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ABSTRACTIn order to bring home to management science students a proper overview of the field, the author undertakes the investigation of real world problems using the methods of systems analysis and operations research. This paper will describe the results of one such investigation. In addition to describing the classroom experiment, a model for speculative stock market investment will be developed. Finally, a simulation model for reviewing investor performance will be outlined.

The effects of knowledge of results on discussions directed toward improving performance in work groups are examined for complex man-machine relationships.

One of the most interesting and challenging problems to confront those interested in the learning process in recent years is the entire area of the self-instructional device, or "teaching machine." The idea of the teaching machine is not new, for Pressey (49) in 1926 wrote concerning a device he had developed, and at the same time indicated that he had had such a device in mind for "a number of years." After this introduction by Pressey, the teaching machine movement lay dormant for several years with only an occasional article written that had any direct relationship to this area. This was not to last indefinitely, however, because during the past ten years the interest has gradually been growing to the point that at the present time this movement demands consideration. It is difficult to identify precisely why this has been the case, but a few reasons might be suggested. The demand on education is greater now than it has ever been before (47, 57): there are more people wanting education, there are more students receiving education, the percentage of school-age persons participating is increasing, and the teacher-pupil ratio is not remaining at a desirable level. To further intensify this problem, much more is being demanded. from education in the general areas of curriculum and desired levels of competence. Since this presents the educational system with the obvious task of keeping abreast of these demands, the educator has been forced to search for more effective and efficient methods of instruction. Glaser (28) in his review suggests that the trend is toward closer cooperation and coordination of effort between "educational psychology" and the "science of learning." The experimentalist and the learning theorist are working more closely together on training and learning problems than they ever have before. A final reason for this increased interest, according to Holland (31), is that in the past the interest has been largely on the device itself, but in recent years this has shifted to focus upon the fact that a person's behavior can be altered in situations outside of the laboratory by the application and utilization of certain psychological principles. These same principles can be incorporated in the teaching machine. Not only is this movement intriguing, but it presents a great challenge, for there are a great number of problems, first to be identified, and second to be solved. From all indications this interest will not dissipate, but rather will become more universal with widespread implications for the student, the teacher, the administrator, the psychologist, and the parent (4, 18, 47). The implications are not confined to t hose associated with a school setting, for as Skinner (57) indicates there is additional application in home study, industrial training, military training, and special education of the exceptional individual. No doubt there are others but this will serve to illustrate the potentially wide-spread effects.

AbstractLearning curves have been used extensively for predictive purposes in the airframe and other industries. In many instances this has led to erroneous results because analysts failed to extend learning curve theory and develop adequate analytical techniques in the turbulent regime of the cost history characterizing these industries. It is this area where a series of design changes induces a series of perturbations whose turbulence intensity is a function of the frequency of occurrence and magnitudes of the design changes under consideration.In Ref. 1, a series of formulations amenable to machine programming was developed for the accurate determination of perturbed unit costs. This development was based on additions of new learning having a constant slope.In this discussion, the development of Ref. 1 will be generalized by developing formulas for the addition of new effort having variable slopes. Consideration will also be given to the expressions involving elementary unit cost expressions so that cumulative average and cumulative total values can readily be obtained from existing experience curve tables. Conversely, the problem of determining the magnitudes of design changes and the slopes of new effort from graphical data will also be considered.

ABSTRACTThis article is based on the belief that students should learn the computational aspects of decision science techniques outside the classroom, thus freeing the instructor from this task and enabling him to devote more class time to focusing on the theoretical and/or application‐oriented elements which will take students further in their learning experience. An experiment was designed to determine the learning value of one way of achieving this, namely the use of a Learning Center where students learned individually using a programmed instruction learning machine. The research involved both graduates and undergraduates learning the fundamentals of PERT—some with the learning machine approach while others were involved in more conventional pedagogies. Surprising success was discovered relative to both the approach and the quality of the PERT program used, with far‐reaching implications regarding futuristic approaches to decision science education.

1. Introduction -- 2. Movement -- 3. Models -- 4. Dynamic Systems -- 5. Signals -- 6. Control -- 7. Automatic Control -- 8. Optimal Control -- 9. Automata -- 10. The Computer -- 11. Adaptation -- 12. Games -- 13. Learning -- 14. Large Systems -- 15. Operational Control -- 16. The Brain -- 17. Organised Systems -- 18. Man and Machine -- 19. Outline of Future Prospects.

Artificial intelligence is neither a myth nor a threat to man. It relates to a serious attempt to develop machine methods for dealing with some of the perplexing problems that should, in all justice, be delegated to machines but which now seem to require the exercise of human intelligence. 2 fundamentally diff approaches to the problem are being explored, one aimed at a complete understanding of the intellectual process involved & the other aimed at duplicating the assumed specific behavior of the brain. The first approach concerns itself with such matters as search, pattern recognition, learning, planning & induction; the second involves a study of the behavior of random nets. It is fair to conclude that artificial intelligence promises to reduce rather than to augment technological unemployment. AA.

A biological model, the nervous system, is applied to a man–machine system in order to stimulate interdisciplinary thought. Conceptual similarities between engineering, communication, and psychology are examined as they relate to the application of this model. If system designers incorporate afferent (Exercise) and efferent (Evaluation) subsystems so that feedback capability exists, system "learning" becomes feasible. This biological model is operational in the Site Production and Reduction System (SPARS), a computer system providing support for system training in the Semi-Automatic Ground Environment (SAGE) System. SPARS problem production (Exercise) and data reduction (Evaluation) capabilities are described and implications for future system technology are presented.

1 Theory of Algorithms and Discrete Processors -- 1. Introduction -- 2. Discrete Processors -- 3. Examples of Discrete Processors -- 4. Computers and Discrete Processors -- 5. Systems of Algorithmic Algebras -- 6. Application of Algorithmic Algebras to Transformations of Microprograms -- 7. Equivalence of Discrete Processors -- 8. Equivalence of Automata with Terminal States Relative to an Automaton without Cycles -- 9. Specific Cases of Solutions to the Equivalence Problem -- 10. Conclusions -- References -- 2 Programming Languages -- 1. Introduction -- 2. The Basic Linguistic Nature of Programming Languages -- 3. Programming Languages and Semiotics -- 4. The Formal Definition of Programming Lan guages -- 5. The Definition of Programmable Automata and their Languages -- 6. Parallel Concurrent Processes -- 7. Machine Languages -- 8. Special and General-Purpose Algorithmic Languages -- 9. Special Problem-Oriented Languages -- 10. Simulation Languages -- 11. Conversational Languages -- 12. Conclusion -- References -- 3 Formula Manipulation—The User's Point of View -- 1. Introduction -- 2. Different Types of Formula Manipulation Systems -- 3. Toward a Mathematical Utility -- 4. The Formula Manipulation Language Symbal -- 5. The Syntax of Symbal -- 6. The Basic Symbols and Syntactic Entities -- 7. Expressions -- 8. The Remaining Parts of the Language -- 9. Standard Variables -- 10. Techniques and Applications -- 11. Summary -- References -- 4 Engineering Principles of Pattern Recognition -- 1. Introduction -- 2. Basic Problems in Pattern Recognition -- 3. Feature Selection and Preprocessing -- 4. Pattern Classification by Distance Functions -- 5. Pattern Classification by Potential Functions.. -- 6. Pattern Classification by Likelihood Functions -- 7. Pattern Classification by Entropy Functions.. -- 8. Conclusions -- References -- 5 Learning Control Systems -- 1. Introduction -- 2. Trainable Controllers -- 3. Reinforcement Learning Control Systems -- 4. Bayesian Learning in Control Systems -- 5. Learning Control Systems Using Stochastic Approximation -- 6. The Method of Potential Functions and its Application to Learning Control -- 7. Stochastic Automata as Models of Learning Controllers -- 8. Conclusions -- Appendix. Stochastic Approximation—A Brief Survey -- References -- Author Index.

Observations on factor costs in public elementary & secondary Sch's, & on efforts made to measure the productivity of educ'al systems. In current dollars, the instructional expenditures per pupil per day have risen from $ 0.357 in 1929-1930, to $ 1.311 in 1957-1958. The increase for the same period in constant dollars (1949-1950 = 100), is from $0.748 to $0.915. The mean/average of all earned income in the US is used as the deflator for the salary portion of the instructional budget, under the assumption that relative changes in teachers' pay affect the calibre of staff &, hence, the output of Sch systems. Progressive Sch authorities now appear to respond to the demand for higher standards of performance by seeking to make changes in the instructional process via curriculum revision, redeployment of staff, & the use of new physical devices. Costbenefit analysis in educ is not sufficiently developed to make close judgments on the efficiency of alternative combinations of inputs, primarily because unobtrusive means of observing the teaching process are not yet available. Should teaching machines come into widespread use, however, it will be possible to link the individual instruments to a central data bank. Examination of the data obtained may allow fairly sophisticated types of systems analysis to be applied in educ. AA.

Artificial intelligence is neither a myth nor a threat to man. It relates to a serious attempt to develop ma chine methods for dealing with some of the perplexing prob lems that should, in all justice, be delegated to machines but which now seem to require the exercise of human intelligence. Two fundamentally different approaches to the problem are being explored, the one aimed at a complete understanding of the intellectual processes involved and the other aimed at du plicating the assumed specific behavior of the brain. The first approach concerns itself with such matters as search, pattern recognition, learning, planning, and induction; the second ap proach involves a study of the behavior of random nets. It is fair to conclude that artificial intelligence promises to reduce rather than to augment technological unemployment.

Book reviewed in this article: An Unusual Statement
Economic and Social Development: A Process of Social Learning by Edgar S. Dunn, Jr. Baltimore Society as a Machine
The Pentagon of Power (The Myth of the Machine, Vol. 2) by Lewis Mumford. A Scottish Moralist
Robert M. Maclver on Community, Society and Power: Selected Writings, edited and with an Introduction by Leon Bramson. A Dream Breaks Down
The Alliance That Lost Its Way: A Critical Report on the Alliance for Progress by Jerome Levinson and Juan de Onis. Study of Thailand
Marketing and Development: The Thailand Experience by Dole A. Anderson. Indonesian Development
Indonesia: Resources and Their Technological Development, edited by Howard W. Beers. Europe's Comeback
The Recovery of Europe: From Devastation to Unity by Richard Mayne. Modernizing Turkey
Turkish Economic, Social, and Political Change: The Development of a More Prosperous and Open Society by Edwin J. Cohn. The Mighty Oceans
The Last Resource: Man's Exploitation of the Oceans by Tony Loftas. Analysis of Employment Growth
The Metropolitan Economy: The Eployment Expansion by Thomas M. Stanback, Jr. and Richard V. Knight. Frustration in Communication
The Unheavenly City: The Nature and Future of Our Urban Crisis by Edward C. Banfield. Forces Affecting Urban Society
Cities of the Prairie: The Metropolitan Frontier and American Politics by Daniel J. Elazar. Political Relationships
Urban Politics in America by John H. Baker. Race and Foreign Policy
Urban Politics in America by John H. Baker. Social Disunity
The Prospects for Revolution: A Study of Riots, Civil Disobedience, and Insurrection in Contemporary America by Ralph W. Conant.

□ Although all that has been said above may seem rather complicated and expensive, an application of these ideas should substantially simplify the task of learning effectively from college assignments. In applying these suggestions to a student's particular situation and to the evaluation of his need for new equipment, the following basic points made in this presentation should be considered: 1) Everything is read at the fastest comprehendable speed; 2) A small, portable machine is useful for recording lectures and for reading during available bits of time throughout the day; 3) Ideas from what is read are condensed and recorded onto separate reels for each major aspect of each subject (low frequency beeps are used to identify sections or dates on tapes); 4) Notes, rather than the original works, are used for review; 5) Face-to-face reading is kept to a minimum; 6) Braille reading is also kept to a minimum; and 7) Paid or volunteer readers are used to do as much "mechanical" work as possible.