Definitions[edit | edit source]

Artificial intelligence[edit | edit source]

A model is

[a]n actionable representation of all or part of the external environment of an AI system that describes the environment's structure and/or dynamics. The model represents the core of an AI system. A model can be based on data and/or expert knowledge, by humans and/or by automated tools like machine learning algorithms.[1]

Biometrics[edit | edit source]

A model is

a representation used to characterize an individual. Behavioral-based biometric systems, because of the inherently dynamic characteristics, use models rather than static templates.[2]

General[edit | edit source]

A model is

[an] approximation, representation, or idealization of selected aspects of the structure, behavior, operation, or other characteristics of a real-world process, concept, or system.[3]
[t]he graphical representation or simulation of a process, relationship or information, along with a narrative that supports the diagram(s).[4]
[r]epresentations of information, activities, relationships, and constraints.[5]

Technology[edit | edit source]

A model is

[a] representation of an actual or conceptual system that involves mathematics, logical expressions, or computer simulations that can be used to predict how the system might perform or survive under various conditions or in a range of hostile environments.[6]
a very detailed description or scaled representation of one component of a larger system that can be created, operated, and analyzed to predict actual operational characteristics of the final produced component.[7]
a physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process.[8]

Overview[edit | edit source]

"Generally speaking, a model is a conceptual structure used to understand and predict phenomena that may be difficult to observe and influence directly. Models are abstract, in the sense that they omit details in order to facilitate expression and understanding of aspects of the phenomenon. Good models are expressive, in that they capture knowledge that cannot easily be gained by experience except perhaps at great expense or danger or over long periods of time. The validity of a model is the extent to which it has predictive power in the domains for which it is designed. Engineers, managers, and others may use a diversity of models in a design process in order to understand the various aspects of a problem, the design issues, and the potential consequences of particular design decisions. Qualitative and informal models . . .may be less predictive than a validated scientific model. The validation of these informal management models is necessarily informally based, and indeed all variables may not be identified. But for many disciplines of endeavor, including research-program management, this is the best that can be achieved with present levels of understanding."[9]

References[edit | edit source]

See also[edit | edit source]

Community content is available under CC-BY-SA unless otherwise noted.