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

magnetic tracker: A noncontact position measurement device that uses a magnetic field produced by a stationary transmitter to determine the real time position of a moving receiver element.

magnetron: A semi-conducting device in which the flow of electrons is controlled by an externally applied magnetic field.

main program: A program that invokes the timing routine to determine the next event and then transfers control to corresponding event routine to update the system state appropriately. The main program may also check for termination and invoke the report generator when the simulation is over.

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

management object model: A group of predefined High Level Architecture constructs (object and interaction classes) that provide the following: a) Access to federation execution operating information, b) Insight into the operations of joined federates and the runtime infrastructure, and c) Control of the functioning of the runtime infrastructure, the federation execution, and the individual joined federates.

Markov chain: A discrete Markov process. See: Markov chain model.

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

Markov process: A stochastic process that assumes that in a series of random events, the probability for occurrence of each event depends only on the immediately preceding outcome. See: semi-Markov process.

mass storage: Refers to any device that can store large amounts of data and retrieve it at some later time, even after system power-down. Mass storage devices are usually categorized in terms of being either on-line storage or off-line storage.

master scenario events list: A chronological list that supplements the exercise scenario with event synopses; expected participant responses; capabilities, tasks, and objectives to be addressed; and responsible personnel. It includes specific scenario events (or injects) that prompt players to implement the plans, policies, and procedures that require testing during the exercise, as identified in the capabilities-based planning process. It also records the methods that will be used to provide the injects (i.e., phone call, facsimile, radio call, e-mail).

master simulation datalink: Acts as the master air battle gamekeeper, presents the appropriate stimuli to the internally networked battle management, command, control, communications, computers and intelligence and weapon system simulators, records data collection events, and allows the neutral force to monitor the scenario and status of equipment. The host computers, array processors, disk and tape drives, terminals, displays, and software included in the master simulation datalink also support data processing functions of scenario development, data collection, data reduction, data analysis, and replay.

mathematical model: A mathematical model is a symbolic model whose properties are expressed in mathematical symbols and relationships. Mathematical models are commonly used to quantify results, solve problems and predict behavior.

measure of effectiveness: 1. A qualitative or quantitative measure of the performance of a model or simulation or a characteristic that indicates the degree to which it performs the task or meets an operational objective or requirement under specified conditions. 2. A qualitative or quantitative measure of aggregate performance or a characteristic of a model, simulation or system that indicates the degree to which it performs the task or meets an operational objective or requirement under specified conditions. 3. Measure of how the system/individual performs its functions in a given environment. Used to evaluate whether alternative approaches meet functional objectives and mission needs. Examples of such measures include loss exchange results, face effectiveness contributions, and tons delivered per day. 4. Variable that describes how well a system carries out a task or set of tasks within a specific context. A measure of effectiveness is measured outside the system for a defined environment and state of the context variables.

measure of outcome: Metric that defines how operational requirements contribute to end results at higher levels, such as campaign or national strategic outcomes.

measure of performance: Measure of how the system/individual performs its functions in a given environment (e.g., number of targets detected, reaction time, number of targets nominated, susceptibility of deception, task completion time). It is closely related to inherent parameters (physical and structural) but measures attributes of system behavior. See: measure of effectiveness.

mechanical tracker: Consists of a serial or parallel kinematic structure composed of links interconnected using sensorized joints for determining the spatial position and orientation of a target object.

mediated reality: Includes adding virtual objects to visual reality but also includes the ability to take away, alter, deliberately diminish, and significantly alter the perception of visual reality.

mental model: 1. Abstraction of thought. 2. An explanation of someone's thought process about how something works in the real world.

Mercator map projection: A conformal map projection of the cylindrical type. The Equator is represented by a straight line true to scale; the geographic meridians are represented by parallel straight lines perpendicular to the line representing the Equator; they are spaced according to their distance apart at the Equator. The geographic parallels are represented by a second system of straight lines perpendicular to the family of lines representing the meridians and therefore parallel with the Equator. Conformality is achieved by mathematical analysis, the spacing of the parallels being increased with increasing distance from the Equator to conform with the expanding scale along the parallels resulting from the meridians being represented by parallel lines. Also called equatorial cylindrical orthomorphic map projection.

message: Format and semantics of data, also known as protocol data units, that are exchanged between simulation applications and simulation management. The protocol data units provide information concerning simulated entity states, the type of entity interactions that take place in a exercise, and data for management and control of a exercise.

metadata: Searchable information describing the characteristics of data; data or information about data; or descriptive information about an object's data, data activities, systems, and holdings. For example, metadata for a model or simulation will include keywords and/or a description of the capabilities along with developer and user information. 2. Data about data; specification of the content, meaning, structure, and use of the data. 3. Information describing the characteristics of data; data or information about data; descriptive information about an organization's data, data activities, systems, and holdings. 4. Searchable data that describes the function and use of an artifact. If the artifact is a model, rather than data, sometimes called a metamodel. 5. Structured, encoded data that describe characteristics of information-bearing entities to aid in the identification, discovery, assessment, and management of the described entities.

metadata catalog: A system that contains the instances of metadata associated with individual data assets. Typically, a metadata catalog is a software application that uses a database to store and search records (or cards) that describe such items as documents, images, and videos. Search portals and applications would use metadata catalogs to locate the data assets that are relevant to their query.

meta-knowledge: Knowledge about knowledge. Knowledge about the use and control of domain knowledge in an expert or knowledge-based system. Knowledge about how the system operates or reasons.

metamodel: A model of a model. Metamodels are abstractions of the M&S being developed that use functional decomposition to show relationships, paths of data and algorithms, ordering, and interactions between model components and subcomponents. Metamodels allow the software engineers who are developing the model to abstract details to a level that subject matter experts can validate.

methodology: The system of principles, practices, and procedures, applied to a specific branch of knowledge.

metric: A measure of the extent or degree to which a product possesses and exhibits a certain quality, property, or attribute.

metric(s): A process or algorithm that may involve statistical sampling, mathematical computations, and rule-based inferencing. Metrics provide the capability to detect and report defects within a sample.

middleware: Software that connects or integrates other software modules or components, typically providing a set of communications or interaction functions that may be invoked by the linked modules.

minimize: (communication) A condition wherein normal message and telephone traffic is drastically reduced in order that messages connected with an actual or simulated emergency shall not be delayed.

mission space: The environment of entities, actions, and interactions comprising the set of interrelated processes used by individuals and/organizations to accomplish assigned tasks.

mock-up: A full-sized model, but not necessarily functional, built accurately to scale, used chiefly for study, testing, or display. See: physical model.

model: A physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process. See: structural model; analytical model.

model concept: Information (and amount) required to develop a model.

model entity: A model entity represents a real world object in a simulation.

model specification: Precise specification for a specific model which, if implemented properly, will produce anticipatable results, i.e., dead reckoning or coordinate conversion. Compare to: modeling method (which is less specific, typically larger in scope).

modeling: 1. Application of a standard, rigorous, structured methodology to create and validate a physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process. 2. Representation of an event and/or things that is real (a case study) or contrived (a use-case). It can be a representation of an actual system. It can be something used in lieu of the real system to better understand a certain aspect about that system. To produce a model you must abstract from reality a description of a vibrant system. The model can depict the system at some point of abstraction or at multiple levels of abstraction with the goal of representing the system in a reliable fashion (i.e., mathematical). 3. The process concerns itself with the extraction of knowledge from the physical plant to be simulated, organizing that knowledge appropriately, and representing it in some unambiguous fashion.

modeling and simulation (M&S): 1. The discipline that comprises the development and/or use of models and simulations. 2. The use of models, including emulators, prototypes, simulators, and stimulators, either statically or over time, to develop data as a basis for making managerial or technical decisions. The terms "modeling" and "simulation" are often used interchangeably.

M&S accreditation: The official certification that a model or simulation is acceptable for use for a specific purpose.

M&S asset: M&S tools, data, and services, including models and simulations, and data assets.

M&S Coordination Agent: A DoD Component designated by USD(AT&L) to coordinate prescribed aspects of DoD M&S for a designated M&S area. A DoD M&S Coordination Agent is not a Modeling and Simulation Executive Agent.

Modeling and Simulation Coordination Office (M&SCO): A USD(AT&L) organization that performs key M&S Enterprise-level coordination functions necessary to encourage cooperation, synergism, and cost-effectiveness among the M&S activities of the DoD Components.

M&S data: Data used to develop models or simulations, data used as input to models and simulations, and data produced by models and simulations.

M&S developer: The agency that actually develops an M&S or the agency that is overseeing the M&S development by a contractor or Federally Funded Research and Development Corporation.

Modeling and Simulation Executive Agent (MSEA): A DoD Component designated by USD (AT&L) to coordinate all aspects of DoD M&S for a designated M&S area. These MSEAs are transitioning to M&S Coordination Agents. There are five such MSEA's. Air Force for Air and Space Environment. Navy for Ocean Environment; National Geospatial-Intelligence Agency for Terrain Environment (now under the authority, direction and control of Under Secretary of Defense for intelligence (USD(I)); Defense Intelligence Agency for Threat Forces and Intelligence Processes (now under the authority, direction and control of USD(I)); and Assistant to the Secretary of Defense for Nuclear and Chemical and Biological Defense Programs (ATSD(NCB)) for Chemical, Biological, Radiation, and Nuclear Defense M&S.

M&S event: An interaction between M&S infrastructure elements that is associated with a particular point in time that results in something happening or changing. M&S Events include tests, analysis, research and design, training, experiments, M&S infrastructure interactions, and internal model interactions.

M&S infrastructure: An underlying base or foundation; the basic facilities, equipment, installations and services needed for the functioning of a system. An M&S infrastructure would consist of M&S systems and applications, communications, networks, architectures, standards and protocols, information resource repositories, etc.

Modeling and Simulation Integrated Process Team (M&S IPT): A DoD sub-committee of the M&S Steering Committee (M&S SC) that makes recommendations and performs functions for the M&S SC.

M&S interoperability: 1. The ability of a model or simulation to provide services to, and accept services from, other models and simulations, and to use the services so exchanged to enable them to operate effectively together. 2. The ability of a federate to provide services to and/or accept services from other federates and to use the services so exchanged to enable the federates to operate effectively together.

M&S Master Plan: A plan published under the authority of the appropriate DOD Component or functional area lead that establishes time-phased objectives and responsibilities aligned with the DOD master plan and targeted at the needs of the DOD Component or functional area.

M&S proponent: The DoD component organization that has primary responsibility to initiate development and life-cycle management of the reference version of one or more models and/or simulations.

M&S reuse: 1. The use of M&S resources, (i.e., models, simulations, databases, algorithms, tools) for purposes beyond those for which they were originally developed. Reuse can occur within an organization or in different organizations, or in different application areas. 2. The process of building, assembling, or executing M&S systems and applications from existing components.

M&S Services: An activity that enhances the ability to effectively and efficiently use M&S to accomplish a mission.

Modeling and Simulation Steering Committee (M&S SC): An executive-level DoD committee that advises and assists the Under Secretary of Defense for Acquisition, Technology and Logistics (USD(AT&L)) in all matters pertaining to M&S.

Modeling and Simulation Strategic Vision: A high-level document describing the strategic vision and goals for the DoD M&S Enterprise.

M&S Tools: Software that implements a model or simulation or an adjunct tool, i.e., software and/or hardware that is either used to provide part of a simulation environment (e.g., to manage the execution of the environment) or to transform and manage data used by or produced by a model or simulation. Adjunct tools are differentiated from simulation software in that they do not provide a virtual or constructive representation as part of a simulation environment.

M&S user: M&S User is the term used to represent the organization, group, or person responsible for the overall application. The M&S user needs to solve a problem or make a decision and wants to use modeling or simulation to do so. The M&S user defines the requirements, establishes the criteria by which model or simulation fitness will be assessed, determines what method or methods to use, makes the accreditation decision, and ultimately accepts the results.

modeling method: Set of organizing principles, fundamental concepts, and common algorithms and data structures for a class of models, i.e., discrete event simulation or finite element modeling. Category of models with a common basis or modeling technique, i.e., Lanchester equations, finite state machines.

model-test-model: An integrated approach to using models and simulations in support of pretest analysis and planning; conducting the actual test and collecting data; and post-test analysis of test results along with further validation of the models using the test data.

modifier: A word that helps define and render a name unique within the database, which is not the prime or class word.

modular semi-automated forces: A class of computer generated forces utilizing a modular software structure in which model components have well-defined and documented interfaces allowing run-time reconfiguration of model behavior to develop generalized, and more sophisticated, representations of reactive behaviors and missions.

monoscopic image depth cues: Are those that can be seen in a single static view of a scene, as in photographs and paintings.

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

Monte Carlo algorithm: a randomized algorithm whose running time is deterministic, but whose output may be incorrect with a certain (typically small) probability.

Monte Carlo method: a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in simulating physical and mathematical systems. These methods are most suited to calculation by a computer and tend to be used when it is infeasible to compute an exact result with a deterministic algorithm. This method is also used to complement the theoretical derivations.

motion depth cues: Come from the parallax created by changing relative position between the head and the object being observed (one or both may be in motion).

multicast: A transmission mode in which a single message is sent to selected multiple (but not necessarily all) network destinations; i.e., one-to-many. Contrast with: broadcast, unicast.

multisensory input/output: The use of more than one sensory mechanism (visual, aural, tactile, etc.) to interact with a computer-generated environment.

multi-resolution modeling: Represents aspects of the real world at more than one level of detail.

multi-state objects: Mission space entities that express a changing state (in attribution and visual display) as the simulation progresses (e.g., damage to structures, changes in vegetation, damage system representations such as vehicles, tanks, etc.).

multi-step methods: Used for the numerical solution of ordinary differential equations. Conceptually, a numerical method starts from an initial point and then takes a short step forward in time to find the next solution point. Multistep methods attempt to gain efficiency by keeping and using the information from previous steps rather than discarding it. Consequently, multistep methods refer to several previous points and derivative values.