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code and data from Gureckis & Goldstone (2009)
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README.md
allruns.pkl
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femaleruns.pkl
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requirements.txt

README.md

baby_names

code and data from Gureckis & Goldstone (2009)

Gureckis, T.M. and Goldstone, R.L. (2009) How You Named Your Child: Understanding The Relationship Between Individual Decision Making and Collective Outcomes. TopiCS in Cognitive Science, 1 (4), 651-674.

The original code for this project was all analyzed using Mathematica. Interestingly, back when we started this it was not such a popular dataset for textbook data-analysis and so had to write scripts to crawl and parse the SSA babyname website. Anyway, as part of a few weeks of "skill updating" I recreated all of the graphs of the paper using Jupyter Notebooks and Pandas. For the most part everything comes out perfectly the same. I didn't go through the fits of the MILEY model because they were largely supportive of the more empirically-driven data analysis and the model fitting itself is a complex issue (involving multiple maximum likelihood searches, many local minima, etc...). However, this is both a relief and was a fun way to test my Python data analysis skills.

The python requirements for this notebook are more extensive than they need to be. I just did pip freeze on my current setup to make sure I got everything. You can probably get by with numpy, scipy, bokeh, pandas, and statsmodels.

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