Explanatory & Analytical Models


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Definition of a "Model":

  1. A simple version: A model is a simplified representation of some aspect of the real world. (p.8)
  2. A more complex version: A mathematical, logical or mechanical representation of a relationship, theory, process, system, or sequence of events, so designed that a study of the model functions as a means of summarizing the complex relations of the real world or as a way of illustrating a theory.

Purposes and Advantages (incl. pedagogic advantages) of Modeling:

Types of Models:
Flow Charts: A descriptive, diagrammatic model presenting situations where commodities (including information) or portions of populations pass through a system from one condition to another. Drawing boxes and showing the connections between them is a good way to attack problems associated, e.g., with student projects. If you find it difficult to draw up such a model, you probably do not understand all aspects of the system you are trying to model.

Decision Trees: Part of decision (including location decision) analysis. They help describe, organize, trace repercussions of and discriminate between alternative decisions.

Long Divisions (averages, percentages, coefficients, quotients; incl. the famous "Location Quotient"): Reduce the complexity and assist in comparisons of quantitative information.

Compound Growth Models: a simple, but formal mathematical model of cumulative change which describes explicitly the quantitative changes in a particular variable or system in response to specific stimuli. (Compound interest, net population growth models etc.)

Vicious Circles (Cycles): Cumulative models of change based on 'positive' feedback.

Descriptive Models: describe the way the world (actually but in simplified form) operates; show what outcomes may result from what action.

Prescriptive Models: go beyond descriptive models in that they also include procedures for choosing between alternative actions, given the decision-makers preferences among the outcomes.

Predictive Models rearrange the structure of a descriptive model so that variables of interest at the end of a causal sequence can be predicted from variables earlier in the sequence.

Theoretical or Conceptual Models have a high degree of abstraction, whiles empirical or operational models have a low level of abstraction and a basis in empirical and/or practical considerations.

Planning Models: allow alternative courses of action to be evaluated

Deterministic Models: Given the relationships, the initial conditions and the actions, the outcome is certain and uniquely determined.

Probabilistic (or Stochastic) Models: are based on relationships which are recognized as leading to variable outcomes which however can be probabilisticly predicted.

[Sources Edith Stokey & Richard Zeckhauser. A Primer for Policy Analysis, Ch.2; Goodall]

Other Literature:

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