Inverse Decision Modeling: Learning Interpretable Representations of Behavior

Code author: Alihan Hüyük (ah2075@cam.ac.uk)

This repository contains the necessary code to replicate the main experimental results in the ICML 2021 paper 'Inverse Decision Modeling: Learning Interpretable Representations of Behavior.' Inverse bounded rational control, which is given as an example instance of inverse decision modeling in the paper, is implemented in files diag/main.py and adni/main.py for the decision-making environments considered in the paper, namely DIAG and ADNI.

Usage

First, install the required python packages by running:

    python3 -m pip install -r requirements.txt

Install LaTeX for figure generation.

Then, the main experimental results in the paper can be replicated by running:

    ./diag/run.sh
    python3 diag/plot-forward.py  # generates Figure 2
    python3 diag/plot-inverse.py  # generates Figure 3
    python3 diag/eval-irl.py      # computes cost-benefit ratios in Section 5.2

    ./adni/run.sh
    python3 adni/eval.py          # computes estimated values of beta in Section 5.3

Note that, in order to run the experiments for ADNI, you need to get access to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

Citing

If you use this software please cite as follows:

@inproceedings{jarrett2021inverse,
  author={Daniel Jarrett and Alihan H\"uy\"uk and Mihaela van der Schaar},
  title={Inverse decision modeling: learning Interpretable Representations of behavior},
  booktitle={Proceedings of the 38th International Conference on Machine Learning (ICML)},
  year={2021}
}