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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/804

Title: Developing reproducible and comprehensible computational models
Authors: Lane, PCR
Gobet, F
Keywords: Computational model
Simulation
Methodology
Behavioural test
Theory development
Specification language
Documentation
Robust testing
Test
CHREST
Canonical result
Cooper
Shallice
Discrimination
Publication Date: 2003
Publisher: Elsevier
Citation: Artificial Intelligence, 144, 251-263.
Abstract: Quantitative predictions for complex scientific theories are often obtained by running simulations on computational models. In order for a theory to meet with wide-spread acceptance, it is important that the model be reproducible and comprehensible by independent researchers. However, the complexity of computational models can make the task of replication all but impossible. Previous authors have suggested that computer models should be developed using high-level specification languages or large amounts of documentation. We argue that neither suggestion is sufficient, as each deals with the prescriptive definition of the model, and does not aid in generalising the use of the model to new contexts. Instead, we argue that a computational model should be released as three components: (a) a well-documented implementation; (b) a set of tests illustrating each of the key processes within the model; and (c) a set of canonical results, for reproducing the model’s predictions in important experiments. The included tests and experiments would provide the concrete exemplars required for easier comprehension of the model, as well as a confirmation that independent implementations and later versions reproduce the theory’s canonical results.
URI: http://www.elsevier.com/wps/find/journaldescription.cws_home/505601/description#description
http://bura.brunel.ac.uk/handle/2438/804
Appears in Collections:School of Social Sciences Research Papers
Psychology

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