2009
DOI: 10.1016/s0076-6879(08)03811-1
|View full text |Cite
|
Sign up to set email alerts
|

Chapter 11 Evaluation and Comparison of Computational Models

Abstract: Computational models are powerful tools that can enhance the understanding of scientific phenomena. The enterprise of modeling is most productive when the reasons underlying the adequacy of a model, and possibly its superiority to other models, are understood. This chapter begins with an overview of the main criteria that must be considered in model evaluation and selection, in particular explaining why generalizability is the preferred criterion for model selection. This is followed by a review of measures of… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
50
0

Year Published

2011
2011
2022
2022

Publication Types

Select...
10

Relationship

0
10

Authors

Journals

citations
Cited by 58 publications
(50 citation statements)
references
References 34 publications
(37 reference statements)
0
50
0
Order By: Relevance
“…Further discussion of these and other tests are beyond the scope of this review [201], but their essence is consistent with the philosophy that scientists should seek to explain phenomena as simply as possible, but no simpler. Considering the four steps in this procedure have been introduced, the following two examples are discussed for clarification.…”
Section: A1 Scientific Models and Four Steps To Any Experimental Dementioning
confidence: 73%
“…Further discussion of these and other tests are beyond the scope of this review [201], but their essence is consistent with the philosophy that scientists should seek to explain phenomena as simply as possible, but no simpler. Considering the four steps in this procedure have been introduced, the following two examples are discussed for clarification.…”
Section: A1 Scientific Models and Four Steps To Any Experimental Dementioning
confidence: 73%
“…There are many ways to do this (see, for example, Myung, Tang, & Pitt, 2010). For example, using adjusted R 2 values is one way to handle this problem in regression situations, a procedure I follow here.…”
Section: General Methodsmentioning
confidence: 99%
“…In comparing our models, we must take into account that data are noisy, and that a more complex model is more flexible in capturing the noise (Myung, Tang, & Pitt, 2009). That is, a more complex model in terms of number of parameters and the functional form (i.e., the way in which the parameters and the variables are combined in the model equation) can come out best from the model comparison because it is more flexible in capturing the noise, and not because it best describes the mental process (Pitt, Myung, & Zhang, 2002).…”
Section: Methodsmentioning
confidence: 99%