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IEEE Standard Glossary of Modeling and Simulation Terminology

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IEEE Std 6103-1989


Sponsor

Standards coordinating Committee, of the EEJ3ComputerSociety

Approved January 1,1989

IE3EStandardBoard

0 Copyright 1989 by

The Institute of Electrical and Electronics Engineers, Inc 345 East 47th Street, New York, NY 10017, USA

No part of this publication may be reproduced in any form, in an electronic retrieval system or otherwise,

without the prior written permission of the publisher.


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(This Foreword is not a part of lEEE Std 610.3-1989, IEEE Standard Glossary of Modeling and Simulation Terminology.)

The computer field is continuing to expand. New terms are being generated and new meanings are being adopted for existing terms. The IEEE Computer Dictionary project was undertaken t o document this vocabulary. Its purpose is to identify terms currently in use in the computer field and to establish standard definitions for these terms. The dictionary is intended to serve as a useful reference for those in the computer field and for those who come into contact with computers either through their work or in their everyday lives.

The completed dictionary will contain terms from each of the following areas: Computer Hardware, Software Engineering, Mathematics of Computing, Theory of Computation, Computer Applications, Artificial Intelligence, Data Management, Image Processing and P a t t e r n Recognition, Modeling and Simulation, Computer Graphics, Computer Networking, Computer Languages, and Computer Security and Privacy. This glossary contains the terms related t o Modeling and Simulation.

Every effort has been made to use definitions from established standards in this dictionary. When existing standards were found to be incomplete, unclear, or inconsistent with other entries in the dictionary, however, new, revised, or composite definitions have been developed.

At the time this glossary was approved, the following people formed the steering committee of the Computer Dictionary working group:

Anne K . Geraci, Leader, Modeling and Simulation Subgroup

Other subgroup leaders: Jane Radatz, Chairperson

John B. Lane Louise McMonegal Jose' Muiioz Hugh Porteous

Charles Russell Paul Schmid Frederick Springsteel

Ernest Stalder William Sutcliffe Mary Yee John Young

Other working group members who contributed to this glossary were a s follows:

Jerry Banks H. Ronald Berlack James Bradley William Dupras John h t z Vir1 Haas Walter Karplus

Michael Kong Gary Lindsay Philip Marriott Russell Mike1 Thomas Perrone Donald Phillips

Pierre Pignal Max Schindler Leonard W. Seagren Sonja Shields Barry Stoll Fermin Trujillo Charlotte Wales

The sponsor for the Computer Dictionary project is the IEEE Computer Soceity Standards Coordinating Committee. At the time this standard was approved, the committee had the following membership:

T. R. Whittemore J. C. Appleyard R. Bednarik S. Bogdanowicz

J. H. Harlow J. E. Holladay F. E. Kimsey S. B. Kuznetsov S. R. Lambert

F. D. Myers J. Osterhout 0. Petersons J. E. Thomas


Special representatives to the Computer Dictionary working group were as follows:

Frank Jay, Advisor from the IEEE Standards Office

Rollin Mayer, Liaison with Accredited Standards Committee X3K5

The following persons were on the balloting committee that approved this document for submission to the IEEE Standards Board:

David A. Ackley Gordon Adshead J. Aldridge Kenneth R. Anderson James H. Aylor Osman Balei Michael A. Bame J im Barbera H.Ronald Berlack Matt Biewer Samuel J. Biondo Kathleen L. Briggs Jerry R. Brookshire A. Winsor Brown Fletcher J. Buckley James H. Burrows Margaret K. Butler Harry Carl Miguel A. Carrio Asheem Chandna John P. Chihorek Frank A. Chimenti Antonio Cicu Frangois Coallier Christopher M. Cooke Guy L. Copeland Gail A. Cordes Manuel A. D'Abreu M. A. Daniels Bostjan Derganc J im Dobbins Frank J. Douglas Michael Dutton Rheodore J. Dzik John D. Earls Leo G. Egan Don Eller John W.Fendrich Glenn S . Fields A. M. Foley Julian Forster Michel Galinier Daniel J. Garvin Anne K. Geraci M. Ghiassi Sumit Ghosh A. Shirley Gloss-Soler J. Garth Glynn John A. Goetz B. K. Gogia Julio Gonzalez-Sanz

J. Kaye Grau Andrej Grebenc Steve Grout L. M. Gunther Harry E. Hansen John D. Hallenstein Clark M. Hay William E. Hefley Terry L. Hengl John Hines Sam Horvitz Henry B. Hoyle David Johnson Walter Karplus Peter P. Klopfenstein Robert A. Kosman Josep A. Krupinski Thomas M. Kurihara Robin B. Lake Lak M. Lam John B. Lane Glen G. Langdon Gregory N. Larsen F. C. Lim Deiter Look John K. Lowell Andy Mahindru Henry A. Malec Judy Malsbury Philip C. Marriott Roger J. Martin Ken Massey T. Matsubara Robert McBeth Paul Menchini Jerry Mersky Russell D. Mike1 Peter 0. Mikes Paul J. Miller Charles S . Mooney William A. Mullen Jose' Muiioz Myron L. Nack Saied Najafi Matt Narotam Geraldine R. Neidhart Michael T. Perkins Donald J. Pfeiffer J . G. Phippen John N. Postak Jane W. Radatz Jock A. Rader

John 0. Neilson Dennis E. Nickle S. R. Norman Larry O'Connell Tuncer I. Oren Kirit P. Parikh Curtis H. Parks Thomas Parrish Max J. Schindler Paul E. Schmid Gregory D. Schumacher Leonard W. Seagren Gerard P. Shabe Moe Shahdad Robert W. Shillato Ronald Short Stanley F. Ralph Krithivasan Ramamritham Charles E. Rauch Meir Razy Thad L.D. Regulinski Daniel Rosich Stephen R. Schach Victor Shtern David M. Siefert Lynn J. Simms Jacob Slonim Lyle Smith Edward A. Straker Oryal Tanier Robert Thibodeau Booker T. Thomas Paul U. Thompson Valentin W. Tirman Tuyet-Lan Lydia Tran Glendon R. Trebble Jon Turino Mark-Rene Uchida Ralph Wachter Charlotte Wales Bruce W. Walker John P. Walter Paul A. Willis Andrew F. Wilson Paul R. Work c. P. w u Alfred W. Yonda Natalie C. Yopconka Janusz Zalewski Mark Zaremba Don Zeleny


When the IEEE Standards Board approved this standard on January 1,1989, it had the following membership:

Dennis Bodson, Chairman Marc0 W. Migliaro,Vice Chairman Andrew G. Salem, Secretary

Arthur A. Biaisdell Fletcher J. Buckley Allen L. Clapp James M. Daly Stephen R. Dillon Donald C. Fleckenstein Eugene P. Fogarty Jay Forstep Thomas L. Hannan

Kenneth D. Hendrix Theodore W. Hissey, Jr John W. Horch David W. Hutchins Frank D. Kirschner Frank C. Kitzantides Joseph L. Koepfingerl Edward Lohse

John E. May, Jr Lawrence V. McCall L. Bruce McClung Donald T. Michael* Richard E. Mosher Stig Nilsson L. John Rankine Gary S. Robinson Donald W. Zipse

*Member Emeritus

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PAGE SECTION

1 . Scope ....................................................................................................... 7

2 . Dictionary Structure ..................................................................................... 7

3 . Definitions for Modeling and Simulation Terms ................................................... 7


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This glossary defines terms in the field of Modeling and Simulation. Topics covered include general modeling and simulation concepts, types of models and simulations, modeling and simulation variables, game theory, and queueing theory.

Terms were excluded if they were considered to be parochial to one group or organization; company proprietary or trademarked; multi-word terms whose meaning could be inferred from the definitions of the component words; or terms whose meaning in the computer field could be directly inferred from their standard English meaning. Some technical terms that appear in the definitions are defined in other P610 glossaries and are not included as entries here.

2. Dictionary structure

Entries in the dictionary are arranged alphabetically. An entry may consist of a single word, such as "model" or a phrase, such as "analytical model." Phrases are given in their natural order (analytical model) rather than in reversed order (model, analytical).

Blanks precede all other characters i n alphabetizing. Hyphens and slashes are treated as blanks. Alternative spellings are shown in parentheses; for example, "human centered (human centred)."

If a term has more than one definition, the definitions are numbered. The order of the definitions does not imply preference or fre- quency of use. In most cases, noun definitions

are given first, followed by verb and adjective definitions as applicable. Examples and notes have been added to clarify selected definitions.

The following cross-references are used t o show a term's relationship to other terms in the dictionary: (1) Contrast with refers to a term with an

opposite or substantially different meaning. (2) Syn refers to a synonymous term. (3) See also refers to a related term. (4) See refers to a preferred term or t o a term

where the desired definition can be found.

3. Definitions for Modeling and Simulation Terms

absorbing Markov chain model. A Markov chain model that has at least one absorbing state and in which from every state i t is possible to get to a t least one absorbing state.

absorbing state. In a Markov chain model, a state that cannot be left once it is entered. Contrast with: nonabsorbing state.

academic simulation. See: instructional simulation.

activity. In modeling and simulation, a task that consumes time and resources a n d whose performance is necessary for a system to move from one event to the next.

activity-based simulation. A discrete simulation that represents the components of a system as they proceed from activity t o activity; for example, a simulation in which

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IEEE Std 610.3-1989 IEEE STANDARD GLOSSARY OF

a manufactured product moves from station to station in an assembly line.

analog simulation. (1) A simulation that is designed to be executed on an analog system. (2) A simulation that is designed to be executed on a digital system but that represents an analog system. (3) A simulation of an analog circuit. Contrast with: digital simulation. See also: hybrid simulation.

analytical model. A model consisting of a set of solvable equations; for example, a system of solvable equations that represents the laws of supply and demand in the world market.

behavioral model. See: black box modeL

black box model. A model whose inputs, outputs, and functional performance are known, but whose internal implementation is unknown o r irrelevant; for example, a model of a computerized change-return mechanism in a vending machine, in the form of a table that indicates the amount of change to be returned for each amount deposited. S y n : behavioral model; inputloutput model. Contrast with: glass box model.

boundary condition. The values assumed by the variables in a system, model, o r simulation when one or more of them is a t a limiting value or a value at the edge of the domain of interest. Contrast with: final condition; initial condition.

built-in simulation. A special-purpose simulation provided as a component of a simulation language; for example, a simulation of a bank that can be made specific by stating the number of tellers, number of customers, and other parameters.

built-in simulator. A simulator that is built-in to the system being modeled; for example, an operator training simulator built into the control panel of a power plant such that the

system can operate in simulator mode o r in normal operating mode.

coenetic variable. In modeling, a variable that affects both the system under consideration and that system's environment.

computational model. A model consisting of well-defined procedures that can be executed on a computer; for example, a model of the stock market, in the form of a set of equations and logic rules.

computer-based simulation. A simulation that is executed on a computer. Syn: machine- centered simulation. Contrast with: human- centered simulation.

computer simulation. A simulation of the operation of a computer. See also: computer- based simulation.

concrete model. A model in which a t least one component represented is a tangible object; for example, a physical replica of a build- ing.

condition. The values assumed a t a given instant by the variables in a system, model, or simulation. See also: boundary condition; final condition; initial condition; state.

conditional event. A sequentially dependent event that will occur only if some other event has already taken place. See also: time- dependent event.

constant. A quantity or data item whose value cannot change. Contrast with: variable.

continuous change model. See: continuous model.

continuous model. (1) A mathematical or computational model whose output variables change in a continuous manner; that is, in changing from one value to another, a variable can take on all intermediate values; for example, a model depicting the rate of air flow over an airplane wing. Syn: continuous-variable model. (2) A model of a system that behaves in a continuous manner. Contrast with: discrete model.

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MODELING AND SIMULATION TERMINOLOGY IEEE

Std 610.3-1989

continuous simulation. A simulation that uses a continuous model.

continuous-variable model. See: continuous model.

correspondence. See: fidelity.

critical event simulation. A simulation that is terminated by the occurrence of a certain event; for example, a model depicting the year-by-year forces leading up to a volcanic eruption, t ha t is terminated when the volcano in the model erupts. See also: time- slice simulation.

dependent variable. A variable whose value is dependent on the values of one or more independent variables. Contrast with: independent variable.

descriptive model. A model used to depict the behavior or properties of an existing system or type of system; for example, a scale model or written specification used t o convey to potential buyers the physical and performance characteristics of a computer. Syn: representational model. Contrast with: prescriptive model

deterministic. Pertaining to a process, model, or variable whose outcome, result, or value does not depend on chance. Contrast with: stochastic.

deterministic model. A model in which the results are determined through known relationships among the states and events, and in which a given input will always produce the same output; for example, a model depicting a known chemical reaction. Contrast with: stochastic model.

digital simulation. (1) A simulation that is designed to be executed on a digital system. (2) A simulation that is designed t o be executed on an analog system but that represents a digital system. (3) A simulation of a digital circuit. Contrast with: analog simulation. See also: hybrid simulation.

discrete change modeL See: discrete modeL

discrete event model. See: discrete modeL

discrete model. (1) A mathematical o r computational model whose output variables take on only discrete values; that is, in changing from one value to another, they do not take on the intermediate values; for example, a model t h a t predicts an organization's inventory levels based on varying shipments and receipts. S y n : discrete change model; discrete event model; discrete variable model. (2) A model of a system that behaves in a discrete manner. Contrast with: continuous model. See also: state machine.

discrete simulation. A simulation that uses a discrete model.

discrete variable model. See: discrete modeL

dynamic model. A model of a system in which there is change, such as the occurrence of events over time or the movement of objects through space; for example, a model of a bridge that is subjected to a moving load to determine characteristics of the bridge under changing stress. Contrast with: static model.

empirical. Pertaining to information that is derived from observation, experiment, or experience.

emulate. To represent a system by a model that accepts the same inputs and produces the same outputs as the system represented. For example, to emulate an &bit computer with a 32-bit computer. See also: simulate.

emulation. (1) A model that accepts the same inputs and produces the same outputs as a given system. See also: simulation. (2) The process of developing or using a model as in (1).

emulator. A device, computer program, or system that performs emulation.

endogenous variable. A variable whose value is determined by conditions and events within a given model. Syn: internal variable. Contrast with: exogenous variable.

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IEEE Std 610.3-1989 JZEE STANDARD GLOSSARY OF

environment. The external objects, conditions, and processes that influence the behavior of a system.

environmental simulation. A simulation that depicts all or part of the natural or man- made environment of a system; for example, a simulation of the radar equipment and other tracking devices that provide input to an aircraft tracking system.

equilibrium. See: steady state.

equilibrium condition. See: final condition.

error model. (1) A model used to estimate or predict the extent of deviation of the behavior of an actual system from the desired behavior of the system; for example, a model of a communications channel, used t o estimate the number of transmission errors that can be expected in the channel. (2) In software evaluation, a model used to estimate or predict the number of remaining faults, required test time, and similar characteristics of a system. Syn: error prediction madeL

e m r prediction model. See: error modeL

event. (1) An occurrence that causes a change of s ta te in a simulation. See also: conditional event; time-dependent event. (2) The instant in time a t which a change in some variable occurs.

event-driven simulation. See: event-oriented simulation.

event-oriented simulation. A simulation in which attention is focused on the occurrence of events and the times a t which those events occur; for example, a simulation of a digital circuit that focuses on the time of state transition. Syn: even t-driven Simulation; event-sequenced simulation.

event-sequenced simulation. See: event- oriented simulation.

exogenous variable. A variable whose value is determined by conditions and events external to a given model. Syn: external variable. Contrast with: endogenous variable.

external variable. See: exogenous variable.

fast time. (1) Simulated time with the property that a given period of actual time represents more than that period of time in the system being modeled; for example, in a simulation of plant growth, running the simulation for one second may result in the model advancing time by one full day; that is, simulated time advances faster than actual time. (2) The duration of activities within a simulation in which simulated time advances faster than actual time. Contrast with: real time; slow time.

fidelity. The degree of similarity between a model and the system properties being modeled. Syn: correspondence. See also: model validation.

final condition. The values assumed by the variables in a system, model, o r simulation a t the completion of some specified duration of time. Syn: equilibrium condition. Contrast with: boundary condition; initial condition.

final state. The values assumed by the state variables of a system, component, or simulation a t the completion of some specified duration of time. Contrast with: initial state.

game. A physical or mental competition in which the participants, called players, seek to achieve some objective within a given set of rules. See also: game theory.

game theory. (1) The study of situations involving competing interests, modeled in terms of the strategies, probabilities, actions, gains, and losses of opposing players in a game. See also: management game; war game. (2) The study of games to determine the probability of winning given various strategies.

gaming simulation. See: simulation game.

glass box model. A model whose internal implementation is known and fully visible; for example, a model of a computerized

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MODELING AND SIMULATION TERMINOLOGY lEEE

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change-return mechanism in a vending machine, in the form of a diagram of the circuits and gears that make the change. Contrast with: black box model. Syn: white box modeL

graphical model. A symbolic model whose properties are expressed in diagrams; for example, a decision tree used to express a complex procedure. Contras t w i t h : mathematical model; narrative model; software model; tabular modeL

heuristic. Pertaining to experimental, espe- cially trial-and-error, methods of problem- solving. Note: The resulting solution may not be the most desirable solution to the problem.

human-centered simulation (human-centred simulation). A simulation carried out by people; for example, a simulation in which a human participant operates a mock-up of an instrument to establish a good ergonomic design of the instrument console. Contrast with: computer-based simulation. See also: human-machine simulation.

human-machine simulation. A simulation carried out by both human participants and computers, typically with the human participants asked to make decisions and a computer performing processing based on those decisions; for example, a simulation in which humans make automotive design decisions and a computer determines and displays the results of those decisions.

hybrid simulation. A simulation, portions of which are designed to be executed on an analog system and portions on a digital system. Interaction between the two portions may take place during execution. See also: analog simulation; digital simulation.

iconic model. A physical model that looks like the system being modeled; for example, a non-functional replica of a computer tape drive used for display purposes. See also: scale model.

identity simulation. A simulation in which the roles of the participants are investigated or defined; for example, a simulation that identifies aircraft based on their physical

profiles, speed, altitude, and acoustic characteristics.

in-basket simulation. A simulation in which a set of issues is presented to a participant in the form of documents on which action must be taken; for example, a simulation of an unfolding international crisis as a sequence of memos describing relevant events and outcomes of the participant's actions on previous memos.

independent variable. A variable whose value is not dependent on the values of other variables. Contrast with: dependent variable.

initial condition. The values assumed by the variables in a system, model, or simulation a t the beginning of some specified duration of time. Contrast with: boundary condition; final condition.

initial state. The values assumed by the state variables of a system, component, or simulation a t the beginning of some specified duration of time. Contrast with: final state.

input/output modeL See: black box modeL

instructional simulation. A simulation intended to provide an opportunity for learning or t o evaluate learning or educational potential; for example, a simulation in which a mock-up of an airplane cockpit is used to train student pilots. Syn: academic simulation; tutorial simulation.

internal variable. See: endogenous variable.

interval-oriented simulation. A continuous simulation in which simulated time is advanced in increments of a size suitable to make implementation possible on a digital system.

laboratory simulation. A simulation developed and used under highly controlled conditions; for example, a simulation of a medical technique implemented in the controlled environment of a laboratory.

lag variable. (1) In a discrete simulation, a variable that is an output of one period and an input for some future period. (2) In an analog simulation, a variable that

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is a function of an output variable and that is used as input to the simulation to provide a time delay response or feedback. S y n : lagged variable; serially-correlated variable.

lagged variable. See: lag variable.

lead variable. (1) In a discrete simulation, a variable that is an output of one period and that predicts what the output of some future period will be. (2) In an analog simulation, a variable that is a function of an output variable and that is used as input to the simulation t o provide advanced time response or feedback.

machine-centered simulation (machine-centred simulation). See: computer simulation.

machine simulation. A simulation that is executed on a machine. See also: computer simulation.

man-centered simulation (mancentred simulation). See: human-centered simulation.

man-machine simulation. See: human-machine simulation.

management game. A simulation game in which participants seek t o achieve a specified management objective given preestablished resources and constraints; for example, a simulation in which participants make decisions designed t o maximize profit in a given business situation and a computer determines the results of those decisions. See also: war game.

Markov chain. A discrete Markov process.

Markov chain model. A discrete, stochastic model in which the probability that the model is in a given state a t a certain time depends only on the value of the immediately preceding state. Syn: Markov model. See also: semi-Markov model.

Markov model. See: Markov chain model.

Markov process. A stochastic process which assumes that in a series of random events,

the probability for occurrence of each event depends only on the immediately preceding outcome. See also: semi-Markov process.

mathematical model. A symbolic model whose properties a re expressed in mathematical symbols and relationships; for example, a model of a nation's economy expressed as a set of equations. Contrast with: graphical model; narrative model; software model; tabular modeL

mock-up. A full-sized structural, but not necessarily functional, model buil t accurately to scale, used chiefly for study, testing, or display; for example, a full-sized model of a n airplane displayed in a museum. See also: physical model,

model. (1) An approximation, representation, or idealization of selected aspects of the structure, behavior, operation, o r other characteristics of a real-world process, concept, or system. Note: Models may have other models as components. (2) To serve a s a model as in (1). (3) To develop or use a model as in (1).

model validation. The process of determining the degree to which the requirements, design, o r implementation of a model are a realization of selected aspects of the system being modeled. See also: fidelity. Contrast with: model verification.

model verification. The process of determining the degree of similarity between the realization steps of a model; for example, between the requirements and the design, or between the design and its implementation. Contrast with: model validation.

Monte Carlo method. In modeling and simulation, any method tha t employs Monte Carlo simulation to determine estimates for unknown values in a deterministic problem.

Monte Carlo simulation. A deterministic simulation in which random statistical sampling techniques are employed such that the resul t determines estimates for unknown values.

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Std 610.3-1989

narrative model. A symbolic model whose which the end result is considered more properties are expressed in words; for important than the process by which it is example, a written specification for a obtained; for example, a simulation of a computer system. Syn: verbal-descriptive radar system that uses methods far different model. Contrast with: graphical model; from those used by the actual radar, but mathematical model; sof tware model; whose output is the same. Contrast with: tabular model proceas-oriented Simulation.

natural model. A model that represents a system by another system that already exists in the real world; for example, a model that uses one body of water to represent another.

network theory. The study of networks used to model processes such a s communications, computer performance, routing problems, and project management.

nodal izat ion. (1) The set of nodes within a system being modeled. (2) The process of developing the nodes as in (1 1.

node. A single entity that is represented in a mathematical model; for example, in a model of a nuclear reactor, a water pump or section of pipe.

nonabsorbing state. In a Markov chain model, a state that can be left once it is entered. Contrust with: absorbing state.

normative model. A model that makes use of a familiar situation to represent a less familiar one; for example, a model that depicts the human cardiovascular system by using a mechanical pump, rubber hoses, and water.

numerical model. (1) A mathematical model in which a set of mathematical operations are reduced to a form suitable for solution by a simpler methods such a s numerical analysis o r automation; for example, a model in which a single equation representing a nation's economy is replaced by a large set of simple averages based on empirical observations of inflation rate, unemployment rate, gross national product, and other indicators. (2) A model whose properties are expressed by numbers.

outcome-oriented simulation. A simulation in

period. The time interval between successive events in a discrete simulation.

Petri net. An abstract, formal model of information flow, showing static and dynamic properties of a system.

physical model. A model whose physical characteristics resemble the physical characteristics of the system being modeled; for example, a plastic or wooden replica of an airplane. Contrast with: symbolic model. See also: iconic model; mock-up; scale model.

predictive model. A model in which the values of future states can be predicted or are hypothesized; for example, a model that predicts weather patterns based on the current value of temperature, humidity, wind speed, and so on a t various locations.

prescriptive model. A model used to convey the required behavior or properties of a proposed system; for example, a scale model or written specification used to convey to a computer supplier t he physical and performance characteristics of a required computer. Contrast with: descriptive model.

probabilistic model See: stochastic modeL

process model. A model of the processes per- formed by a system; for example, a model that represents the software development process as a sequence of phases. Contrast with: structural model.

process-oriented simulation. A simulation in which the process is considered more important than the outcome; for example, a model of a radar system in which the objective is to replicate exactly the radar's operation, and duplication of its results is a lesser concern. Contrast with: o u t c o m e - oriented simulation.

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prototype. A preliminary type, form, or instance of a system that serves as a model for later stages or for the final, complete version of the system.

queue. In queueing theory, a set of zero or more entities waiting to be serviced by a service facility.

queueing model. A model consisting of service facilities and entities waiting in queues to be served; for example, a model depicting teller windows and customers at a bank.

queueing network model. A model in which a process is described as a network in which each node represents a service facility rendering a given type of service and a queue for holding entities waiting to be served; for example, a model depicting a network of shipping routes and docking facilities a t which ships must form queues in order to unload their cargo.

queueing theory. The study of queues and the performance of systems that service entities that are organized into queues. See also: queueing model; queueing network modeL

random. Pertaining to a process or variable whose outcome or value depends on chance or on a process that simulates chance, often with the implication that all possible outcomes or values have an equal probability of occurrence; for example, the outcome of flipping a coin or executing a computer-pro- grammed random number generator.

real time. In modeling and simulation, simulated time with the property that a given period of actual time represents the same period of time in the system being modeled; for example, in a simulation of a radar system, running the simulation for one second may result in the model advancing time by one second; that is, simulated time advances at the same rate a s actual time. Contrast with: fast time; slow time.

reliability model. A model used to estimate, measure, o r predict the reliability of a system; for example, a model of a computer system, used to estimate the total down time that will be experienced.

representational model. See: descriptive model.

scale model. A physical model that resembles a given system, with only a change in scale; for example, a replica of an airplane one tenth the size of the actual airplane.

scenario. A set of initial conditions and a sequence of events used to develop, test, or apply a system, model, or simulation.

semi-Markov model. A Markov chain model in which the length of time spent in each state is randomly distributed.

semi-Markov process. A Markov process in which the duration of each event is randomly distributed.

serially-correlated variable. See: lag variable.

simuland. The system being simulated by a simulation.

simulate. To represent a system by a model that behaves or operates like the system. See also: emulate.

simulated time. Time as represented within a simulation. Syn: virtual time. See also: fast time; real time; slow time.

simulation. (1) A model that behaves or operates like a given system when provided a set of controlled inputs. Syn: simulation model. See also: emulation. (2) The process of developing or using a model as in (1).

simulation clock. A counter used to accumu- late simulated time.

simulation game. A simulation in which the participants seek to achieve some agreed- upon objective within an established set of rules. For example, a management game, a war game. Note: The objective may not be to compete, but t o evaluate the participants, increase their knowledge concerning the simulated scenario, o r achieve other goals. Syn: gaming simulation.

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Std610.3-1989

simulation language. A programming another in a system, component, or simula- language used to implement simulations. tion.

simulation model. See: simulation.

simulator. A device, computer program, or system that performs simulation.

slow time. (1) Simulated time with the property that a given period of actual time represents less than that period of time in the system being modeled; for example, in a simulation of the internal workings of a computer, running the simulation for one second may result in the model advancing time by only a microsecond; t ha t is, simulated time advances slower than actual time. (2) The duration of activities within a simulation in which simulated time advances slower than actual time. Contrast with: fast time; real time.

state variable. A variable that defines one of the characteristics of a system, component, or simulation. The values of all such variables define the state of the system, component, or simulation.

static model. A model of a system in which there is no change; for example, a scale model of a bridge, studied for its appearance rather than for its performance under varying loads. Contrast with: dynamic model.

steady state. A situation in which a model, process, or device exhibits stable behavior independent of time. Syn: equilibrium.

stimulate. To provide input to a system in order to observe or evaluate the system's response.

ical model; narrative model; tabular model.

stabilized-variable model. A model in which some of the variables are held constant and the others are allowed to vary; for example, a model of a controlled climate in which humidity is held constant and temperature is allowed to vary.

state. (1) A condition or mode of existence that a system, component, or simulation may be in; for example, the pre-flight state of an aircraft navigation program or the input state of given channel. (2) The values assumed a t a given instant by the variables that define the characteristics of a system, component, or simulation. Syn: system state. See also: final state; initial state, steady state.

state machine. A model of a system in which all values are discrete, as in a digital computer.

state transition. A change from one state to

stochastic model. A model in which the results are determined by using one o r more random variables to represent uncertainty about a process or in which a given input will produce an output according to some statistical distribution; for example, a model that estimates the total dollars spent a t each of the checkout stations in a supermarket, based on probable number of customers and probable purchase amount of each customer. Syn: probabilistic model. See also: Markov-chain model. Contrast with: deterministic model.

structural model. A model of the physical or logical structure of a system; for example, a model that represents a computer network as a set of boxes connected by communication lines. Contrast with: process model.

symbolic model. A model whose properties are expressed in symbols. Examples include graphical models, mathematical models, narrative models, software models, and tabular models. Contrast with: physical model.

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IEEE Std 610.3-1989

system. A collection of components organized to accomplish a specific function o r set of functions.

system state. See: state.

tabular model. A symbolic model whose properties are expressed in tabular form; for example, a truth table that represents the logic of an OR gate. Contrast with: graphical model; mathematical model; narrative model; software model

time variable. A variable whose value represents simulated time or the state of the simulation clock.

time-dependent event. An event that occurs a t a predetermined point in time or after a predetermined period of time has elapsed. See also: conditional event.

t ime-interval simulation. See: time-slice Simulation.

time-slice simulation. (1) A discrete simulation tha t is terminated after a specific amount of time has elapsed; for example, a model depicting the year-by- year forces affecting a volcanic eruption over a period of 100 000 years. Syn: time- interval simulation. See ako: critical event simulation. (2) A discrete simulation of continuous events in which time advances by intervals

IEEE STANDARD GLOSSARY OF

chosen independent of the simulated events; for example, a model of a time multiplexed communication system with multiple channels transmitt ing signals over a single transmission line in very rapid succession.

tutorial simulation. See: instructional simulation.

variable. A quantity or data item whose value can change. Contrast with: constant. See also: dependent variable; independent variable; state variable.

verbal-descriptive model. See: narrative model.

virtual time. See: simulated time.

w a r game. A simulation game in which participants seek to achieve a specified military objective given preestablished resources and constraints; for example, a simulation in which participants make battlefield decisions and a computer determines the resul ts of those decisions. See also: management game.

white box modeL See: glass box modeL

yoked variable. One of two or more variables that are dependent on each other in such a manner that a change in one automatically causes a change in the others.

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Std 610.3-1989

The following organizations supported employee participation in the development of this standard:

Atlantic Consultants Apollo Computer, Inc Burroughs Wellcome Company Computer Technology Associates, Inc Dutchess Engineering Company Electronics Design Magazine Endinboro University of Pennsylvania Ford Aerospace Corporation Georgia Institute of Technology

IBM France IIT Research Institute Logicon, Inc

The MITRE Corporation ORI/Intercon Planning Research Corporation RCA Sanders Associates Teledyne Brown Engineering Texas A&M University University of California at Los Angeles U S . Department of Housing and Urban

US. Department of the Interior U S . Naval Facilities Wyse Technology

Development

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