to the basic laws of logic, and are for the most part  measurable. Science is a way of organizing reproducible knowledge about such problems; of  focusing and disciplining imagination; of weighing evidence; of deciding what is relevant  and what is not; of impartially testing hypotheses; of ruthlessly discarding data that  prove to be inaccurate or inadequate; of finding, interpreting, and facing facts, and of  making the facts of nature the servants of man.
  The essence of science is not to be found in its outward appearance, in its physical  manifestations; it is to be found in its inner spirit. That austere but exciting technique  of inquiry known as the scientific method is what is important about science. This  scientific method requires of its practitioners high standards of personal honesty,  open-mindedness, focused vision, and love of the truth. These are solid virtues, but  science has no exclusive lien on them. The poet has these virtues also, and often turns  them to higher uses.
  Science has made notable progress in its great task of solving logical and quantitative  problems. Indeed, the successes have been so numerous and striking, and the failures have  been so seldom publicized, that the average man has inevitably come to believe that  science is just about the most spectacularly successful enterprise man ever launched. The  fact is, of course, that this conclusion is largely justified.
  Impressive as the progress has been, science has by no means worked itself out of a  job. It is soberly true that science has, to date, succeeded in solving a bewildering  number of relatively easy problems, whereas the hard problems, and the ones which perhaps  promise most for man's future, lie ahead.
  We must, therefore, stop thinking of science in terms of its spectacular successes in  solving problems of simplicity. This means, among other things, that we must stop thinking  of science in terms of gadgetry. Above all, science must not be thought of as a modern  improved black magic capable of accomplishing anything and everything.
  Every informed scientist, I think, is confident that science is capable of tremendous  further contributions to human welfare. It can continue to go forward in its triumphant  march against physical nature, learning new laws, acquiring new power of forecast and  control, making new material things for man to use and enjoy. Science can also make  further brilliant contributions to our understanding of animate nature, giving men new  health and vigor, longer and more effective lives, and a wiser understanding of human  behavior. Indeed, I think most informed scientists go even further and expect that the  precise, objective, and analytical techniques of science will find useful application in  limited areas of the social and political disciplines.
  There are even broader claims which can be made for science and the scientific method.  As an essential part of his characteristic procedure, the scientist insists on precise  definition of terms and clear characterization of his problem. It is easier, of course, to  define terms accurately in scientific fields than in many other areas. It remains true,  however, that science is an almost overwhelming illustration of the effectiveness of a  well-defined and accepted language, a common set of ideas, a common tradition. The way in  which this universality has succeeded in cutting across barriers of time and space, across  political and cultural boundaries, is highly significant. Perhaps better than in any other  intellectual enterprise of man, science has solved the problem of communicating ideas, and  has demonstrated the world-wide cooperation and community of interest which then  inevitably results.
  Yes, science is a powerful tool, and it has an impressive record. But the humble and  wise scientist does not expect or hope that science can do everything. He remembers that  science teaches respect for special competence, and he does not believe that every social,  economic, or political emergency would be automatically dissolved if "the  scientists" were only put into control. He does not-with a few aberrant  exceptions~expect science to furnish a code of morals, or a basis for esthetics. He does  not expect science to furnish the yardstick for measuring, nor the motor for controlling,  man's love of beauty and truth, his sense of value, or his convictions of faith. There are  rich and essential parts of human life which are alogical, which are immaterial and  non-quantitative in character, and which cannot be seen under the microscope, weighed with  the balance, nor caught by the most sensitive microphone.
  If science deals with quantitative problems of a purely logical character, if science  has no recognition of or concern for value or purpose, how can modern scientific man  achieve a balanced good life, in which logic is the companion of beauty, and efficiency is  the partner of virtue:
  In one sense the answer is very simple: our morals must catch up with our machinery. To  state the necessity, however, is not to achieve it. The great gap, which lies so  forebodingly between our power and our capacity to use power wisely, can only be bridged  by a vast combination of efforts. Knowledge of individual and group behavior must be  improved. Communication must be improved between peoples of different languages and  cultures, as well as between all the varied interests which use the same language, but  often with such dangerously differing connotations. A revolutionary advance must be made  in our understanding of economic and political factors. Willingness to sacrifice selfish  short-term interests, either personal or national, in order to bring about long-term  improvement for all must be developed.
  None of these advances can be won unless men understand what science really is; all  progress must be accomplished in a world in which modern science is an inescapable,  ever-expanding influence.


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