2008年12月27日 星期六

領導

102
三呆老師說"領袖"的課程需求孔殷
我以前翻譯Peter Scholtes的 The Leader Handbook 為戴明領導手冊
還跟作者趣說 其中少談政治上的領導
跟Herbert Simon也談過類似的話
整本管理行為 少談 領導
因為這主題或許不容易為二十幾歲的Simon所掌握
不過他書房中的六位心儀的人物 或許也有領導學之意義

萨科齐说:"我认为,今天没有人可以否认,欧盟需要一个有魄力的轮值主席,一个真正的领袖。"

2008年12月15日 星期一

紀夢

現在很難得記住夢
不過前幾天想起兩年前作的一夢
發現幾乎已全忘了
那夢是關於Simon 夫婦最後一次到台灣來演講 我是招待者 (事實上 Simon一直都沒來過台灣)
事後他們很感激
那夢 比小說還精彩 可惜我太懶 竟然沒記下來
現在回想 損失頗大

汽車業的排放標決策過程

在Models of My Life by Herbert A. Simon
作者將他參與制定汽車業的排放標準等經驗總結 1970s

現在美國人民和政府都很不諒解 Big Three 廠商在這方面的不識大局
所以紓困 bail out 方案遇到很大的阻力

比較一下豐田英一在二十年前對通產省的要求環保的說法和做法 1980s
幾年前Toyota的混合車在美國大暢銷

現在呢
從新型混合動力車到電動汽車,環保車聚集一堂   
在“Eco Products 2008”上,豐田汽車、本田、日產汽車、馬自達、三菱汽車以及富士重工業等汽車廠商均設立了展區,展示了自己的環保車……

2008年12月4日 星期四

Allen Newell

Allen Newell, photo

Allen Newell, signature
Allen Newell
March 19, 1927 — July 19, 1992
By Herbert A. Simon

WITH THE DEATH FROM cancer on July 19, 1992, of Allen Newell the field of artificial intelligence lost one of its premier scientists, who was at the forefront of the field from its first stirrings to the time of his death and whose research momentum had not shown the slightest diminution up to the premature end of his career. The history of his scientific work is partly my history also, during forty years of friendship and nearly twenty of collaboration, as well as the history of the late J. C. (Cliff) Shaw, a longtime colleague; but I will strive to make this account Allen-centric and not intrude myself too far into it. I hope I will be pardoned if I occasionally fail.1

If you asked Allen Newell what he was, he would say, "I am a scientist." He played that role almost every waking hour of every day of his adult life. How would he have answered the question, "What kind of scientist?" We humans have long been obsessed with four great questions: the nature of matter, the origins of the universe, the nature of life, the workings of mind. Allen Newell chose for his life's work answering the fourth of these questions. He was a person who not only dreamt but gave body to his dream, brought it to life. He had a vision of what human thinking is. He spent his life enlarging that vision, shaping it, materializing it in a sequence of computer programs that exhibited the very intelligence they explained.

THE CAREER

In a remarkable talk about his research strategies and history given at Carnegie Mellon University in December 1991, seven months before his death,2 Allen described his career as aimed single-mindedly at understanding the human mind, but he also confessed to four or five substantial diversions from that goal--almost all of which produced major scientific products of their own. These "diversions" included his work with Gordon Bell on computer hardware architectures, the work with Stu Card and Tom Moran on the psychology of human-computer interaction, a major advisory role in the ARPA program of research on speech recognition, and his leadership in establishing computer science at Carnegie Mellon University and in creating the pioneering computer networking of that university's campus.

For the rest, Allen's work aimed steadily, from the autumn of 1955 onward, at using computer simulation as the key research tool for understanding and modeling the human mind. After the first burst of activity, which produced the Logic Theorist, the General Problem Solver, and the NSS chess program, he focused increasingly on identifying and overcoming the limitations and inflexibilities of these models that impeded their extension into a wholly general theory of the mind. His final book, Unified Theories of Cognition (1990), records the vast progress that he and others made over thirty years toward such generality, progress that in the final decade of his life focused on the emerging Soar system that he and his colleagues built.

HOW IT BEGAN

Allen Newell was born in San Francisco on March 19, 1927, the son of Dr. Robert R. Newell, a distinguished professor of radiology at Stanford Medical School, and Jeanette Le Valley Newell. An older sister was his only sibling. His father provided an important model for his son. In an interview (McCorduck, 1979, p. 122), Allen once said of him: "He was in many respects a complete man. . . . He'd built a log cabin up in the mountains. . . . He could fish, pan for gold, the whole bit. At the same time, he was the complete intellectual. . . . Within the environment where I was raised, he was a great man. He was extremely idealistic. He used to write poetry."

Allen's childhood was uneventful enough, many of the summers being spent in the Sierra Nevada at the log cabin his father built. Allen acquired a love of the mountains that never left him (an early ambition was to become a forest ranger) and a love of sports that, combined with his 6´1" height and sturdy build, led to the high school football team. He said of his own high school career (Newell, 1986, p. 347): "Allen was an indifferent pupil, though some people seemed to think he was bright. He went to Lowell High School--the intellectual high school of San Francisco--where he turned on academically. He also fell in love at age 16 with a fellow student, Noël McKenna, and married her as soon as tactically possible (age 20)." The marriage demonstrated that Allen and Noël were excellent decision makers even at that early age, for they formed a close and mutually supporting pair throughout the forty-five years of their marriage.

Allen graduated from high school just as World War II was ending, worked for the summer in a shipyard, and then enlisted in the U.S. Navy. Although close to his father and acquainted with many other scientists who were family friends, he had no intention, up to that time, of following a scientific career. Adoption of science as his vocation came, he said, rather suddenly, when, serving on a U.S. Navy ship that carried scientific observers to the Bikini nuclear tests and assigned the task of making maps of the radiation distribution over the atolls, he was infected with the excitement of the scientific enterprise.

On completing his service in the Navy, Allen enrolled in Stanford University, where he majored in physics. Undergraduate research led to his first paper, on X-ray optics (Newell and Baez, 1949). Stanford also exposed him in the classroom to George Polya, who was not only a distinguished mathematician but also a thoughtful student of mathematical discovery. Polya's widely read book, How to Solve It, published in 1945, had introduced many people (including me) to heuristic, the art of discovery. Allen came away from that experience aware that the processes of discovery could be investigated and analyzed and that heuristic--the art of guided search--played a key role in creative thinking. (Our common fascination with heuristic helps account for the rapidity with which Allen and I established common ground on first meeting early in 1952.)

RAND

A year in mathematics (1949-50) as a graduate student at Princeton and exposure to game theory, invented shortly before by von Neumann and Morgenstern, convinced Allen that he preferred a combination of experimental and theoretical research to pure mathematics. Taking a leave from Princeton, he found a position at the RAND Corporation, the then-new think tank in Santa Monica, in a group that was studying logistics problems of the Air Force. Two technical reports he coauthored with Joseph B. Kruskal (A Model for Organization Theory [1950] and Formulating Precise Concepts in Organization Theory [1951]) demonstrate his interest at that time in applying formal methods to complex empirical phenomena. Both papers adopt a style of axiomatization that was fashionable then in game theory and economics.

A six-week field visit to the Munitions Board in Washington impressed Allen with the distance that separated the formal models from reality, and his trip report, Observations on the Science of Supply (1951), exhibits his sensitivity to and sophistication about the organizational realities that he observed (probably reinforcing his brief naval experience and summer's work in the wartime shipyard). Somewhat disillusioned with axiomatization as the route to reality, Allen then turned to the design and conduct of laboratory experiments on decision making in small groups, a topic of considerable active interest in RAND at that time.

Dissatisfied also with small-group experiments as a way of studying organizations, the RAND team of John Kennedy, Bob Chapman, Bill Biel, and Allen conceived of constructing and operating a full-scale simulation of an Air Force Early Warning Station in order to study the organizational processes of the station crews. This effort, funded by the Air Force in 1952, led to the creation of the Systems Research Laboratory at RAND (eventually spun off as the Systems Development Corporation) (Chapman et al., 1959). Central to the research was recording and analyzing the crew's interactions with their radar screens, with interception aircraft, and with each other. These data focused Allen's attention on the information-handling and decision-making processes of the crew members and led to a search for an appropriate technique for analyzing and modeling the process. I met Allen when I became a consultant to the laboratory, and in the first minutes of our initial meeting he and I found common ground in the study of information processes as a route to understanding human decision making in organizations.

One of Allen's special responsibilities in the project was to find a way to simulate a radar display of air traffic, for no technology was available to the lab for making appropriate simulated patterns of blips as they move over radar screens. While searching for computational alternatives, Allen met Cliff (J. C.) Shaw, a RAND systems programmer, then working with the Card-Programmed Calculator, a prehistoric device that just preceded the first stored-program computers. Allen and Cliff conceived the idea of having the CPC calculate the successive air pictures and print out simulated radar maps. This not only provided the required laboratory simulation but also demonstrated to Al and Cliff (and to me when I learned of it) that computers, even prehistoric computers, could do more than arithmetic: they could produce spatial arrangements of nonnumerical symbols representing airplanes.

Now two of the preconditions were in place for Allen's move to the goal of understanding human thinking. He clearly saw information processing as a central activity in organizations, and he had had a first experience in symbolic computing. A third precondition derived from contact with the stored-program computer Johnniac that John von Neumann was building at RAND in about 1954.

At this time the ideas of cybernetics and artificial life were abroad. W. Ross Ashby had published in 1952 his Design for a Brain. W. Grey Walter (1953) in England had constructed some mechanical "turtles" that wandered about the room searching for a wall outlet when their batteries ran low, and similar creatures were built by Merrill Flood's group at RAND. By 1950 both Turing and Shannon had described (but not actually programmed) strategies for computer chess, and in 1952 I described (but did not implement) a program extending Shannon's ideas. On an auto trip en route to observing some Air Force exercises in the summer of 1954, Allen and I discussed at length the possibilities of using a computer to simulate human problem solving, but we were not then diverted from our current research on organizations.

THE COMMITMENT

In September 1954 Allen attended a seminar at RAND in which Oliver Selfridge of Lincoln Laboratories described a running computer program that learned to recognize letters and other patterns. While listening to Selfridge characterizing his rather primitive but operative system, Allen experienced what he always referred to as his "conversion experience." It became instantly clear to him "that intelligent adaptive systems could be built that were far more complex than anything yet done." To the knowledge Allen already had about computers (including their symbolic capabilities), about heuristic, about information processing in organizations, about cybernetics, and proposals for chess programs was now added a concrete demonstration of the feasibility of computer simulation of complex processes. Right then he committed himself to understanding human learning and thinking by simulating it. The student of organizations became a student of the mind.

In the months immediately following Selfridge's visit Allen wrote (1955) "The Chess Machine: An Example of Dealing with a Complex Task by Adaptation," which outlined an imaginative design for a computer program to play chess in humanoid fashion, incorporating notions of goals, aspiration levels for terminating search, satisfying with "good enough" moves, multidimensional evaluation functions, the generation of subgoals to implement goals, and something like best first search. Information about the board was to be expressed symbolically in a language resembling the predicate calculus. The design was never implemented, but ideas were later borrowed from it for use in the NSS chess program in 1958.

Newell's goals already extended far beyond chess: "The aim of this effort, then, is to program a current computer to learn to play good chess. This is the means to understanding more about the kinds of computers, mechanisms, and programs that are necessary to handle ultracomplicated problems (Newell, 1955). When the paper was presented in March 1955 at the Western Joint Computer Conference, Walter Pitts, the commentator for the session, said, "But, whereas [the authors of the other papers] are imitating the nervous system, Mr. Newell prefers to imitate the hierarchy of final causes traditionally called the mind. It will come to the same thing in the end, no doubt. . . ." From the very beginning something like the physical symbol system hypothesis was embedded in the research.

THE LOGIC THEORIST AND LIST PROCESSING

Even before his "conversion" Allen had been making plans to move to Pittsburgh early in 1955, with Noël and their new son Paul, to work with me in organizational research and earn a doctoral degree (in industrial management!). RAND agreed to continue to employ Allen as its (one-man) Pittsburgh outpost. This plan was duly executed but with the crucial alteration that the research was to be on programming a chess machine. It was arranged that Cliff Shaw at RAND would collaborate with us, and the program would run on RAND's Johnniac. For various technical and accidental reasons chess soon changed to geometry and geometry to logic, and the Logic Theory Machine (LTM), which discovered proofs for theorems in the propositional calculus, emerged as a hand simulation by December 15, 1955, and a running program in the summer of 1956.

Work was pursued simultaneously on a programming language that would be adequate for implementing the design, leading to the invention of the Information Processing Languages (IPLs), the first list-processing languages for computers. It is fair to say that the LTM and its successor, the General Problem Solver, laid the foundation for most of the artificial intelligence programs of the following decade. A genuine computer program performing a task of some sophistication has much more persuasive and educational powers than do verbal discussions of ideas. A running program is the moment of truth.

LTM was not a "deduction machine"--in fact, it worked backwards, inductively, from hypothesized theorem to the axioms. Discovering proofs is much like discovering anything else, a process of selective search. The fact that the task involves symbolic logic does not make the problem-solving process any more "logical" or less "intuitive" than if some other task (e.g., looking for a law that would connect the distances of planets from the sun with their periods of revolution) were in question.

Although this work was incorporated in Allen's doctoral dissertation, I never regarded him as my "student." Allen, Cliff, and I were research partners, each contributing his knowledge to a wholly joint product. Allen, when he arrived in Pittsburgh, already had five years of scientific work under his belt and needed colleagues more than teachers. I do not suggest that he did not learn--he never stopped growing and learning throughout his life--but he learned as scientists learn, from everyone and everything around them, especially from observation of nature itself.

Why did this particular work, which was part of an already existing Zeitgeist that had engaged the efforts of many able scientists, become highly visible and influential? An essential element in its impact was the actual running program. In addition, LTM and its successors were not directed at a single task. The specific programs were steps toward the solution of the general problem: understanding the human mind. The strategy is stated clearly in the first publication on LTM: "In this paper we describe a complex information processing system . . . capable of discovering proofs for theorems in symbolic logic. This system, in contrast to the systematic algorithms . . . ordinarily employed in computation, relies heavily on heuristic methods similar to those that have been observed in human problem solving activity. The specification is written in a formal language, of the nature of a pseudo-code . . . for digital computers. . . . The logic theory machine is part of a program of research to understand complex information processing systems by specifying and synthesizing a substantial variety of such systems for empirical study" (Newell and Simon, 1956).3

It is all there: complex information processing, symbolic computation, heuristic methods, human problem solving, a programming language, empirical exploration. These are the components of the fundamental research strategy of the Carnegie-RAND group in 1955 and 1956 that continued to guide Allen Newell's scientific work throughout his career. It led him continually to identify and diagnose the limitations of the programs he built and to ponder about architectures that would remove those limitations, and it led him in the last decade to Soar--not as the final answer, for he knew that there are no final answers in science, but as the next step of progress along a path that he followed as long as he was able to work.

EXPLOITING THE FIRST SUCCESS

For about five years after 1955 the Newell-Shaw-Simon team, aided by a growing circle of graduate students, pushed forward the research ideas opened up by LTM and the IPL programming languages. Among the main thrusts in which Allen was involved were thinking-aloud protocols, the General Problem Solver, information-processing languages and production systems, the NSS chess-playing program, and human problem solving. Until 1961 he remained on the staff of RAND (in Pittsburgh); in that year he accepted appointment as an institute professor at Carnegie Institute of Technology.

THINKING-ALOUD PROTOCOLS

There are severe difficulties in testing a theory of human thinking that predicts the sequence of thought processes each of only a few hundred milliseconds duration. Apart from neurological evidence, which is only now beginning to become available for tracing some processes, there were few obvious ways of obtaining data while a task was being performed, even at a density of one data point per second. It occurred to the team to instruct subjects to think aloud while performing problem-solving tasks. However, fifty years earlier the method called "introspection" had been thoroughly discredited as a means of obtaining reliable data in psychology. Hence, it was necessary to show that the thinking-aloud method was quite different from classical introspection and to determine the circumstances under which it could provide objective evidence about thought processes. A program of laboratory experimentation using thinking-aloud methods was launched by the beginning of 1957; formal methods were developed for encoding protocol data (problem behavior graphs); and a decade later Allen and Don Waterman made the first, only partially successful, attempt at automating protocol analysis (Waterman and Newell, 1971).

THE GENERAL PROBLEM SOLVER (GPS)

In the summer of 1957, during a workshop at Carnegie Tech on organizational behavior, Al and I extracted from the protocol of a single subject solving logic problems what proved to be a key mechanism in human problem solving: means-ends analysis. In M-E analysis the problem solver compares the current situation with the goal situation; finds a difference between them; finds in memory an operator that experience has taught reduces differences of this kind; and applies the operator to change the situation. Repeating this process the goal may gradually be attained, although there are generally no guarantees that the process will succeed.

The idea of M-E analysis led to the General Problem Solver (Newell, Shaw, and Simon, 1960), a program that could solve problems in a number of domains after being provided with a problem space (domain representation), operators to move through the space, and information about which operators were relevant for reducing which differences. The research also discovered schemes that permitted GPS to produce its own operators from a small set of primitives and to learn which operators were relevant for reducing which differences.

THE INFORMATION PROCESSING LANGUAGES (IPLS)

The IPL languages in artificial intelligence and their contemporary FORTRAN in numerical computing settled once and for all the essentiality of higher-level languages for sophisticated programming. The IPLs were designed to meet the needs for flexibility and generality: flexibility, because it is impossible in these kinds of computations to anticipate before run time what sorts of data structures will be needed and what memory allocations will be required for them; generality, because the goal is not to construct programs that can solve problems in particular domains, but to discover and extract general problem-solving mechanisms that can operate over a range of domains whenever they are provided with an appropriate definition for each domain.

To achieve this flexibility and generality the IPLs introduced many ideas that have become fundamental for computer science in general, including lists, associations, schemas (frames), dynamic memory allocation, data types, recursion, associative retrieval, functions as arguments, and generators (streams). The IPL-V Manual (Newell, 1961), exploiting the closed subroutine structure of the language, advocated a programming strategy that years later would be reinvented independently as structured programming--mainly top-down programming that avoided go-to's. LISP, developed by John McCarthy in 1958, which embedded these list-processing ideas in the lambda calculus, improved their syntax and incorporated a "garbage collector" to recover unused memory, soon became the standard programming language for artificial intelligence (AI).

PRODUCTION SYSTEM LANGUAGES (OPS5)

Allen did not regard the IPLs or their successors as final solutions to the problems of organizing AI programs. Experience with the General Problem Solver revealed a tendency for the program to burrow into a deep pit of successive subgoals, with no way for the top program levels to regain control. A way out of the dilemma began to appear in the middle 1960s in the form of production system languages, introduced into computing by Bob Floyd and others to aid in compiling compilers. In a production system each instruction in the language takes the form of a condition followed by an action: "IF [such and such is the case] THEN [do so and so]." Completely general programming languages can be constructed on this plan.

The Carnegie-RAND group saw in production system languages a solution to the control problem, and Allen took leadership in the development of a succession of such languages, the best known and most widely used of which is OPS5. OPS5 in turn provided the central ideas for the language employed to program the Soar system. A closely related set of ideas that we developed at about the same time, out of concern with the program control problem, led to a decentralized system in which independent processes add information to a common memory ("blackboard") and obtain information they need from that memory. The blackboard idea has achieved wide use in speech recognition, vision programs, and elsewhere.

CHESS: THE NSS PROGRAM

The third main substantive product of the Carnegie-RAND group was a chess program named NSS, the initials of its authors (Newell, Shaw, and Simon, 1958). It was not the first chess program to be implemented and run (Alex Bernstein, among others, completed programs somewhat earlier), nor was it a very strong player: as critics of artificial intelligence were fond of pointing out, it was once beaten by a ten-year-old child. What the critics failed to understand was its purpose: to demonstrate how highly selective search guided by heuristics and by goals evoked by cues in the problem situation could achieve intelligent behavior in a complex task.

HUMAN PROBLEM SOLVING

With the completion of LTM, GPS, the list-processing languages and production systems, and NSS, Al, Cliff, and I began more and more to pursue separate projects in collaboration with other colleagues and graduate students. The last major project Allen and I undertook together was to summarize our research on problem solving--experiments, simulation, and theory--in Human Problem Solving, which was published in 1972. The gradual cessation of close collaboration reflected no rift, as is evident from our joint 1975 Turing Lecture (Newell and Simon, 1976) and the weekly or more frequent conversations that continued until a few days before Allen's death, but a natural drift as each of us interacted with different graduate students and faculty colleagues and built our research strategies to reflect different bets about the locus of the biggest payoffs from studying intelligence.

COGNITIVE ARCHITECTURE

Allen, from an early stage of his research and increasingly as the years passed, was especially concerned with computational architecture and modeling the control structures underlying intelligence.

An architecture is a fixed set of mechanisms that enable the acquisition and use of content in a memory to guide behavior in pursuit of goals. In effect, this is the hardware-software distinction. . . . This is the essence of the computational theory of mind. (Newell, 1992, p. 27)

The early attention of the RAND-Carnegie group to flexibility and generality and the realization of these properties in the programming languages the group invented have already been noticed. The languages became part of the "hardware" that supported the underlying structure for the AI programs, anticipating the much later efforts of others to embed list processing in actual physical hardware. The languages also built into the AI systems some of the salient characteristics of human memory as revealed by psychological research, for example, its associative structure embodied in lists and schemas and the production-like character of stimulus-response connections.

UNSOLVED ARCHITECTURAL PROBLEMS

But important architectural problems remained unsolved. The experience with GPS underlined the importance of control structures for keeping a problem-solving system on course, neither dissipating its efforts in scattered random search nor following long narrow paths that often led, after much wasted effort, to dead ends. The concern for these problems can be traced through a series of Allen's publications beginning in the early 1960s and continuing through most of his career: "Some Problems of Basic Organization in Problem-Solving Programs" (1962), "Learning, Generality and Problem Solving" (1963), "The Search for Generality" (with G. Ernst, 1965), "Limitations of the Current Stock of Ideas for Problem Solving" (1965), "On the Representation of Problems" (1966), "The Trip Towards Flexibility" (1968), "A Model for Functional Reasoning in Design" (with P. Freeman, 1971), "A Theoretical Exploration of Mechanisms for Coding the Stimulus" (1972), "Production Systems: Model of Control Structures" (1973), and "How Can Merlin Understand?" (description of a "unified" architecture based on matching) (with J. Moore, 1974); then, after about an eight-year interval, "Learning by Chunking: Summary of a Task and a Model" (with P. S. Rosenbloom, 1982) and "A Universal Weak Method" (with J. Laird, 1983)--these last two papers being early descriptions of crucial components of what became the Soar system, which occupied the last decade of Allen's life.

THE MERLIN PROGRAM

MERLIN, an architectural enterprise undertaken about 1967, began as an attempt to build a pedagogical tool but became a serious effort to construct a system that had understanding. "MERLIN," Newell wrote, "was originally conceived . . . out of an interest in building an assistance-program for a course in AI. The task was to make it easy to construct and play with simple . . . instances of AI programs. . . . [T]he effort transmuted into . . . building a program that would understand AI--that would be able to explain and run programs, ask and answer questions about them. . . . The intent was to tackle a real domain of knowledge as the area of constructing a system that understood."

The basic ideas around which MERLIN was built were analogy and matching: "the construction of maps from the structure that represents what MERLIN knows to be the structure that MERLIN seeks to understand." The difficulties that were encountered en route to this goal were so severe that Newell regarded MERLIN as a failure, not reaching its practical goals and not producing results that had an impact on the rest of the field. It is described in a single published article (Moore and Newell, 1974). Many innovative AI ideas were embedded in MERLIN, but Allen was reluctant to publish them prior to building a complete running system that incorporated them all.

DIVERSIONS

The important work that Allen described as his "diversions" included research on computer hardware structures, the fostering of research on speech understanding, and research on human-computer interaction. Later I will mention other diversions in the form of institution-building activities.

COMPUTER STRUCTURES

It is perhaps not surprising that someone deeply concerned with program organization would become interested in computer hardware architectures, and Allen did. Nevertheless he regarded his work on this topic, which began with Gordon Bell's invitation in about 1968 to collaborate on a book on computer systems, as a diversion from his main objective. The strategy of simulating human thinking did not rest on any assumption of similarity between computer architectures and the architecture of the brain beyond the very general assumptions that both were physical symbol systems and that therefore the computer could be programmed to behave like the mind. Nevertheless, there are fundamental architectural problems common to all computing that reveal themselves in hardware and software at every level, for example, how to organize systems so that they can operate in parallel on multiple tasks with due respect for priorities and precedence constraints between processes.

Newell and Bell undertook to describe architectures at two levels: (1) the system level in terms of memories, processors, switches, controls, transducers, data operators, and links (the PMS language) and (2) the instruction level in terms of the detailed operations of the instruction set (the ISP language). Their book, Computer Structures: Readings and Examples, using the PMS and ISP languages to characterize a large number of computers, appeared in 1971. A revised edition coauthored with Bell and Siewiorek was published in 1981.

The work with Bell led Allen to other projects on computer and software systems design, and a number of his publications up to about 1982 were devoted to these projects. In 1970-71 Newell, McCracken, Robertson, and others built a language, L*, that aimed at providing systems programmers with a kernel that would facilitate building operating systems and user interface.

In 1972, in connection with an AI workshop that we organized, Newell, Robertson, and McCracken built a pioneering hierarchical menu system that gave the workshop participants access to demonstrations of an assortment of AI programs.

Some years later in 1978-82, using the new touch-screen technology, this idea developed into the hypermedia ZOG system, which became a tool for accessing the administrative data base on the newly launched aircraft carrier USS Carl Vinson (Newell et al., 1982). Other computer and systems design diversions for Allen included work with colleagues in the computer science department around 1971 on C.mmp and other parallel hardware cum software systems that were being designed there (Bell et al., 1971).

SPEECH UNDERSTANDING

A further major diversion for Allen in the 1970s resulted from ARPA's interest in the possibility of launching a program in automatic speech recognition. Specifically because he was not an active speech researcher and hence stood in a neutral corner, Allen was asked to chair a study group whose 1971 report formed the basis for a major ARPA research effort (Newell et al., 1971). Allen then became chair of the steering committee for the project and produced a progress report in 1975 (Newell et al.) and a final evaluation in 1977 (Medress et al.). His role in the speech effort illustrates both his stature in the profession and his willingness to accept "citizenship" responsibilities for the growth of artificial intelligence.

HUMAN-COMPUTER INTERACTION

When the Xerox PARC laboratory was formed in 1970, Allen, consulted about its research program, proposed a project that would apply psychological theory to human-computer interaction and, in particular, to the design of computer interfaces. Beginning in 1974, Allen with two of his former students, Stu Card and Tom Moran, began to bring together existing psychological data, examine them for regularities (such as Fitts's Law and the power law for learning), construct an engineering-level model of routine cognitive skills (MODEL HUMAN PROCESSOR) and a methodology, "GOMS," standing for goals, operators, methods, and selection, for analyzing new tasks in terms of the basic processes required to perform them. This work was brought together in The Psychology of Human-Computer Interaction (Card et al., 1983).

Though in one sense a diversion from his main concerns, the Xerox PARC activity brought Allen back from a preoccupation with computers to concern for the human mental architecture. Moreover, the requirement of modeling entire human tasks required the group to think in terms of a broad-gauged, unified theory. In this sense the project was a step toward the Soar system, planning for which began in the later 1970s before publication of the human-computer interaction book.

SOAR

Allen came to doubt that lack of experimental evidence was the limiting factor in the progress of cognitive psychology. Sufficient data, he thought, already existed to pin down much of the structure of the mind at the architectural level. Moreover, further experimental work would be well aimed and useful only if guided, not by particularistic microtheories, but by a broad theoretical framework. In his final book, Unified Theories of Cognition (1990), based on his William James Lectures at Harvard in 1987, he called for such theories and drew the bold outlines of what such a theory might look like, taking Soar as his model. He was careful not to refer to Soar as "the unified theory of cognition," but introduced it as "a candidate unified theory." Indeed, in his final chapter he gives a reasoned argument as to why "there must be many unified theories" on the road to developing a veridical one.

When existing unified theories are viewed closely, each can be seen to be built around a core cognitive activity, which is then extended to handle other cognitive tasks. In Anderson's Act* the core is semantic memory; in EPAM, perception and memory; in connectionist models, concept learning. In Soar as in GPS the core is problem solving, and the central GPS concept of problem space is taken over and expanded to allow the system to use multiple problem spaces in solving a single problem. The Soar program is a production system. To this were added two key components developed in collaboration with graduate students: learning by chunking (Rosenbloom and Newell, 1982), which produced a wide variety of kinds of learning obeying the empirically observed power law, and a universal weak method (Laird and Newell, 1983), which incorporated a method for universal subgoaling.

Learning by chunking derived from previous AI work on memory organization in terms of chunks and on learning by adaptive production systems (systems that created and assimilated new productions). What was new in Soar was the use of this mechanism as the sole learning mechanism and the demonstration that it was both powerful and consistent with the power law of learning.

The universal weak method of problem solving consisted at each step of finding which operators were then executable; if there were none or if there were more than one, declaring an impasse, and moving to a new problem space with a new subgoal to resolve the impasse. This procedure generalized the idea of problem spaces and established a consistent semantics for the possible relations among them.

The Soar project continued to grow through the 1980s and 1990s with steadily increasing numbers of active participants at Carnegie Mellon and elsewhere (including the University of Michigan, the Information Systems Institute at the University of Southern California, and several European sites). The effort was directed at extending and strengthening the basic Soar architecture and simultaneously demonstrating its capacity for handling a widening range of tasks, including language comprehension, complex problem solving, and even cryptarithmetic--one of GPS's initial tasks. The scope of the system at the time of Allen's death can be seen from his Unified Theories book, and work on it continues actively today on numerous fronts.

While it would be hazardous to predict what resemblance there will be between Soar and the "ultimate" unified theory of cognition, it is already evident that Allen's strategy of putting all of his (and many other people's) energies into Soar has intensified interest in building broad-gauged theories that cover a wide range of cognitive processes and has left an important permanent mark on cognitive science.

SCIENCE STATESMANSHIP

It is hard to know whether to classify the time Allen spent as a citizen of the university and of the wider science community as one of his diversions or as part of the mainstream of his scientific work. From the time of his employment at RAND he was keenly aware of the dependence of progress in science upon the institutions that housed and nourished it and he identified closely with the institutions in which he worked. During the early years of his stay at RAND he was persuaded that the think tank was the preferred research organization of the future, but he gradually came to believe that universities had capacities for self-renewal that were hard to maintain in independent laboratories. This change in belief played an important part in his decision to move in 1961 from RAND to the faculty of the Carnegie Institute of Technology.

Allen played an important leadership role in every organizational setting in which he found himself: RAND, the computer science department (later a school) at Carnegie Mellon, the whole university, the national and international computer science research community, and ARPA as a part of it. In general he did not do this by occupying formal administrative positions but by taking on specific assignments and by serving as a very active and highly valued elder statesman. For these purposes he was, as I have remarked, "elder" all his life.

THE COMPUTER SCIENCE DEPARTMENT

While still a doctoral student Allen was already called on for advice as we first brought computers to Carnegie Mellon University. (The first one arrived with Alan Perlis in about 1956.) By 1961, when an informal graduate program in computer science was set up by mutual agreement among four departments, Allen was a major figure along with Perlis and myself in pushing its development and then creating a computer science department, involved deeply in decisions about curriculum and the acquisition of equipment.

With Bert Green, then chairman of the psychology department, Allen was instrumental in obtaining the first large, continuing NIMH research grant for cognitive science research in that department. He was a principal figure, initially along with Alan Perlis, in obtaining and renewing the large ARPA grants that provided the core funding for what quickly became one of the nation's leading computer science departments. For the ensuing quarter century or more Allen played a major role in both departments through his research, his teaching, his guidance of graduate students, and his participation in policy.

THE CAMPUS NETWORK

From about 1972 the experience of members of the computer science department with the ARPA network convinced the community that a network of electronic communications was essential not only for the department but for the university. With the department having persuaded the university administration that Carnegie Mellon was in a unique position to offer national leadership in this direction, Allen agreed to serve as chairman of a task force that was appointed to prepare a plan and to educate the campus community about its potential. In February 1982 the task force issued its report, The Future of Computing at Carnegie Mellon University. An agreement was reached with IBM for collaboration in designing and installing the system, and the Andrew system, CMU's campus-wide network--one of the first in the nation--came into being. (The Andrews were Andrew Carnegie and Andrew Mellon.)

ARPA

From its beginnings artificial intelligence and simulation of human thinking have been foci of controversy, eliciting disbelief and anger from those who find the idea of a machine thinking either incredible or threatening. Decisions about funding AI research inevitably became enmeshed in this controversy about its worth, and the support by ARPA of computer science in general and AI in particular was periodically under attack throughout a long and stormy history.

A very large slice of Allen's life was spent preparing research proposals and budget defenses for computer science at Carnegie Mellon, as well as participating in ARPA planning exercises and interpreting AI and cognitive science research to the broader scientific community. This, too, is a normal part of institution building in science, but not its pleasantest part. Allen, while resenting the time lost in these duties, never shirked them. However, his belief (and mine) was that propaganda of the deed was more important than propaganda of the word: that in the longer run the fate of AI and cognitive simulation would be determined not by debates with philosophers about what was possible, a priori, but by our success or failure in building programs that demonstrably simulate and thereby provide theoretical explanations for human thought processes. Every possible waking moment was to be reserved for that task.

COGNITIVE SCIENCE AND AAAI

Professional organizations are important among the institutions of science, and Allen played his role in them also. It was an honor that he was proud of, but no surprise, that he was elected the first president of the American Association for Artificial Intelligence and received the first Award for Research Excellence from the International Joint Conference on Artificial Intelligence. Editorships, however, were not for him.

ALLEN, THE PERSON

Allen Newell was a memorable person in the most literal meaning of that phrase. I will draw here on my own impressions as recorded in my autobiography (Simon, 1991) and follow these with some comments by others who knew him well.

When I first met Al at RAND in 1952, he was 25 years old and fully qualified for tenure at any university--full of imagination and technique. . . . His energy was prodigious, he was completely dedicated to science, and he had an unerring instinct for important (and difficult) problems. If these remarks suggest that he was not only bright but brash, they are not misleading.
If imagination and technique make a scientist, we must also add dollars. I learned . . . [from Al] . . . how to position the decimal point in a research proposal. . . . Thinking big has characterized Al's whole research career, not thinking big for bigness' sake, but thinking as big as the task invites. . . .
From our earliest collaborations, Al has kept atrocious working hours. By this I . . . mean . . . that he works at the wrong time of day. . . . He preferred sessions that began at eight in the evening and stretched almost to dawn. I would have done most of my day's work by ten that morning, and by ten in the evening was ready to sleep, and not always able not to.
Perhaps his greatest pleasure . . . is an "emergency" that requires him to stay up all night or two consecutive nights. I recall his euphoria on our visit to March Air Force Base in 1954, when the air exercise extended over a whole weekend, twenty-four hours per day.
Some of these memories are frivolous, but high spirits, good humor, and hard work have characterized my relations with Al from the beginning.

Allen was serious but not solemn. Whimsy and laughter came easily and often to him. Life, sometimes perplexing, was not a plodding march but a vivid drama in which he acted with brilliance and éclat, quite aware of the dramatic effects he was producing. This too was obvious early on. The Systems Research Laboratory operated on the grandest scale, its cast an entire Air Force unit. Only Allen and his codirectors could have dreamed up theater on this megabuck scale at a time when behavioral scientists might timidly request $5,000 or $10,000 for their research. His forceful qualities and his exuberance impressed themselves on all who met him.

As Cliff Shaw recalled (McCorduck, 1979): "Energy is the thing I remember mainly about working with Al. Energy and brilliance. Long phone calls and long sessions on the teletype were typical. We would have sessions late into the night at Al's home. I felt like I was tagging along behind, trying to get that Johnniac to do what we already knew could be done. And with Al's energy, it was a good thing he had IPL-V, the programming language, as another outlet, so all that energy didn't descend on me."

And much later, from a graduate student: "Allen Newell was not my adviser . . . and was not on my thesis committee. But still Allen shaped my thinking: from him I learned every day more and more what research is. Through him I understood how to work dynamically towards my research goals. . . . Very rapidly, the initial intimidation I felt . . . was transformed into admiration, friendship and respect." That testimonial, given at his memorial service, could be duplicated dozens of times over by his co-workers--from full professors to new graduate students. In his work with students he was patient, his criticism was constructive, he never lost his temper. If he had any faults as a mentor (and who does not?), it was probably in becoming so involved himself in his students' research problems that he sometimes provided them with more structure and more insights than was good for them. They had to work very hard and fast to retain the strategic initiative.

Everything he attended to he attended to with energy and depth--whether it was his current research problem or an inquiry directed to him by a student or a visitor. In fact, it was this inability to address matters superficially that made the diversions weigh so heavily on Allen, taking him from his main research for considerable periods of time. But he handled the diversions with the same cheery enthusiasm and éclat as he did the mainstream tasks. It is hard to recall a lackluster Newell performance, whether it be a public address, a conversation in his office, or the analysis of a thinking-aloud protocol.

I have said nothing about Allen's family life or leisure. Noël and Allen with their son Paul formed a close-knit family. As much of his work was done on the computer at home, he was not at all an absentee husband or father, but shared his activities with his family in spite of his marathon work week. The Newells enjoyed entertaining their friends, most of them from CMU or other academic communities. The categories of introvert or extrovert don't quite seem to fit Allen; he worked long hours in his study, but he spent enormous amounts of time with other people--usually engaged in professional tasks.

There was little evidence of, or time for, the simpler kinds of leisure or hobbies unrelated to his work. He traveled fairly often abroad, usually with Noël, but mostly to professional meetings, and only occasionally did he add more than a few vacation days to these trips. At the very end of his life I learned--much to my astonishment, as I had had no inkling of it--that he frequently watched Sunday afternoon (or was it Saturday afternoon?) TV football games, perhaps a bit of fond nostalgia for his high school athletic days.

It is fitting to conclude this account with a selection from Allen Newell's own set of maxims for the dedicated scientist, proposed in his "Desires and Diversions" talk of December 1991, for these maxims describe his own life:

To each scientific life, its own style; and each style defines a life.
Science is in the details.
To work with the results of field X, you must be a professional in X.
There is no substitute for working hard--very hard.
A scientist is a transducer from nature to theory; seek out nature and listen to her.
The scientific problem chooses you; you don't choose it.
New things get their start by evolution or change, not design.
Everything must wait until its time; science is the art of the possible.
Diversions occur, make them count; salvage what is possible for the main goal.
Solve whatever problems must be solved; but do not be seduced by them.
Deep scientific ideas are exceedingly simple; others usually see them as trivial.

and, finally:

Choose a final project to outlast you.

For Allen, Soar was that project.

NOTES

1 In preparing this account of Allen Newell's life I have drawn heavily on a briefer memorial (Simon, 1993) published in Artificial Intelligence and on a more complete one published by John Laird and Paul Rosenbloom (1992) in AI Magazine. Newell's papers are deposited in the Archives of Hunt Library at Carnegie Mellon University, where can also be found the transcripts of lengthy interviews with Newell by Pamela McCorduck, which were used extensively in her Machines Who Think (1979), and by Arthur L. Norberg, who interviewed Newell about his activities in connection with ARPA.

2 This talk was videotaped and is available by writing to University Video Communications, P.O. Box 5129, Stanford CA 94309.

3 Although, for reasons that are no longer obvious, Cliff Shaw was not a coauthor of this paper; he was a full partner in the entire research effort.

REFERENCES

Ashby, W. R. 1952. Design for a Brain. New York: Wiley.
Bell, C. G., and A. Newell. 1971. Computer Structures: Readings and Examples. New York: McGraw-Hill.
Bell, C. G., W. Broadley, W. Wulf, A. Newell, C. Pierson, R. Reddy, and S. Rege. 1971. C.mmp: The CMU Multiminiprocessor Computer: Requirements, Overview of the Structure, Performance, Cost and Schedule. Technical Report, Computer Science Department, Carnegie Mellon University, Pittsburgh.
Berkeley, E. C. 1949. Giant Brains, or Machines That Think. New York: Wiley.
Bowden, B. V., ed. 1953. Faster Than Thought. New York: Pitman. (Contains Turing's description of a chess-playing program.)
Card, S., T. P. Moran, and A. Newell. 1983. The Psychology of Human-Computer Interaction. Hillsdale, N.J.: Erlbaum.
Chapman, R. L., J. L. Kennedy, A. Newell, and W. C. Biel. 1959. The systems research laboratory's air defense experiments. Manage. Sci. 5:250-69.
Freeman, P., and A. Newell. 1971. A model for functional reasoning in design. In Proceedings of the Second International Joint Conference on Artificial Intelligence. The British Computer Society, London, England, pp. 621-40.
Kruskal, J. B., Jr., and A. Newell. 1950. A Model for Organization Theory. Technical Report LOGS-103. Santa Monica, Calif.: RAND Corporation.
Laird, J., and A. Newell. 1983. A Universal Weak Method. Technical report, Computer Science Department, Carnegie Mellon University, Pittsburgh.
Laird, J., and P. Rosenbloom. 1992. In pursuit of mind: The research of Allen Newell. AI Mag. 13(4):17-45.
McCorduck, P. 1979. Machines Who Think. San Francisco: W. H. Freeman.
Medress, M. F., F. S. Cooper, J. W. Forgie, C. C. Green, D. H. Klatt, M. H. O'Malley, E. P. Newburg, A. Newell, D. R. Reddy, B. Ritea, J. E. Shoup-Hummel, D. E. Walker, and W. A. Woods. 1977. Speech understanding systems: A report of a steering committee. SIGART Newslett. 62:4-8.
Moore, J., and A. Newell. 1974. How Can Merlin Understand? In Knowledge and Cognition, ed. L. Gregg. Hillsdale, N.J.: Erlbaum.
Newell, A. 1951. Observations on the Science of Supply. Technical Report D-926. Santa Monica, Calif.: RAND Corporation.
Newell, A. 1955. The chess machine: An example of dealing with a complex task by adaptation. In Proceedings of the 1955 Western Joint Computer Conference. Institute of Radio Engineers, New York, pp. 101-108. (Also issued as RAND Technical Report P-620.)
Newell, A., ed. 1961. Information Processing Language V Manual. Englewood Cliffs, N.J.: Prentice-Hall.
Newell, A. 1962. Some problems of basic organization in problem-solving programs. In Self Organizing Systems, eds. M. C. Yovits, G. T. Jacobi, and G. D. Goldstein. Washington, D.C.: Spartan.
Newell, A. 1963. Learning, generality and problem solving. In Proceedings of the IFIP Congress-62, pp. 407-12.
Newell, A. 1965. Limitations of the current stock of ideas for problem solving. In Conference on Electronic Information Handling, eds., A. Kent and O. Taulbee. Washington, D.C.: Spartan.
Newell, A. 1966. On the representation of problems. Comput. Sci. Res. Rev., pp. 45-58.
Newell, A. 1968. The trip towards flexibility. In Bio-engineering--An Engineering View, ed. G. Bugliarello. San Francisco: San Francisco Press.
Newell, A. 1972. A theoretical exploration of mechanisms for coding the stimulus. In Coding Processes in Human Memory, eds. A. W. Melton and E. Martin. Washington, D.C.: Winston.
Newell, A. 1973. Production systems: Models of control structures. In Visual Information Processing, ed. W. C. Chase. New York: Academic Press.
Newell, A. 1982. The knowledge level. Artif. Intell. 18:87-127.
Newell, A. 1986. Awards for distinguished scientific contributions: 1985. Am. Psychol. 41:347-53.
Newell, A. 1990. Unified Theories of Cognition. Cambridge, Mass.: Harvard University Press.
Newell, A. 1992. Unified theories of cognition and the role of Soar. In Soar: A Cognitive Architecture in Perspective, eds. J. A. Michon and A. Anureyk. Dordrecht: Kluwer Academic Publishers.
Newell, A., and A. V. Baez. 1949. Caustic curves by geometric construction. Am. Phys. 29:45-47.
Newell, A., and G. Ernst. 1965. The search for generality. In Proceedings of IFIP Congress 65:195-208.
Newell, A., and J. B. Kruskal, Jr. 1951. Formulating Precise Concepts in Organization Theory. Technical Report RM-619-PR. Santa Monica, Calif.: RAND Corporation.
Newell, A., and H. A. Simon. 1956. The logic theory machine: A complex information processing system. IRE Trans. Inf. Theory IT-2:61-79.
Newell, A., and H. A. Simon. 1972. Human Problem Solving. Englewood Cliffs, N.J.: Prentice-Hall.
Newell, A., and H. A. Simon. 1976. Computer science as empirical inquiry: Symbols and search. Commun. Assoc. Comput. Machinery 19:111-26.
Newell, A., J. C. Shaw, and H. A. Simon. 1958. Chess-playing programs and the problem of complexity. IBM J. Res. Develop. 2:320-25.
Newell, A., J. C. Shaw, and H. A. Simon. 1960. Report on a general problem solving program. In Proceedings of the International Conference on Information Processing. UNESCO, Paris, pp. 256-64.
Newell, A., D. McCracken, G. Robertson, and R. Akscyn. 1982. ZOG and the USS Carl Vinson. Comput. Sci. Res. Rev. (Computer Science Department, Carnegie Mellon University, Pittsburgh.)
Newell, A., J. Barnett, J. Forgie, C. Green, D. Klatt, J. C. R. Licklider, M. Munson, R. Reddy, and W. Wood. 1971. Speech Understanding Systems: Final Report of a Study Group. Department of Computer Science, Carnegie Mellon University, Pittsburgh.
Newell, A., F. S. Cooper, J. W. Forgie, C. C. Green, D. H. Klatt, M. F. Medress, E. P. Neuberg, M. H. O'Malley, D. R. Reddy, B. Ritea, J. E. Shoup, D. E. Walker, and W. A. Woods. 1975. Considerations for a Follow-on ARPA Research Program for Speech Understanding Systems. Technical report, Computer Science Department, Carnegie Mellon University, Pittsburgh.
Polya, G. 1945. How to Solve It. Princeton, N.J.: Princeton University Press.
Polya, G. 1954. Mathematics and Plausible Reasoning. Princeton, N.J.: Princeton University Press.
Rosenbloom, P. S., and A. Newell. 1982. Learning by chunking: Summary of a task and a model. In Proceedings of AAAI-82 National Conference on Artificial Intelligence. AAAI, Menlo Park, Calif.
Shannon, C. E. 1950. Programming a digital computer for playing chess. Philosoph. Mag. 41:256-75.
Siewiorek, D., G. Bell, and A. Newell. 1981. Computer Structures: Principles and Examples. New York: McGraw-Hill.
Simon, H. A. 1993. Allen Newell: The entry into complex information processing. Artif. Intell. 59:251-59.
Simon, H. A. 1996. Models of My Life. Cambridge, Mass.: The MIT Press.
Walter, W. G. 1953. The Living Brain. New York: Norton.
Waterman, D. A., and A. Newell. 1971. Protocol analysis as a task for artificial intelligence. Artif. Intell. 2:285-318.


Biographical Memoirs

Oliver Selfridge 啟發Allen Newell

Oliver Selfridge

Oliver Selfridge, an Early Innovator in Artificial Intelligence, Dies at 82


查一下 在七零年代他們共寫AI 史
在寫摯友的回憶錄

Allen Newell
March 19, 1927 — July 19, 1992
By Herbert A. Simon

中 如此說明
In September 1954 Allen attended a seminar at RAND in which Oliver Selfridge of Lincoln Laboratories described a running computer program that learned to recognize letters and other patterns. While listening to Selfridge characterizing his rather primitive but operative system, Allen experienced what he always referred to as his "conversion experience." It became instantly clear to him "that intelligent adaptive systems could be built that were far more complex than anything yet done." To the knowledge Allen already had about computers (including their symbolic capabilities), about heuristic, about information processing in organizations, about cybernetics, and proposals for chess programs was now added a concrete demonstration of the feasibility of computer simulation of complex processes. Right then he committed himself to understanding human learning and thinking by simulating it. The student of organizations became a student of the mind.

In the months immediately following Selfridge's visit Allen wrote (1955) "The Chess Machine: An Example of Dealing with a Complex Task by Adaptation," which outlined an imaginative design for a computer program to play chess in humanoid fashion, incorporating notions of goals, aspiration levels for terminating search, satisfying with "good enough" moves, multidimensional evaluation functions, the generation of subgoals to implement goals, and something like best first search. Information about the board was to be expressed symbolically in a language resembling the predicate calculus. The design was never implemented, but ideas were later borrowed from it for use in the NSS chess program in 1958.





1977
1 Pamela McCorduck, Marvin Minsky, Oliver G. Selfridge, Herbert A. Simon: History of Artificial Intelligence. IJCAI 1977: 951-954


2006
5EEOliver G. Selfridge: Learning and Education: A Continuing Frontier for AI. IEEE Intelligent Systems 21(3): 16-23 (2006)
1993
4 Oliver G. Selfridge: The Gardens of Learning: A Vision for AI. AI Magazine 14(2): 36-48 (1993)

2008年12月1日 星期一

The Sciences of Design—Observations on an Emerging Field

Working Paper: The Sciences of Design—Observations on an Emerging Field

http://hbswk.hbs.edu/item/6061.html
Download the paper. Design sciences is a movement that affects not only the information systems discipline but also several allied disciplines, which must also contribute to its definition and participation. HBS professor Carliss Y. Baldwin and colleagues summarize and synthesize the positions, reflections, opportunities, and challenges expressed at the first doctoral consortium to explore the topic.

2008年11月1日 星期六

兩司馬

"良史莫無兩司馬 傳經唯有一公羊" 康有為

查一下中日文
似乎將 Simon 當成資訊科學家或經濟學家
其實Herbert Simon 何止兩司馬.....




這是搬家而已



記 Models of a Man 和回憶
2006-08-16 22:23:52 | 人氣(210)






「2月9日Simon, Herbert (Alexander) 謝世(1916-2001)。痛心、之後5年未能正式出版其著作翻譯本,慚愧。」

這樣講也不全對,我的月刊在2001年6月出版過專集。之後我每年會上網更新相關的資源,不料去年磁碟機毀損,今年忙其他事。

「我今天去台大圖書館,才知道Herbert A. Simon過世(2001)之後,the MIT Press 出一本 Models of a Man -- 朋友紀念他的。書可能都在經濟系的老師手中。」

我到書店目錄一看,簡直是舉世諸領域名人的作品。我當時實在應該試筆,寫一位透過閱讀與通信而知道皮毛的SIMON.



Models of a Man
Essays in Memory of Herbert A. Simon
Edited by Mie Augier and James G. March

Table of Contents and Sample Chapters

Herbert Simon (1916-2001), in the course of a long and distinguished career in the social and behavioral sciences, made lasting contributions to many disciplines, including economics, psychology, computer science, and artificial intelligence. In 1978 he was awarded the Nobel Prize in economics for his research into the decision-making process within economic organizations. His well-known book The Sciences of the Artificial addresses the implications of the decision-making and problem-solving processes for the social sciences.

This book (the title is a variation on the title of Simon’s autobiography, Models of My Life) is a collection of short essays, all original, by colleagues from many fields who felt Simon’s influence and mourn his loss. Mixing reminiscence and analysis, the book represents "a small acknowledgment of a large debt."

Each of the more than forty contributors was asked to write about the one work by Simon that he or she had found most influential. The editors then grouped the essays into four sections: "Modeling Man," "Organizations and Administration," "Modeling Systems," and "Minds and Machines." The contributors include such prominent figures as Kenneth Arrow, William Baumol, William Cooper, Gerd Gigerenzer, Daniel Kahneman, David Klahr, Franco Modigliani, Paul Samuelson, and Vernon Smith. Although they consider topics as disparate as "Is Bounded Rationality Unboundedly Rational?" and "Personal Recollections from 15 Years of Monthly Meetings," each essay is a testament to the legacy of Herbert Simon -- to see the unity rather than the divergences among disciplines.

Mie Augier is a postdoctoral fellow at Stanford University.

James G. March is a Professor Emeritus at Stanford University.



Essays in Memory of Herbert A. Simon
Edited by Mie Augier and James G. March

Preface xiii

Acknowledgments xv

Prologues

Herbert A. Simon, Scientist
Mie Augier and James G. March 3

He’s Just My Dad!
Katherine Simon Frank
Sample Chapter - Download PDF (49 KB) 33

I Modeling Man

Is Bounded Rationality Unboundedly Rational? Some Ruminations
Kenneth J. Arrow 47

On Rational Satisficing
William J. Baumol 57

Memorial to Herbert A. Simon
William W. Cooper 67

Consilience, Economic Theory, and the Legacy of Herbert A. Simon
Richard H. Day 75

Interdisciplinary Reasoning and Herbert Simon’s Influence
Yuji Ijiri 93

Beliefs and Tastes: Confessions of an Economist
David M. Kreps 113

The Best Is the Enemy of the Good
Roy Radner 143

The Hawkins and Simon Story Revisited
Paul A. Samuelson 153

Herbert A. Simon Opened My Eyes
Reinhard Selten 163

Monetary Rewards and Decision Cost in Strategic Interactions
Vernon L. Smith and Ferenc Szidarovszky 169

II Organizations and Administration

A Focus on Processes: Part of Herbert Simon’s Legacy
Philip Bromiley 183

Herbert Simon as Friend to Economists Out of Fashion
John Conlisk 191

Herbert A. Simon and the Education of Managers
William R. Dill 197

A Very Reasonable Objective Still Beyond Our Reach: Economics as an Empirically Disciplined Social Science
Giovanni Dosi 211

Lessons I Learned from Herbert A. Simon and His Friends: A Reflection on My Years at the Graduate School of Industrial Administration
Julian Feldman 227

Heuristics of Public Administration
Robert E. Goodin 233

"Warmly Yours, Herb"
Harold Guetzkow 251

Economics after Simon
Brian J. Loasby 259

Herbert Simon and Organization Theory: Lessons for the Theory of the Firm
Oliver E. Williamson 279

The "Easy Problem" Problem
Sidney G. Winter 297

III Modeling Systems

Causality, Decomposition, and Aggregation in Social Science Models
Albert Ando 307

Bounded Rationality and Decomposability: The Basis for Integrating Cognitive and Evolutionary Economics
Peter E. Earl and Jason Potts 317

Near-Decomposability, Organization, and Evolution: Some Notes on Herbert Simon’s Contribution
Massimo Egidi and Luigi Marengo 335

Herbert Simon
Axel Leijonhufvud 351

Rational Forecasting, Learning, and Decision Making
Charles C. Holt 355

Herbert Simon: Intellectual Magnet
Michael C. Lovell 365

Herbert Simon: Some Cherished Memories
Franco Modigliani 373

Herbert Simon and Production Scheduling
John F. Muth 377

IV Minds and Machines

"On a Different Plane"
Edward A. Feigenbaum 383

Striking a Blow for Sanity in Theories of Rationality
Gerd Gigerenzer 389

Attribute Substitution in Intuitive Judgment
Daniel Kahneman and Shane Frederick 411

Encounters with the Force of Herbert A. Simon
David Klahr 433

Strong Ideas
Kenneth Kotovsky 451

Heuristics for Scientific Discovery: The Legacy of Herbert Simon
Pat Langley 461

Herb Simon: A Recollection
Pamela McCorduck 473

Letter to Herb Simon
George A. Miller 481

Herbert Simon, David Hume, and the Science of Man: Some Philosophical Implications of Models
Joseph C. Pitt 483

Markets as Artifacts: Aggregate Efficiency from Zero-Intelligence Traders
Shyam Sunder 501

Personal Recollections from 15 Years of Monthly Meetings
Raul E. Valdes-Perez 521

Epilogue

A Soft Goodbye
James G. March 533

About the Contributors 537

Name Index

請教司馬賀管理教育
  讀者當知,司馬賀(H. A. Simon)為一了不起的教育家。他在40年代創設(主持)的卡內基理工工商學院,是世界頂尖的,也出了一些諾貝爾經濟學獎得主;這在《管理行為》中,有個案:「一所商學院的設計」。


  我在1999年2月寫信向他討教管理學如何教?因為根據西方名校傳統,入門課要由資深的教授教,才會有融會貫通的樂趣。我以前在東海大學化學系四年級 (1990)實驗一年,所得的結論是:「管理學無法教,因為智慧無法傳授。」上學期在中原試一學期「近代管理學趨勢」,期終改論文作業時,才知自己教的不 好,學生因而所學甚淺。我感到很深的挫折,就向司馬賀大師請教,如果他是我,會如何啟發後輩學者。

  他在3月5日(’99)給我一封信,其中有關管理學教育的一段,翻譯出來給大家參考:


  「如何教管理學(Administration)是個難題。目前美國大多數商學院所選出來的MBA(企管碩士)大多至少有兩、三年商業實務經驗,這樣 教起來就極不一樣了。要是學生沒有這種經驗背景,我總是試著要求學生把所就學的的大學看成一個組織,以組織學的話來看待大學中的事情,從而能把大學當作實 驗室的代替品。這並不改變你的論點(按:其實這是司馬賀在自傳《我生活的種種模式》中的看法):許多管理學上的原理(principles) 很簡單而又明白清楚;難在如何根據所信的原理養成力行的習慣。然而,我們不該從中得出結論說,:習慣是不可改的。(按《管理行為》中有專節討論組織的習慣 與創新。)」

(我按照他的建議,希望學生研究中原大學,這可把他們難倒了,……我記得當時談很多電腦化註冊和選課系統…..)


  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 -207)

  Tawney真是名家,他對竹爭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)


>我最後讀過高先生的文章,是他1982年80歲在美國寫毋忘斯人。劉振老師說: “….自美寄台,不僅情意誠懇,增光泉壤,益覺前輩風範,堪為後人法則。”

Quality Times No.149, June 5, 07;品質時報 第146期:07年6月5日(週二 )
鍾漢清Hanching Chung

通信
主旨

proficiency 與proficiency testing


海事 proficiency 熟練
航空太空名詞 event proficiency 事件熟悉度;flying proficiency 飛行熟練度; proficiency check 熟練測驗 ; proficiency flight 熟練飛行
科學教育 occupational proficiency 職業熟練度
電機工程 COPE; console operator proficiency examination 可佩(操作能力)考試

**** 品質專門用語(Justing Kuo郭展銓 先生作)
proficiency testing
能力試驗(Proficiency Testing ) 的主要功能是評估與確認實驗室測試或校正能力,並可加強實驗室內部的品保作業;它有別於現場評鑑,為實驗室認證程序中的一部份。加上WTO 與APEC 致力於國際貿易自由化的趨勢下,測試與校正結果的追溯性與可比較性愈來愈受關注。因此,能力試驗在消除技術性障礙的符合性評鑑中扮演著重要角色。在 CNLA* 共通規範6.3 中也強調,參加能力試驗活動的最低要求為(1) 於申請認證時,應提供申請範圍內至少一次能力試驗良好表現的資料;(2) 獲得認證後,至少每三年對認證範圍的每一類別有一次能力試驗活動的良好表現。

*現在稱為TAF,之前是稱CNLA.

中華民國實驗室認證體系 (Chinese National Laboratory Accreditation ,簡稱CNLA.。
民國七十九年,經濟部推動中華民國實驗室認證體系 (Chinese National Laboratory Accreditation ,以下簡稱 CNLA) ,辦理實驗室認證工作。民國八十六 年經濟部成立中華民國品質管理及環境管理認證委員會﹝九十年三月更名 為「中華民國認證委員會」 (Chinese National Accreditation Board ,以下簡稱 CNAB) ﹞,辦理管理系統、產品、稽核員驗證機構及稽核員訓練機構等認證業務。

為提供單一窗口之認證服務,合併 CNL A 與 CNA B 業務,九十一年七月三十一 日成立本會籌備處,負責各項籌備工作,包含基金之募集及申請文件之準備等。 之後依據「經濟事務財團法人設立許可及監督準則」 ,於九十二年九月十七日完 成法定登記,定名 為「財團法人全國認證基金會」 財團法人全國認證基金會( Taiwan Accreditation Foundation , 簡寫為 TA F。本會成立之初,基金來自民 間機構捐助金額占百分之五十五。九十二年十月一日起本會正式運作並辦理實驗 室認證業務,九十三年一月一日起亦正式辦理 CNAB 業 務。為使國內相關單位明 瞭此政策轉變,經濟部於九十二年十一月二十七日經標字第 09202617610 號函知 各界 CNLA 業務已轉移本會辦理。此外,於民國九十三年增闢檢驗機構認證服務業務。

***** hc與justing 談
Hi 鍾老師,
真的想請您提筆,多寫些相關戴明博士資料.或召回案例.
因為我不懂日文,在螺絲行業,一些非常珍貴資料卻都是日文.
---
沒問題 要提醒那些最切身
qualityraiwan 當機 現在移師品質論壇

日文資料可傳來 我或Hans或可幫忙
----
Dear 鍾老師,
OK,因為台灣螺絲技術實在落後日本至少5年以上.一些舊技術都是早期日本留下.
但最近日本早就改善不用,但台灣還是在用.是最近幾年有與日本做生意,他們過來看才發現.
所以實在是漏氣十足.
最切身問題,莫過於現在被許多認證模糊品質真正涵義.許多企業為了認證可以有生意,結果索賠更多.
*******
The W. Edwards Deming Institute® 今天(2007/6/4)的每日戴明名言為:
Quote from W. Edwards Deming:
Are we noted for quality?
---
這”we” 應該指美國人。這也許是他上課問參與者。
晚年的Deming曾希望以「愛國者」留名:畢竟,他是1900-93年的人,我們或許同意20世紀是美國世紀。
你看他們真的地大物博,知識上領先【以抽樣等統計技術而言,何嘗不是領先國…..】
-----
noted
adjective
known by many people, especially because of particular qualities:
Summerhill school is noted for its progressive policies.【這所學校Summerhill台灣翻譯成夏山學校】
She`s not noted for her patience (= She is not a patient person).

(from Cambridge Advanced Learner`s Dictionary)
The W. Edwards Deming Institute® is a nonprofit organization that was founded in 1993 by noted consultant Dr. W. Edwards Deming.


*****
*****
主旨:省能與經濟
KELVIN

似乎要提出自己的、經過認證的 經濟計算:(環保團體與立委呼籲民眾換掉家裡的鎢絲燈泡,改用省電燈泡,一年可省下74億元電費,由於省電燈泡的價格,比一般的鎢絲燈泡高出10倍之多, 再加上市面上燈泡良莠不齊,連消基會都不敢保證市面上所販售的省電燈泡,到底能替消費者省下多少錢?)
-----
Dear H.C.,

其實推廣省電燈泡不只是成本的考量而已,是環保政策的問題;但由於省電燈泡的推
廣,太過簡易,可能反而誤導消費者對於燈具的選擇。
若純由省電這個因素來考量,省電燈泡的確是較鎢絲燈泡省電(注意比較對象是鎢絲燈
泡),同時由於其壽命較長,發熱較少,故就消費者自己面臨之成本,應是較低的;總
體政策去推廣省電燈炮(澳洲要立法禁止鎢絲燈泡),個人認為主要是為了減緩溫室效
應。使用鎢絲燈泡,不但耗電,也由於其發熱問題,會導致空調的用電也增加;而電力
取得的效率是很低的,且每度電的取得會產生0.69Kg的CO2;而省電燈泡最大的環保問
題是熒光劑的回收。但鎢絲燈泡的顯色性較佳,對眼睛應是較佳的;而部份的農蓄業要
的是鎢絲燈泡的光色及溫度。
但省電燈泡真的省電嗎?其實不然,但由於省電燈泡可以直接安裝在原有的鎢絲燈泡的
燈座上,故是減少用電的最簡易方法,但大家可以參考一下省電燈泡的包裝盒子,可以
發現省電燈泡的功率因素都在60%以下,換具話說,每具燈具有40%以上的電輸入是無效
的;同時也可能造成的諧波的產生。事實上,省電燈泡就是熒光燈,只是將其形狀改變
而已,因此大家只要將其視為熒光燈即可。
當然熒光燈具是無法缺安定器而獨立運作;基本上在熒光燈上的電子式安定器(非電感
式),其功率因素可以在98%左右,但為何省電燈泡只有60%以下呢?成本考量是也。這
也衍生了省電燈泡(熒光燈具皆同)的問題,安定器品質及色衰。壽命不長大都是安定器
問題,色衰是熒光燈管問題。不幸,由於市場價格競爭,且大家無法在購置燈泡時,確
認其品質,因此就產生了連消基會也不敢保證市面上所販售的省電燈泡的問題。
回歸使用面,個人認為省電燈泡就如一般的電器產品般,自從大陸品主導市場後,選擇
的問題就很重要了;因此若燈光使用時間長者,能用熒光燈就使用熒光燈,長優於短,
直優於彎,曲率大優於小,直徑小優於大;只使用高頻電子式安定器,然後配合高顯色
性及適合的色溫選擇。
由於燈光的選擇也是一門複雜的學問,因此也是為何我只敢涉入熒光燈具的節能,同時
也為何在眾多的廠商中,選擇了長庚生物科技。至於經濟計算,則因如消基會所言,品
質難以確認,難以計算;若依據理論值,則恐使用者換了之後,覺得無如此效果。

Kevin
*****

『三呆部落格』開台2007/04/18 18:05:tw.myblog.yahoo.com/wang-sundye
這已是我們編寫晃三兄的退休紀念文集(Essays In Honor of (and by) W. H. S. )近年的事了。
其實,他的退休第一年可能比以前更忙碌呢!
我去年弄「Sony電池回收事件的『品質論壇』」,因為是開始,所以WHS不怎麼參與,大家就LEARNING BY DOING的方式混過去。今年的第一炮,其實應該不是現在從事的 Lean /Six Sigma(LSS),不過,LSS在WHS手下用力最勤,然而,我們第一次體會WHS的行事做風之優缺點。

*****
David to HC
謝謝。 近來 Xquality 偶有新人留言 但更常有閒雜人等廣告留言 喜怒參半
給小皓哥 是我父愛的唯一出口 不寫出來 會悶死人 多謝關心
**---
奇怪 @WIKI的SEARCH 不見 幾乎無法運作
HC
---
我想這是atwiki的問題 我們無能為力
---
如何寫信給Ono 先生
---
他的email: info@atwiki.com
不過這種系統問題 常常會自動修正 尤其這次的問題不小
****




趨勢

由均勻設計方法(UD)說起由均勻設計方法(UD)說起;

我在1972-73年(大二)就從吳玉印先生*學田口流的實驗計畫【這一流派影響台灣產業界可能最大。在1970年代中,它為Toyota汽車公司所拒, 不過在美日還繼續發揮其影響力】。1977-78我在英國拿的學位是「統計與作業研究之碩士」,受到比較正統的西方統計學訓練。
這兒是台灣。讀者多半不知道大陸一小部分人津津樂道的均勻設計(Uniform Design 簡稱 UD),它是一種試驗設計方法(Experimental Design Method),稱為均勻設計(Uniform Design)或均勻設計試驗法(Uniform Design Experimentation)。
對產業人而言,實驗設計/計畫(簡稱DOE等)只是工具。可是它可以從統計學甚或從數學上來講—如UD。

均勻設計是方開泰教授(Fang, K.T)和王元院士在1978年共同提出,二十年來,在諸多行業的眾多領域中獲得成功的應用。方開泰教授寫了均勻設計的講義(Two Monographes),在八十年代中和九十年代初的十年間,為均勻設計的首批應用者提供了方便。1994年經修改後正式出版,成為科普教材。

方開泰和馬長興編寫的《正交和均勻實驗設計》等二書,一直是正交設計和均勻設計方面的開山之作和扛鼎之作。除中國數學會均勻設計分會之外,中國已有專門UD組織:中國均勻設計學會。

方開泰是中國正交設計和均勻設計的泰山北斗,在全世界範圍內都有很高的聲譽。底下是他最得意的美國Ford汽車公司2002年邀請他去講UD,作為他們Six Sigma 設計的應用例:
-

Performance of Uniform Designs• The Ford Motor Company has used the UD for developing new engines. Agus Sudjianto, Engineering Manager in FORD invited me to visit the FORD in 2002. His letter of invitation wrote:
&quotIn the past few years, we have tremendous in using Uniform Design for computer experiments. The technique has become a critical enabler for us to execute `design for Six Sigma` to support new product development, in particular, automotive engine design. Today, computer experiments using uniform design have become standard practices at Ford Motor Company to support early stage of product design before hardware is available." It shows that there is a big potential applications of the uniform design in Six Sigma development.

其實,這舉動,在西方重視知識的大公司並不是什麼了不起的事,不過多少知道中國熱情應用自己的方法之運動,多少有點成績。


*****
我的 Herbert Simon 2007

http://tw.myblog.yahoo.com/hcdeming

2008年10月8日 星期三

《管理行為 第四版》翻譯說明

......我希望《管理行為 第四版》的讀者能夠在讀完它後,再進一步讀《關於人類事務的科學》,並把「管理與組織行為」等思想,與設計理論課題建立起「持續改善」的對應。

翻譯說明

本書的「出發的語言」是英文,「到達的語言」是中文,希望譯本能作到「信」、「達」。本書譯者加註,都用[ ]括號來表示。

我在譯文中,用標的(goals)、目標(objectives)、目的(purposemeans-ends中之ends)來區分,一般的讀者會覺得不習慣。這種例子,在這樣的近代管理科學百科全書中是可能的,譬如說,本書有一關鍵字眼叫representation,我譯成「再現模式」(表徵),這是我們在解決問題時,必須將它的一些特性重新組合,以新角度來看它。作者也說,有些人在不同環境中,會喜歡用〝企業文化〞來表示。另外,在認知心理學中,人們談production也許是指專家系統中的〝提示〞。

我國有些學者慣將Adminitrative Science譯為〝行政學〞,可是行政學被認為只是〝公共事務〞管理的學問。中譯本根據原著精神,並徵詢了包括作者本人和諸多專家的意見,此詞譯為〝管理學〞或〝管理的科學〞。其實,〝管理〞是清朝的一官職,一百多年前即用〝管理〞來譯administration,本書初版時,〝企業〞管理等名聲不太好,而且作者以為公共事務的管理,是遠比企業管理更複雜而困難的,所以取法乎上,沿用舊名。最近五十年來企業管理理論突飛猛進,風行全球,作者在本版中將公營、民營組織一以貫之,作了深入探討,基本原理數十年來未變。

英文是可以比較精確的,單數或複數所指的不太一樣,所以讀者應善用「索引」,例如〝value〞和〝values在譯文中都譯為〝價值〞。

鍾漢清先生負責本書的統合譯校,所以本書文責由他負,雖然他根據本書第三版(由楊礫,韓春立及徐立於1988譯出)作了大規模的校改。特別要感謝林益正、及卓弘哲先生在電腦文字處理上的幫忙,與蔡士魁先生的校稿。

19998月末,台北,鍾漢清作於華人戴明學院)

2008年9月30日 星期二

西蒙 Simon管理行為

"烏德爾發言人西蒙(Sam Simon)表示﹐在昨日接到的電話和電子郵件中﹐有66%-75%的選民都反對救助議案。有的選民不接受任何救助計劃﹐有的選民只是反對週一那個救助計劃。在選民提出的問題中﹐他們擔心救助計劃在幫助面臨止贖的購房者方面做的太少。"WSJ


這則消息可知 在中國 Simon稱為 西蒙
有趣的是美國總統發言人稱"接到的電話和電子郵件中"...
Simon很早(1960S-1970s)就提過
現在處於資訊氾濫成災時代
要爭取顧客的attention 最重要

參考

管理行為

 ──管理型組織中決策過程的研究

Administrative Behavior

 ──A Study of Decision-Making Processes in Administrative Organizations

Copyright � 1945, 1947, 1957, 1976, 1997 by Herbert A. Simon

All rights reserved.

ISBN:0-684-83582-7

Originally pulished by The Free Press. Chinese translatin arranged by Big-Apple-Mori, Taiwan. 中文繁體字版由大蘋果公司協助取得版權

原著者:Herbert A. Simon

原出版社:The Free Press

譯者:鍾漢清等

發行者:華人戴明學院

出版者:華人戴明學院

出版兼發行:鍾漢清

地址:臺北市(106)新生南路3段88號2樓

電話:(02)23625776,(02)23650127

傳真:(02)23650128

免費管理學月刊網址:http://www.deming.com.tw

郵政劃撥:18827960 戶名:鍾漢清

出版日期:西元1999年9月 初版(1----2000)

定價:新台幣600元

ISBN:957-97401-0-0






2008年9月21日 星期日

Leo Strauss"學派"與 Simon

Encounters and Reflections: Conversations with Seth Benardete


這篇我附一篇紐約時報書介 其中談到他老師Leo Strauss (芝加哥大學政治學系)
近十幾年來 大陸開始介紹這"學派"

早在60年前 這學派有人猛烈評擊 Simon (芝加哥大學政治學系 博士)的理性論
Simon說 要回這些評論
必須再寫一本書
而他忙於其他創作

2008年9月3日 星期三

Nobel Laureate in Economic Sciences (1978)

Herbert A. Simon, CMU, Nobel Laureate in Economic Sciences (1978)
現在可能找不到這說法 因為1978是否叫 CMU
不管這
重要的是SIMON在回憶錄說瑞典的專家早說他一定是諾貝爾經濟學獎的前十名

你或許注意到Nobel Laureate in Economic Sciences (1978)

****


An Engine, Not a Camera
How Financial Models Shape Markets
By Donald MacKenzie

"In one lifetime modern finance theory has revolutionized the arts of canny investing. MacKenzie knows this exciting story, and he tells it well."
—Paul A. Samuelson, MIT, Nobel Laureate in Economic Sciences (1970)

September 2008 ISBN 978-0-262-63367-3
$23.00/£14.95 (PAPER)
Table of Contents Available


2008年9月1日 星期一

Herbert Simon的書房人像

Ernst Cassirer

卡西爾《盧梭康德歌德》 北京:生活·讀書·新知三聯書店 2002
康德書房內只掛盧梭像

Herbert Simon的書房可能掛6人像
包括 愛因斯坦 羅斯福 等等

那天該找出談一下
不過 我與Simon談過他不欣賞歌德
似乎還有點動火氣 無知的我

2008年8月30日 星期六

Technology That Outthinks Us


紐約時報這篇報導 可能是Simon 在60年前的極端看法--當時根本無法見容於"學界"--那 "人"的尊嚴安在

Technology That Outthinks Us: A Partner or a Master?

Vernor Vinge has been urging his fellow humans to get smarter by collaborating with computers.

2008年8月20日 星期三

Administrative Man Faces the Quality Transformation: Comparing the Ideas of Herbert A. Simon and W. Edwards Deming

這篇有空該瞧一瞧

The American Review of Public Administration, Vol. 24, No. 1, 67-84 (1994)
DOI: 10.1177/027507409402400104

Administrative Man Faces the Quality Transformation: Comparing the Ideas of Herbert A. Simon and W. Edwards Deming

John H. Little

Virginia Polytechnic Institute and State University

Is Deming's quality management just another management fad? Examination of its philosophical underpinnings shows that it is based in a new theoretical framework that places it at a cross-roads for organization theory and design that is as momentous as Simon's development and application of decision theory and positivistic science. Failure to understand Deming's "system of profound knowledge" has meant an undervaluing of his major contribution to the theory of organizations.

2008年8月19日 星期二

一場"天安門革命"

讀 廣告 "上海至瑞士的直達航班﹐現已開闢。一周七天﹐每日航班任選﹐更利於您從瑞士轉機前往超過42個歐洲目的地。"

想六四 (1989) Simon 從德國直飛北京
去體會一場"天安門革命"

這段經歷是出身政治學的Simon 千載難逢的
所以回憶陸 Models of My Life 約5頁記之

大陸當然"刪之"
我向 Simon說
這種政權....

2008年8月15日 星期五

昔日對於統計數字的無知

"...In 2007, Japan's average life expectancy again hit a new record. For the last 23 years, Japanese women have had the longest life span in the world. The figure now stands at 85.99 years. Japanese men rank third in the world, at 79.19 years...."--Lesson in longevity from the summer life cycle

統計數字適合於全體,無法預測個人的實際情況。
這樣簡單的道理,還是要有機會自行體會才行。

2000我可能查過美國預後統計值,說如果Simon80歲,至少可活約86歲。….所以後來我從The Economist 知道 Simon去世(約2周後),頗震驚。`我當時投 Learning Organization/ LO 討論文章之末尾,都附Simon之名引言來紀念他,可惜這些都沒留底。2008.8.15

2008年8月14日 星期四

Herbert Simon 青春鄉野之旅

Herbert Simon 晚年接到我讚美他回憶錄中一章
講他年青時的鄉野之旅
實在像史詩般 為一傑作
他說 要再讀讀
重溫舊夢



The Young Man and the Lakes

By JOHN J. MILLER
August 14, 2008; Page D7

Seney, Mich.

When Ernest Hemingway was a young writer in the 1920s, he pinned a map of northern Michigan to the wall of his room in Paris. It probably came in handy as he wrote his first batch of short stories. Although he was born and raised in Oak Park, Ill., Hemingway spent the summers of his boyhood in the woods and lakes of what Michiganders call "Up North." They provide the settings for most of his early tales.

[photo]
The Granger Collection
Ernest Hemingway fishing in Michigan in 1920.

One of these yarns, however, has traditionally puzzled anyone who reads it and then checks a map. "Big Two-Hearted River" is probably Hemingway's first great contribution to literature, an example of nature writing at its finest and perhaps America's best fishing story, especially for readers who remember that Moby Dick didn't have gills.

The narrative begins with Nick Adams, Hemingway's protagonist and alter-ego, having just gotten off the train in Seney, a town in Michigan's Upper Peninsula. He hikes into the wilderness and fishes for trout. The problem is that the Two-Hearted River lies about 20 miles north of Seney and flows into Lake Superior. On foot, it's virtually impossible to get there with Nick's apparent speed. The Fox River -- a perfectly good stream for brook trout -- runs right through the town, on its way to Lake Michigan.

Hemingway visited Seney with a couple of friends in 1919. Wouldn't he have just fished the Fox?

On the East Coast, every hamlet that can claim "Washington slept here" eagerly does so, for both patriotic and commercial reasons. In parts of Michigan, there's a Hemingway corollary: He slept here (at the family cottage on Walloon Lake), ate here (at Jesperson's Restaurant in Petoskey), and fished here (lots of places).

Michigan is so proud of its ties to Hemingway that the state humanities council has just wrapped up the Great Michigan Read, a literacy initiative that used "The Nick Adams Stories" as its focal point. For the past year, schools and libraries have sponsored discussion groups, a traveling exhibit, and even a Hemingway look-alike contest.

In Seney, a small historical museum includes a display with the gear Nick is described as having brought on his journey, such as a can of pork and beans and a can of spaghetti that he mixes together for a meal. Last month, the museum acquired a rowboat that Hemingway is said to have used. "Some days we won't get a soul in here, and the next day we might get 15," says Candace Blume, the curator.

In a letter to Gertrude Stein, Hemingway described "Big Two-Hearted River" as a story in which "nothing happens." Nick Adams walks out of Seney, makes camp, and goes fishing. Beneath this mundane surface, however, swims a potent personal drama.

Something bothers Nick. The text doesn't say what. As an author, Hemingway routinely withheld what would seem to be key information; his stories are often exercises in decipherment. A close reading of "Big Two-Hearted River" reveals that Nick's trek into the backwoods of Michigan is about much more than hooking trout.

Hemingway was famous for short declarative sentences, and "Big Two-Hearted River" is full of them: "It had been a hard trip. He was very tired. That was done. He had made his camp. He was settled. Nothing could touch him. It was a good place to camp. He was there, in the good place. He was in his home where he had made it."

Nothing could touch him? The good place? Clearly, Nick has issues.

The standard interpretation is that Nick is a shell-shocked military veteran who has returned from the grinding combat of World War I. Kenneth Lynn, one of Hemingway's biographers, has suggested that the author was disturbed by a quarrel with his mother.

The ultimate source of Nick's troubles hardly matters. The interest lies in how he tries to tame them through ritualistic activities: Step by step, Hemingway portrays him pitching a tent, brewing coffee, and collecting grasshoppers for bait.

Anyone who wants to discover precisely where Nick went fishing won't find a conclusive answer in "Big Two-Hearted River." In Seney, however, Don Reed is happy to help with a few ideas. He's the township supervisor and owner of the Fox River Motel. About once a week during the summer, he says, someone calls or drops by and wants to fish where Hemingway did.

"Trout fishermen don't like to reveal their best spots," he says. "Maybe that's why Hemingway named his story after the Two-Hearted. Everyone around here knows he fished the Fox."

That's the local lore. The truth is that in 1919 Hemingway didn't need a fishing license -- and years later he confessed to using literary license: "The change of name was made purposely, not from ignorance or carelessness but because Big Two-Hearted River is poetry."

Opinions still vary about whether he fished the Fox itself, a swampy branch to the east, or both. "All we can do is approximate," says Mr. Reed.

Hemingway once boasted that on his actual trip to Seney, he and his friends reeled in 200 trout. It would be tough to repeat their catch today, given Michigan's daily limit of five keepers. Yet the fishing may have improved: The riverbanks continue to recover from an era of mass logging, and new tree canopies shade the water. The planet may be warming, but the Fox is possibly cooling -- and trout prefer cool water.

Almost a century later, Hemingway's good place arguably has become a better place. Just don't expect a trout fisherman to tell you that.

Mr. Miller writes for National Review.