I can see what you're getting at but noone has figured out how to track the trajectory of candidate models that have been considered for the scope of modelling that I do. The space is too high dimensional
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I have discussed this issue in a few different places. Here with
@NPRougier Guest, O. & Rougier, N. P. (2016). Dialogue: What is Computational Reproducibility?. IEEE CIS Newsletter on Cognitive and Developmental Systems. 13 (2). http://oliviaguest.com/doc/guest_rougier_2016.pdf … -
This blog post that
@chbergma invited me to write (thank you!): http://bootphon.blogspot.co.uk/2015/10/replication-in-computational-cognitive.html … -
This paper: Implementations are not specifications: Specification, replication and experimentation in computational cognitive modeling https://doi.org/10.1016/j.cogsys.2013.05.001 …
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So there are more than a single issue here but I have been discussing this usually on my own and often without any support from others since the academic year of 2009/2010.
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:( It's sad people don't see the importance of the points you've brought up. It's fascinating to see another field facing nearly the same issues as machine learning research and suggesting similar trajectories through them.
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Yup! Not only similar issues, but I think the "root" is the same and the crossover of people/ideas from the two areas is not a coincidence!
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I wanted to say something like, really subfields of the same field distinguished by emphasis placed on biological plausibility but thought that sounded to much like the kind of thing physicists say :p
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