Choosing the best model: Level of detail, complexity, and model performance

R. J. Brooks*, A. M. Tobias

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

125 Citations (Scopus)


The lack of a methodology, or at least detailed guidelines, for choosing the best model in a mathematical or computer modelling study stems from a poor understanding of the precise ways in which the success of the study depends upon the particular model used. As a result, the choice of the best model is regarded as more of an art than a science. In order to improve the model selection process, model performance needs to be clearly defined, and suitable model attributes identified that can be used to predict the performance of the alternative candidate models. This paper distinguishes the different aspects of model performance and considers the extent to which they can be measured. The most common attributes used to compare alternative models are level of detail and complexity although these terms are used in a number of different ways. The meanings of these concepts are therefore discuss and the likely relationships with the model performance elements considered. The related area of simplification is reviewed and the areas in which further work is required are set out.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalMathematical and Computer Modelling
Issue number4
Publication statusPublished - Aug 1996


  • Complexity
  • Level of detail
  • Model performance
  • Modelling methodology
  • Simplification

ASJC Scopus subject areas

  • Information Systems and Management
  • Control and Systems Engineering
  • Applied Mathematics
  • Computational Mathematics
  • Modelling and Simulation


Dive into the research topics of 'Choosing the best model: Level of detail, complexity, and model performance'. Together they form a unique fingerprint.

Cite this