2010年12月23日 星期四

賽蒙行政決策理論與教育行政

這位"惡名昭彰"的教育部長的博士論文是 賽蒙行政決策理論與教育行政 (台北:五南 1986)

拿國家的錢去美國問Simon 一些沒什麼水準的話
照張像 回來出書 交差了事

吳清基:八德學生沒有那麼壞 我敢說「沒人對我鼓譟」!

NOWnews - ‎16小時之前‎
教育部長吳清基急赴桃園八德國中演說,卻因台下學生鼓譟不得不中斷好幾次。 桃園縣八德國中最近因校園霸凌事件躍上媒體版面,教育部長吳清基日前親自到校宣導反霸凌,卻有許多學生在台下鼓譟,導致吳清基的演說數度中斷,吳清基今(23)日在行政院主動澄清表示,「我敢說 ...
大讚有禮貌 吳清基讚賞八德國中生 蘋果日報

2010年11月24日 星期三

約走過地球8周?

Herbert Simon每天走路上學
所以計程是約走過地球8周


"不善左腦思維與數字的夫妻終於同意擴充產能了。在顧問軟硬兼施下,開始尋覓較大廠房、製作標準作業流程、儲備並訓練人力。「擀麵棍擀一圈是七公分,一條吐 司要擀60次所以是4.2公尺,每天做60條就是252公尺,每年工作約280天70公里,大概是台北到新竹的距離。」這是戚其明在台上講的一段話。愛好 美學,喜歡音樂的他原本連食材成本占售價是多少比例都搞不清楚,如今他在台上誓言要進行左腦革命。"

2010年11月23日 星期二

代某光通信部品與次系統台商徵高階品管主管

代某光通信部品與次系統台商徵高階品管主管

本刊版主剛從中國回來 一周來未更新

代某光通信部品與次系統台商徵高階品管主管
請將履歷等資訊寄
hcsimonl@gmail.com



鍾 漢清
Hanching Chung (or HC/ hc)
網址:http://www.deming.com.tw
台灣戴明圈: A Taiwanese Deming Circle
http://demingcircle.blogspot.com/
地址:台北市新生南路三段88號2樓
電話:(02) 23650127

2010年11月9日 星期二

卡內基美隆大學

卡內基美隆大學- 維基百科,自由的百科全書

卡內基美隆大學(Carnegie Mellon University)是一所研究型私立大學。該校位於美國賓夕法尼亞州匹茲堡。1967年由卡內基理工學院和梅隆工業研究所合併成立。 ...


美隆是好辭 相對枯槁

2010年11月1日 星期一

Models of Bounded Rationality: Empirically grounded economic reason - Google 圖書結果

Models of Bounded Rationality: Empirically grounded economic reason - Google 圖書結果

Herbert Alexander Simon - 1997 - Business & Economics - 479 頁
The third volume of Simon's collected papers continues this theme, bringing together work on this and other economics-related topics that have occupied his ...

這套書共3大冊 不知收入那一冊
少部分論文有中譯

The sciences of the artificial - Google 圖書結果

The sciences of the artificial - Google 圖書結果

Herbert Alexander Simon - 1996 - Science - 231 頁
Continuing his exploration of the organization of complexity and the science of design, this new edition of Herbert Simon's classic work on artificial ...


末章收入下文

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The Architecture of Complexity Herbert A. Simon Proceedings of the ...

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由 HA Simon 著作 - 1962 - 被引用 2555 次 - 相關文章
HERBERT A. SIMON. [PROC. PHIL.SOC. "complex systems." Roughly, by a complex system I mean one made up of a large number of parts that interact in a ...

2010年10月31日 星期日

Benoit Mandelbrot (1924-2010)

Simon 和數學家 Benoit Mandelbrot---他10月14日過世

http://www.answers.com/Benoit%20Mandelbrot



Benoit Mandelbrot, a well-known mathematician who was largely responsible for developing the field of fractal geometry and who once worked as an IBM researcher in Yorktown, died Thursday. He was 85 and lived in Cambridge, Mass.

The cause, his wife, Aliette said, was pancreatic cancer.

The Polish-born French mathematician founded the field of fractal geometry about four decades ago, the first broad attempt to quantitatively investigate the notion of roughness. In 2003, a story in The Journal News described fractal geometry as "a way to measure the rough and tumble real world. Nature abounds with complex shapes, from trees to snowflakes to mountains."

Fractals now are used in many fields. Fractal equations help Hollywood computer artists create more realistic landscapes and doctors measure irregularly shaped red-blood cells to develop better medication.

"As a physicist, I was trying to find ways of representing the messiness of nature. How many flat things do we see in nature? Maybe a lake when there is no breeze and no fish, but not many more," Mandelbrot told The Journal News in 2003. "How many flat things do we see in industry? Everything! This cabinet, the wall, and so on. So industry took some very basic shapes which are rare in nature and made them everywhere."

He received the Japan Prize that year, a prestigious award that is described as being second only to the Nobel Prize. It's awarded annually by the Science and Technology Foundation of Japan to scientists whose work has "advanced the frontiers of knowledge and served the cause of peace and prosperity for mankind."

Mandelbrot shaped many of his theories during his time as at IBM's Thomas J. Watson Research Center in Yorktown. Later, he became Sterling Professor Emeritus of Mathematical Sciences at Yale University.

He was born in Warsaw and emigrated to France with his family in 1936 to escape the growing German threat. They eventually settled in Lyon, where they endured World War II.

"I have had a very adventurous life," he told The Journal News in 2003. "I almost perished a number of times in my youth. I survived all kinds of complications. In the war in France I was almost killed by Germans or French police. I was 19 at the time. At 20 I was a war-hardened veteran, even though I had never seen military action, because I happened to have survived."

He is survived by his wife, two sons and three grandchildren.

The Associated Press contributed to this report.


Benoît Mandelbrot, Novel Mathematician, Dies at 85

Benoît B. Mandelbrot, a maverick mathematician who developed the field of fractal geometry and applied it to physics, biology, finance and many other fields, died on Thursday in Cambridge, Mass. He was 85.

Shizuo Kambayashi/Associated Press

Benoît B. Mandelbrot, left, and James A. Yorke, sharing a Japan Prize in 2003 for their pioneering work in chaos theory.

Wolfgang Beyer

Graphic representations of the Mandelbrot set have been implanted in popular culture, gracing T-shirts and album covers.

The cause was pancreatic cancer, his wife, Aliette, said. He had lived in Cambridge.

Dr. Mandelbrot coined the term “fractal” to refer to a new class of mathematical shapes whose uneven contours could mimic the irregularities found in nature.

“Applied mathematics had been concentrating for a century on phenomena which were smooth, but many things were not like that: the more you blew them up with a microscope the more complexity you found,” said David Mumford, a professor of mathematics at Brown University. “He was one of the primary people who realized these were legitimate objects of study.”

In a seminal book, “The Fractal Geometry of Nature,” published in 1982, Dr. Mandelbrot defended mathematical objects that he said others had dismissed as “monstrous” and “pathological.” Using fractal geometry, he argued, the complex outlines of clouds and coastlines, once considered unmeasurable, could now “be approached in rigorous and vigorous quantitative fashion.”

For most of his career, Dr. Mandelbrot had a reputation as an outsider to the mathematical establishment. From his perch as a researcher for I.B.M. in New York, where he worked for decades before accepting a position at Yale University, he noticed patterns that other researchers may have overlooked in their own data, then often swooped in to collaborate.

“He knew everybody, with interests going off in every possible direction,” Professor Mumford said. “Every time he gave a talk, it was about something different.”

Dr. Mandelbrot traced his work on fractals to a question he first encountered as a young researcher: how long is the coast of Britain? The answer, he was surprised to discover, depends on how closely one looks. On a map an island may appear smooth, but zooming in will reveal jagged edges that add up to a longer coast. Zooming in further will reveal even more coastline.

“Here is a question, a staple of grade-school geometry that, if you think about it, is impossible,” Dr. Mandelbrot told The New York Times earlier this year in an interview. “The length of the coastline, in a sense, is infinite.”

In the 1950s, Dr. Mandelbrot proposed a simple but radical way to quantify the crookedness of such an object by assigning it a “fractal dimension,” an insight that has proved useful well beyond the field of cartography.

Over nearly seven decades, working with dozens of scientists, Dr. Mandelbrot contributed to the fields of geology, medicine, cosmology and engineering. He used the geometry of fractals to explain how galaxies cluster, how wheat prices change over time and how mammalian brains fold as they grow, among other phenomena.

His influence has also been felt within the field of geometry, where he was one of the first to use computer graphics to study mathematical objects like the Mandelbrot set, which was named in his honor.

“I decided to go into fields where mathematicians would never go because the problems were badly stated,” Dr. Mandelbrot said. “I have played a strange role that none of my students dare to take.”

Benoît B. Mandelbrot (he added the middle initial himself, though it does not stand for a middle name) was born on Nov. 20, 1924, to a Lithuanian Jewish family in Warsaw. In 1936 his family fled the Nazis, first to Paris and then to the south of France, where he tended horses and fixed tools.

After the war he enrolled in the École Polytechnique in Paris, where his sharp eye compensated for a lack of conventional education. His career soon spanned the Atlantic. He earned a master’s degree in aeronautics at the California Institute of Technology, returned to Paris for his doctorate in mathematics in 1952, then went on to the Institute for Advanced Study in Princeton, N.J., for a postdoctoral degree under the mathematician John von Neumann.

After several years spent largely at the Centre National de la Recherche Scientifique in Paris, Dr. Mandelbrot was hired by I.B.M. in 1958 to work at the Thomas J. Watson Research Center in Yorktown Heights, N.Y. Although he worked frequently with academic researchers and served as a visiting professor at Harvard and the Massachusetts Institute of Technology, it was not until 1987 that he began to teach at Yale, where he earned tenure in 1999.

Dr. Mandelbrot received more than 15 honorary doctorates and served on the board of many scientific journals, as well as the Mandelbrot Foundation for Fractals. Instead of rigorously proving his insights in each field, he said he preferred to “stimulate the field by making bold and crazy conjectures” — and then move on before his claims had been verified. This habit earned him some skepticism in mathematical circles.

“He doesn’t spend months or years proving what he has observed,” said Heinz-Otto Peitgen, a professor of mathematics and biomedical sciences at the University of Bremen. And for that, he said, Dr. Mandelbrot “has received quite a bit of criticism.”

“But if we talk about impact inside mathematics, and applications in the sciences,” Professor Peitgen said, “he is one of the most important figures of the last 50 years.”

Besides his wife, Dr. Mandelbrot is survived by two sons, Laurent, of Paris, and Didier, of Newton, Mass., and three grandchildren.

When asked to look back on his career, Dr. Mandelbrot compared his own trajectory to the rough outlines of clouds and coastlines that drew him into the study of fractals in the 1950s.

“If you take the beginning and the end, I have had a conventional career,” he said, referring to his prestigious appointments in Paris and at Yale. “But it was not a straight line between the beginning and the end. It was a very crooked line.”



2010年10月25日 星期一

Bandwagon and Underdog Effects and the Possibility of Election

Bandwagon and Underdog Effects and the Possibility of Election ...

- [ 翻譯此頁 ]由 HA SIMON 著作 - 1954 - 被引用 125 次 - 相關文章
30 Sep 2010 ... to those predictions. Herbert A. Simon is Professor of Administration in ... of at least some people (e.g., a bandwagon or underdog effect). ...

Public Choice Society

Public Choice Society成立,網羅各方領域人才,如Riker (政治學)Herbert Simon(公共行政)、James Buchanan(財政)、Gordon Tullock(行政)、John Rawls(哲學)、James Coleman(社會、心理)、Vincent Ostrom(美國研究、公共行政)、Elinor Ostrom(社區研究)


Public Choice Society

http://www.pubchoicesoc.org/about_pc.php

2010年10月11日 星期一

認知科學與文學批評

朋友
這些所謂"交情千千" 最近數周起 都可以在"新生歲月"找到

這封也是向太座敬禮的


(皮亞傑訪談錄
劉玉燕譯
臺北市書泉
民85 .
根據皮亞傑的認知發展論,孩童的生理成熟度是掌控學習的重要因素(劉玉燕譯,1996))


正如Piaget 讀 Marcel Proust 的名著Remembrance of Things Past
他看到主角認知之發展
(H. A. Simon 真的寫過認知科學與文學批評的論文 在Stanford大學的刊物發表 可惜我讀到它時 Simon已過世 無法和他打屁了)ㄅ
我讀福原義春《文化打造極致創意》的"經營管理"那條線
也讀出許多論點

*****
謝謝 Ken Su 指出我給東海EMBA的幾處 甚至可能近十處的不一致或錯字
我暫時"沒心情"去改它們
因為演出的角本臨場再說吧

*****
上周六 忠信兄廚房有一架唐香燕學姐的約50本貓的書
她說只有
特別的貓. 作者:多麗斯?萊辛:Doris Lessing; 譯者:彭倩文; 出版社:時報出版
最值得讀
我建議她寫一本貓之經典導論
譬如說 T. S. Elliot的詩
我是貓......

*****
上周李建築師介紹德國時 很強調德國施政等最重視"公平".....
可惜 我現在比較難追憶他說的例子......

2010年10月5日 星期二

Aiming to Learn as We Do, a Machine Teaches Itself

SMARTER THAN YOU THINK

Aiming to Learn as We Do, a Machine Teaches Itself

Jeff Swensen for The New York Times

NELL’S TEAM Tom M. Mitchell, center, and, from left, William Cohen, Jayant Krishnamurthy, Justin Betteridge, Derry Wijaya and Bryan Kisiel.


Smarter Than You Think

Give a computer a task that can be crisply defined — win at chess, predict the weather — and the machine bests humans nearly every time. Yet when problems are nuanced or ambiguous, or require combining varied sources of information, computers are no match for human intelligence.

Grasping Language

Articles in this series are examining the recent advances in artificial intelligence and robotics and their potential impact on society.

Readers' Comments

Few challenges in computing loom larger than unraveling semantics, understanding the meaning of language. One reason is that the meaning of words and phrases hinges not only on their context, but also on background knowledge that humans learn over years, day after day.

Since the start of the year, a team of researchers atCarnegie Mellon University — supported by grants from the Defense Advanced Research Projects Agency andGoogle, and tapping into a research supercomputing cluster provided by Yahoo — has been fine-tuning a computer system that is trying to master semantics by learning more like a human. Its beating hardware heart is a sleek, silver-gray computer — calculating 24 hours a day, seven days a week — that resides in a basement computer center at the university, in Pittsburgh. The computer was primed by the researchers with some basic knowledge in various categories and set loose on the Web with a mission to teach itself.

“For all the advances in computer science, we still don’t have a computer that can learn as humans do, cumulatively, over the long term,” said the team’s leader, Tom M. Mitchell, a computer scientist and chairman of the machine learning department.

The Never-Ending Language Learning system, or NELL, has made an impressive showing so far. NELL scans hundreds of millions of Web pages for text patterns that it uses to learn facts, 390,000 to date, with an estimated accuracy of 87 percent. These facts are grouped into semantic categories — cities, companies, sports teams, actors, universities, plants and 274 others. The category facts are things like “San Francisco is a city” and “sunflower is a plant.”

NELL also learns facts that are relations between members of two categories. For example, Peyton Manning is a football player (category). The Indianapolis Colts is a football team (category). By scanning text patterns, NELL can infer with a high probability that Peyton Manning plays for the Indianapolis Colts — even if it has never read that Mr. Manning plays for the Colts. “Plays for” is a relation, and there are 280 kinds of relations. The number of categories and relations has more than doubled since earlier this year, and will steadily expand.

The learned facts are continuously added to NELL’s growing database, which the researchers call a “knowledge base.” A larger pool of facts, Dr. Mitchell says, will help refine NELL’s learning algorithms so that it finds facts on the Web more accurately and more efficiently over time.

NELL is one project in a widening field of research and investment aimed at enabling computers to better understand the meaning of language. Many of these efforts tap the Web as a rich trove of text to assemble structured ontologies — formal descriptions of concepts and relationships — to help computers mimic human understanding. The ideal has been discussed for years, and more than a decade ago Sir Tim Berners-Lee, who invented the underlying software for the World Wide Web, sketched his vision of a “semantic Web.”

Today, ever-faster computers, an explosion of Web data and improved software techniques are opening the door to rapid progress. Scientists at universities, government labs, Google, Microsoft, I.B.M. and elsewhere are pursuing breakthroughs, along somewhat different paths.

For example, I.B.M.’s “question answering” machine, Watson, shows remarkable semantic understanding in fields like history, literature and sports as it plays the quiz show “Jeopardy!” Google Squared, a research project at the Internet search giant, demonstrates ample grasp of semantic categories as it finds and presents information from around the Web on search topics like “U.S. presidents” and “cheeses.”

Still, artificial intelligence experts agree that the Carnegie Mellon approach is innovative. Many semantic learning systems, they note, are more passive learners, largely hand-crafted by human programmers, while NELL is highly automated. “What’s exciting and significant about it is the continuous learning, as if NELL is exercising curiosity on its own, with little human help,” said Oren Etzioni, a computer scientist at the University of Washington, who leads a project called TextRunner, which reads the Web to extract facts.

Computers that understand language, experts say, promise a big payoff someday. The potential applications range from smarter search (supplying natural-language answers to search queries, not just links to Web pages) to virtual personal assistants that can reply to questions in specific disciplines or activities like health, education, travel and shopping.

“The technology is really maturing, and will increasingly be used to gain understanding,” said Alfred Spector, vice president of research for Google. “We’re on the verge now in this semantic world.”

With NELL, the researchers built a base of knowledge, seeding each kind of category or relation with 10 to 15 examples that are true. In the category for emotions, for example: “Anger is an emotion.” “Bliss is an emotion.” And about a dozen more.

Then NELL gets to work. Its tools include programs that extract and classify text phrases from the Web, programs that look for patterns and correlations, and programs that learn rules. For example, when the computer system reads the phrase “Pikes Peak,” it studies the structure — two words, each beginning with a capital letter, and the last word is Peak. That structure alone might make it probable that Pikes Peak is a mountain. But NELL also reads in several ways. It will mine for text phrases that surround Pikes Peak and similar noun phrases repeatedly. For example, “I climbed XXX.”

NELL, Dr. Mitchell explains, is designed to be able to grapple with words in different contexts, by deploying a hierarchy of rules to resolve ambiguity. This kind of nuanced judgment tends to flummox computers. “But as it turns out, a system like this works much better if you force it to learn many things, hundreds at once,” he said.

For example, the text-phrase structure “I climbed XXX” very often occurs with a mountain. But when NELL reads, “I climbed stairs,” it has previously learned with great certainty that “stairs” belongs to the category “building part.” “It self-corrects when it has more information, as it learns more,” Dr. Mitchell explained.

NELL, he says, is just getting under way, and its growing knowledge base of facts and relations is intended as a foundation for improving machine intelligence. Dr. Mitchell offers an example of the kind of knowledge NELL cannot manage today, but may someday. Take two similar sentences, he said. “The girl caught the butterfly with the spots.” And, “The girl caught the butterfly with the net.”

A human reader, he noted, inherently understands that girls hold nets, and girls are not usually spotted. So, in the first sentence, “spots” is associated with “butterfly,” and in the second, “net” with “girl.”

“That’s obvious to a person, but it’s not obvious to a computer,” Dr. Mitchell said. “So much of human language is background knowledge, knowledge accumulated over time. That’s where NELL is headed, and the challenge is how to get that knowledge.”

A helping hand from humans, occasionally, will be part of the answer. For the first six months, NELL ran unassisted. But the research team noticed that while it did well with most categories and relations, its accuracy on about one-fourth of them trailed well behind. Starting in June, the researchers began scanning each category and relation for about five minutes every two weeks. When they find blatant errors, they label and correct them, putting NELL’s learning engine back on track.

When Dr. Mitchell scanned the “baked goods” category recently, he noticed a clear pattern. NELL was at first quite accurate, easily identifying all kinds of pies, breads, cakes and cookies as baked goods. But things went awry after NELL’s noun-phrase classifier decided “Internet cookies” was a baked good. (Its database related to baked goods or the Internet apparently lacked the knowledge to correct the mistake.)

NELL had read the sentence “I deleted my Internet cookies.” So when it read “I deleted my files,” it decided “files” was probably a baked good, too. “It started this whole avalanche of mistakes,” Dr. Mitchell said. He corrected the Internet cookies error and restarted NELL’s bakery education.

His ideal, Dr. Mitchell said, was a computer system that could learn continuously with no need for human assistance. “We’re not there yet,” he said. “But you and I don’t learn in isolation either.”

2010年10月4日 星期一

Imagined communities: reflections on the origin and spread of Nationalism

Imagined communities: reflections on the origin and spread of Nationalism

"群體史縮斂成一故事是特別重要的。秉持此一精神,Benedict Anderson (1991)論到,國家感牽涉到建立在透過一遺忘,發明,和詮釋的審慎結合所建構的歷史之想像共同體的了解。" james march (2010)

2010年10月3日 星期日

司馬賀R. H. Tawney 談教育

司馬賀R. H. Tawney 談教育



1999

我 又與他談大中國區的大學教育之「質」(例如世界上真正的科學實証教育,並未在教育中生根立足,所以怪力亂神現象特別嚴重。)和「量」(台灣的高教並不符合 人民的期望,而大陸的高教投資,遠低於發展之所需。台灣的所謂「追求卓越」,大筆的散財於高教,小學等基本教育和設備之質與量堪虞…)

  我是有心辦網路上的自由SIMON大學。他以為大學量已經太多,當前提以及未來數世代)中,最重要的是如何利用現代化的傳播科技,把世界上的一流大學(Strong universities)之資源與社區連用、分享。

我希望能編本《司馬賀談教育》;這以後有機會再談。

近來讀張漢裕教授所譯的R. H. Tawney《中國的土地與勞力》(1995,協志工業叢書;原書1929年出版),其中有許多話很重要:「...國家所需要的是受過教育的人,不是沒受過教育的畢業生,…再不可為了大量生產而犧牲內容。應該側重教學生自己思考──這是比較費力的事...」(中譯本,pp. 206 -07

R. H. Tawney真是名家,他對於中國現代化的整體建議是引《浮士德》中的一句詩為喻:『設非自己心靈出,何得精神助你與。』意思是:若非從你自己心中湧出,你不能得到什麼使你心靈更爽健。(p. 209

Land and Labour in China by R. H. Tawney《中國的土地與勞力》

2010年10月1日 星期五

Administrative behavior: a study of decision-making processes in administrative organizations

Page 345 …because they provide employees with a conceptual scheme of th…



Page 358 …by the human beings themselves of the forces which mold their b…


Page 368 ... Specialization (cont.) modes of, 292 by place, 29-31,36-40,26…




Page 2 …staff capable of influencing the operative group toward a pattern…Page 3 This purposiveness brings about an integration in the pattern of behavio…Page 18 In this book, the term organization refers to the pattern of communi …Page 19 …the processes for making and implementing decisions. This pattern p…Page 24 …heavily upon location in the organization and the pattern of …Page 25 …does not leave any room for rationality. If a role is a pattern of be…Page 28 …data seems to show some increase in this kind of work pattern, ...Page 53 ... a general pattern of accounting department organizatio…Page 54 ... as well as different communications patterns between the en…Page 81 …to do it. To state the matter formally, in a cooperative pattern bot…Page 86 …of alternatives casts considerable light on the patterns of i…Page 92 …fashion, then adult behavior would show little more pattern or i…Page 93 …behaving subject molds all his behavior into an integrated pattern…Page 96 …Imagination falls down also in conceiving all the possible patterns of …Page 99 …that assists in the preservation of useful behavior patterns is h…Page 101 …sophisticated level of behavior. Simpler behavior patterns ma…Page 105 …that bring about the integration of behavior in a broad pattern. ...Page 106 ... If the pattern of associations is rich, the mind acts as a sort of clo…Page 110 …institutions be conceived, in the broad sense, as patterns of g…Page 111 …meet new needs with new arrangements. The behavior patterns …


Page 111 …meet new needs with new arrangements. The behavior patterns


Page 77 …important departure of actual behavior from the model…Page 80 …little reflection will convince the reader that this game is a model…Page 118 … model of economic man was far more completely and …Page 119 …the perceived world is a drastically simplified model of…Page 121 …man rather than the administrator and are not part of the model use…Page 124 …for problem recognition is to build computational models …Page 132 …realm of human endeavor, and by using computers to model …Page 133 The goal of the cognitive science research has been to model the hu…Page 152 … models for reaching "optimal" decisions. Our exampl…Page 155 …production of goods and services. If we had enlarged our model t…Page 226 …is misconceived, because it is based on an invalid model of th…Page 245 …of computer time in organizations) is to model co…Page 246 …in an intelligent way is the creation of one or more models—eithe…Page 248 ... It incorporates analytic and synthetic models that are capable…Page 249 ... by embedding these fragments in comprehensive models. Page 297 …the seed around which the decision-maker's simplified model of th…Page 302 …attributing altruistic motives to people. In standard models of na…Page 330 …the excessively rational stance of other management models…Page 331 …willingness to depart from a strict utility-maximizing model. O…



Administrative behavior: a study of decision-making processes in administrative organizations
封面
Free Press, 1997 - 368 頁
In this fourth edition of his ground-breaking work, Herbert A. Simon applies his pioneering theory of human choice and administrative decision-making to concrete organizational problems. To commemorate the fiftieth anniversary of the book's original publication, Professor Simon enhances his timeless observations on the human decision-making process with commentaries examining new facets of organizational behavior. Investigating the impact of changing social values and modem technology on the operation of organizations, the new ideas featured in this revised edition update a book that has become a worldwide classic.

Named by Public Administration Review as "Book of the Half Century," Administrative Behavior is considered one of the most influential books on social science thinking, and was referred to by the Nobel Committee as "epoch-making."
















Written for managers and other professionals who wish to understand the decision-making processes at the heart of organization and management, it is also essential reading for students in business and management, economics, sociology, psychology computer science, government, and law.

2010年9月29日 星期三

From Newton's Sleep by Joseph Vining

From Newton's Sleep by Joseph Vining

第2-3次讀From Newton's Sleep by Joseph Vining

終於讀進去
這本書內容甚難
有幾次參考或推薦 HERB SIMON 的人工智能一書 不過索引查不到
我讀完 epigram (可從Amazon 網站讀 ) 等之後 重讀 William Blake 的書.....

Leadership and ambiguity: the American college president

這本書我在公企中心見過
可惜那兒現在查書很不方便

Leadership and ambiguity: the American college president - Google 圖書結果

Michael D. Cohen, James G. March, Carnegie Commission on Higher Education - 1986 - Education - 298 頁
Examines the American college presidency, discusses goals, budgets, policy decisions, and tenure, and recommends ways to improve university administration

Dr. James G. March publications

James G. March (Stanford University) All publications




PUBLICATIONS
Bibliographies 0
Book chapters 1
Book reviews 0
Books 15
Books, edited 5
Conf. papers 0
Conf. presentations 0
Conferences 0
Discussion groups 0
Grants 0
Journal articles 3
Periodicals/series 0
Proceedings 2
Proceedings, papers 0
Reports 0
Special issues 0
Theses 0
Treaties 0
Working papers 0
Display All

Dr. James G. March (b. ----, d. ----)

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POSITION(S) / JOB TITLE(S):
Professor Emeritus
AREAS OF EXPERTISE:
Sociology; Business/Management
ACADEMIC RANK:
Professor Emeritus
FACULTY/DEPARTMENT:
Education
INSTITUTION/ORGANIZATION:
Stanford University
EMAIL: (Homepage)
Unknown
HIGHEST DEGREE:
Doctorate
DEGREE FROM:
Yale University
SEX / LANGUAGE:
Male /
LAST LOGIN:
2008/10/20 01:19:19
MEMBER ID:
1105-8704 (Last changed on 2008/10/20 01:19:10)
ALL PUBLICATIONS IN REVERSE CHRONOLOGICAL ORDER
Author
[ 0 | 0 ]
March, James G.. (1999) The pursuit of organizational intelligence Malden, Ma: Blackwell.
(Book )
Editor
[ 0 | 0 ]
Brunsson, Nils; Olsen, Johan P.; March, James G.. (1998) Organizing organizations Bergen-Sandviken Norway and Middleton, WI: Fagbokf�rlaget.
(Book, Edited )
Author
[ 1 | 0 ]
March, James G.; Olsen, Johan P.. (1995) Democratic governance New York: Free Press.
(Book )
Author
[ 0 | 0 ]
March, James G.; Heath, Chip. (1994) A primer on decision making: How decisions happen New York and Toronto and New York: Free Press.
(Book )
Author
[ 0 | 0 ]
March, James G.; Simon, Herbert A.; Guetzkow, Harold Steere.. (1993) Organizations 2nd edition. Cambridge, Mass., USA: Blackwell.
(Book )
Author
[ 3 | 0 ]
March, James G.; Olsen, Johan P.. (1989) Rediscovering institutions: The organizational basis of politics New York: Free Press.
(Book )
Author
[ 1 | 0 ]
March, James G.. (1988) Decisions and organizations New York, N.Y.: Blackwell.
(Book )
Editor
[ 0 | 0 ]
March, James G.. (1987) Handbook of organizations New York: Garland Pub..
(Book, Edited )
Author
[ 0 | 0 ]
Cohen, Michael D.; March, James G.. (1986) Leadership and ambiguity: The American college president 2nd edition. Boston, Mass.: Harvard Business School Press.
(Book )
Editor
[ 0 | 0 ]
March, James G.; Weissinger-Baylon, Roger. (1986) Ambiguity and command: Organizational perspectives on military decision making Boston: Pitman.
(Conference Proceedings )
Author
[ 0 | 0 ]
Herriott, Scott R.; Levinthal, Daniel; March, James G.. (1985) Learning from Experience in Organizations
(Journal Article in The American Economic Review (AER) )
Author
[ 0 | 0 ]
Harrison, J. Richard; March, James G.. (1984) Decision making and postdecision surprises
(Journal Article in Administrative Science Quarterly )
Editor
[ 0 | 0 ]
Fogel, Robert W.; March, James G.. (1981) Aging--stability and change in the family New York: Academic Press.
(Book, Edited )
Author
[ 1 | 0 ]
March, J. G.. (1981) Decisions in organizations and theories of choice
(Book Chapter in Perspectives on organization design and behavior )
Editor
[ 0 | 0 ]
McGaugh, James L.; Kiesler, Sara B.; March, James G.. (1981) Aging--biology and behavior New York: Academic Press.
(Conference Proceedings )
Author
[ 0 | 0 ]
March, James G.. (1980) Autonomy as a factor in group organization: A study in politics New York: Arno Press.
(Book )
Author
[ 0 | 0 ]
March, James G.; Olsen, Johan P.. (1979) Ambiguity and choice in organizations 2d edition. Bergen: Universitetsforlaget.
(Book )
Author
[ 3 | 0 ]
March, James G.; Olsen, Johan P.. (1976) Ambiguity and choice in organizations Bergen: Universitetsforlaget.
(Book )
Author
[ 0 | 0 ]
Lave, Charles A.; March, James G.. (1975) An introduction to models in the social sciences New York: Harper & Row.
(Book )
Author
[ 0 | 0 ]
March, James G.. (1975) Autonomy as a factor in group organization: A study in politics None: None.
(Book )
Author
[ 0 | 0 ]
Cohen, Michael D.; March, James G.. (1974) Leadership and ambiguity: the American college president New York: McGraw-Hill.
(Book )
Author
[ 2 | 0 ]
Cohen, M. D.; March, J. G.; Olsen, J. P.. (1972) A garbage can model of organizational choice
(Journal Article in Administrative Science Quarterly )
Editor
[ 0 | 0 ]
Eulau, Heinz; March, James G.. (1969) Political science Englewood Cliffs, N.J: Prentice-Hall.
(Book, Edited )
Editor
[ 1 | 0 ]
March, James G.. (1965) Handbook of organizations Chicago: Rand McNally.
(Book, Edited )
Author
[ 5 | 0 ]
Cyert, Richard M.; March, James G.. (1963) A behavioral theory of the firm Englewood Cliffs, N.J: Prentice-Hall.
(Book )
Author
[ 2 | 0 ]
March, James G.; Simon, Herbert A.. (1958) Organizations New York: Wiley.
(Book )