The TEXbook
Software—Practice & Experience
Unified theories of cognition
Programming perl
Literate programming
Programming from specifications (2nd ed.)
Programming from specifications (2nd ed.)
A systematic methodology for cognitive modelling
Artificial Intelligence - Special volume on empirical methods
The practice of programming
Refactoring: improving the design of existing code
Refactoring: improving the design of existing code
Extreme programming explained: embrace change
Extreme programming explained: embrace change
A Discipline of Programming
A Specification of the Soar Cognitive Architecture in Z
A Specification of the Soar Cognitive Architecture in Z
Discovering predictive variables when evolving cognitive models
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
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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.