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Code for Humans decompose tasks by trading off utility and computational cost

Setup guide

  1. Using Python 3.8 or 3.9, install packages in requirements.txt.
  2. Using R 4.1, install packages in DESCRIPTION.
  3. Follow steps to set up Solway et al. (2014) model here.
  4. Follow steps to set up Tomov et al. (2020) model here.

Code structure

This is a quick guide to the structure of the code. I note locations of data, and the entrypoints that compute the figures in the paper.

  • Participant data is in rrtd/experiment/analyze/.data/1.14. Contains raw trial data (trialdata.csv), experimental condition (questiondata.csv), and experimental configuration for psuedo-random trial assignment (configuration.json).
  • rrtd/journal/openfield.ipynb - Figure 2
  • rrtd/journal/graph-heatmap.ipynb - Figures 3, 7, 10, A1
  • rrtd/journal/model-corr.ipynb - Figure 4
  • rrtd/journal/behavior.ipynb - Misc. Results, Figure 6
  • rrtd/journal/predict-behavior-with-model.ipynb - Tables 2, A1, A2, A3 and Figures 8, 9, A2, A3, A4
  • rrtd/journal/rw-theory.ipynb - Figure A5
  • Experiment code is available here: https://github.com/cgc/cocosci-optdisco.

Running models

While the above code will transparently run models, some analyses can take quite some time -- in particular, running the Solway analysis over all graphs took somewhere between 2000-4000 hours of CPU-time. Models can be run in parallel using the command-line interface to run_models.py, and the model results will be cached in directories starting with cache_run_models-v2. Some examples of the CLI:

To run the Tomov model with 50 CPUs (Should take some care -- running MATLAB with high parallelism via Python can cause crashes):

python run_models.py explicit_compute --n_jobs=50 --model_filter=tomov

To run the Solway model with 100 CPUs:

python run_models.py --n_jobs=100 --model_filter=solway

To run all other models:

python run_models.py --n_jobs=100 --model_filter=~solway

Running tests

make test
make integration-test