The organizers of this conference have told us that we should write at least 25 pages of manuscript, but that we may produce as many pages more as we wanted. Perhaps they did not envisage the possible consequences, but we have taken their words at face value.This paper has implied a vast amount of work and archeological activities. We are grateful to SIGPLAN for defining a task to which resources had to be allocated by our institutions and which forced us to write down an account of our work from 1961 to 1971. While we are writing this preface, those years are very much alive to us. We realize that we lived through this period in a state of semimadness, a combination of very hard work, frustration, and euphoria.

The development of the SIMULA languages

This paper describes an improved method for investment decision making. The method, which is called the stochastic decision tree method, is particularly applicable to investments characterized by high uncertainty and requiring a sequence of related decisions to be made over a period of time. The stochastic decision tree method builds on concepts used in the risk analysis method and the decision tree method of analyzing investments. It permits the use of subjective probability estimates or empirical frequency distributions for some or all factors affecting the decision. This application makes it practicable to evaluate all or nearly all feasible combinations of decisions in the decision tree, taking account of both expected value of return and aversion to risk, thus arriving at an optimal or near optimal set of decisions. Sensitivity analysis of the model can highlight factors that are critical because of high leverage on the measure of performance, or high uncertainty, or both. The method can be applied relatively easily to a wide variety of investment situations, and is ideally suited for computer simulation.

https://www.jstor.org/stable/info/2627813

Stochastic Decision Trees for the Analysis of Investment Decisions

Time and state descriptions form the core of a simulation model representation. The historical influence of initial application areas and the exigencies of language implementations have created a muddled view of the time and state relationships. As a consequence, users of simulation programming languages work in relative isolation; model development, simulation application, model portability, and the communication of results are inhibited and simulation practice fails to contribute to the recognition of an underlying foundation or integrating structure. A model representation structure has been forged from a small set of basic definitions which carefully distinguish the state and time relationships. This paper focuses on the coordination of the time and state concepts using \"object\" as the link. In addition to clarifying the relationships, the structure relates the concept of \"state-sequenced simulation\" to the variations in time flow mechanisms. In conclusion, some speculations are offered regarding alternative algorithms for time flow mechanisms.

The time and state relationships in simulation modeling

The deterministic sequencing problem is reviewed from the points of view of variety, models, context, methodology, and current state of the art. The relationship between the theory of sequencing and other areas of control is illustrated with the relationship of sequencing to inventory. The “cyclical EMQ (Economical Manufacturing Quantities)” problem is discussed and new formulations are presented which promise a computationally feasible resolution of this outstanding problem.

The machine sequencing problem – review and extensions

The purpose of this paper is to present a comparison of some computer simulation languages and of some of the packages by which each is implemented. Some considerations involved in comparing software packages for digital computers are discussed in Part I. The issue is obvious: users of digital computers must choose from available languages or write their own. Substantial costs can occur, particularly in training, implementation and computer time if an inappropriate language is chosen. More and more computer simulation languages are being developed: comparisons and evaluations of existing languages are useful for designers and implementers as well as users.The second part is devoted to computer simulation and simulation languages. The computational characteristics of simulation are discussed with special attention being paid to a distinction between continuous and discrete change models. Part III presents a detailed comparison of six simulation languages and packages: SIMSCRIPT, CLP, CSL, GASP, GPSS and SOL. The characteristics of each are summarized in a series of tables. The implications of this analysis for designers of languages, for users, and for implementers are developed.The conclusion of the paper is that the packages now available for computer simulation offer features which none of the more general-purpose packages do and that analysis of strengths and weaknesses of each suggests ways in which both current and future simulation languages and packages can be improved.

Computer simulation—discussion of the technique and comparison of languages

Systems engineers have come to recognize simulation as a valuable tool in their work. However, writing simulation programs can be a difficult, time consuming task requiring intricate and extensive programming. For simulation to be most useful, it must be possible to carry out a simulation quickly and be possible to change the simulation easily as the system design proceeds.

This paper describes a general purpose simulation program designed to simplify the task of simulating systems. It is applicable to a wide variety of important problems. The program features a simple block diagram language with which to describe the system to be simulated. Given this description, the program will automatically simulate the system.

G. Gordon

Papers

A general purpose systems simulator