Indecision Modeling
Authors: Duncan C. McElfresh, Lok Chan, Kenzie Doyle, Walter Sinnott-Armstrong, Vincent Conitzer, Jana Schaich Borg, John P. Dickerson5975-5983
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test these models using data collected from an online survey where participants choose how to (hypothetically) allocate organs to patients waiting for a transplant. Our Contributions. First, we conduct a pilot experiment to illustrate how different interpretations of indecision lead to different outcomes ( ). To test the utility of these models, we conduct a second experiment to collect a much larger dataset of decision responses ( ). We take a machine learning (ML) perspective, and evaluate each model class based on its goodness-of-fit to this dataset. |
| Researcher Affiliation | Academia | Duncan C Mc Elfresh,1 Lok Chan,2 Kenzie Doyle,3 Walter Sinnott-Armstrong,2 Vincent Conitzer,2 Jana Schaich Borg,2 John P Dickerson1 1 University of Maryland, College Park 2 Duke University 3 University of Oregon |
| Pseudocode | No | The paper describes mathematical models and processes but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | All code used for our analysis is available online, 9 and details of our implementation can be found in Appendix ??. 9https://github.com/duncanmcelfresh/indecision-modeling |
| Open Datasets | No | The paper mentions data collected from an online survey for Study 1 and Study 2 and notes that the datasets collected in Study 1 and Study 2 provide some insight, but it does not provide concrete access information (link, DOI, specific repository, or formal citation with author/year) for these datasets to be publicly available. |
| Dataset Splits | No | For each participant we randomly split their question-response pairs into a training and testing set of equal size (20 responses each). No explicit mention of a validation set split is found. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'scikit-learn' used for the MLP, citing Pedregosa et al. (2011), but does not specify its version number or other software dependencies with versioning. |
| Experiment Setup | Yes | We take a maximum likelihood estimation (MLE) approach to fitting each model: i.e., we select agent parameters u and λ which maximize the loglikelihood (LL) of the training responses. Since the LL of these models is not convex, we use random search via a Sobol process (Sobol 1967). The search domain for utility vectors is u [ 1, 1]N, the domain for probability parameters is (0, 1), and the domain for λ depends on the model type (see Appendix ??). |