General Types of Models
Models transform user inputs and data into useful information. A model represents a real situation as an abstract framework. A model may be specified in mathematical expressions, in natural language statements or as a computer program. Managers can manipulate the input to a model to change outputs. Models update files, provide responses to user actions, and perform recurring analytical tasks. We can use "tool labels" like optimization and simulation to describe categories or types of models and those terms will be used in this chapter, but letís begin with some more general concepts. The terms explanatory, contemplative and algebraic explain some major differences in the purpose of decision models.
An explanatory model describes what has occurred to create current results or outcomes, and it provides an explanation or analysis of a situation. For example, the model Sales = f (Advertising, Number of Salespersons) may be based on a correlation of advertising and the number of salespersons with sales in prior quarters. This explanatory model may also be used to forecast future sales.
A contemplative model indicates or forecasts what outcomes might result from introducing a specific set of parameters or changes to a model. This type of analysis is significantly more dynamic and requires a higher level of interaction on the part of a manager or analyst.
An algebraic model indicates which values must be introduced into a system of simultaneous equations to create a specific outcome. A manager specifies an outcome and a starting point, and then animates or runs the model. This type of model helps managers gain insight about what variables must be manipulated and to what extent.
Explanatory models are descriptive models that describe situations. Contemplative and algebraic models are predictive models (cf., Starfield, Smith, and Blelock, 1990; Codd, Codd, and Salley, 1992).