Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Pairwise Choice Markov Chains
Authors: Stephen Ragain, Johan Ugander
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that the PCMC model significantly outperforms both the Multinomial Logit (MNL) model and a mixed MNL (MMNL) model in prediction tasks on both synthetic and empirical datasets known to exhibit violations of Luce s axiom. |
| Researcher Affiliation | Academia | Stephen Ragain Management Science & Engineering Stanford University Stanford, CA 94305 EMAIL Johan Ugander Management Science & Engineering Stanford University Stanford, CA 94305 EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Data and code available here: https://github.com/sragain/pcmc-nips |
| Open Datasets | Yes | We evaluate our inference procedure on two empirical choice datasets, SFwork and SFshop, collected from a survey of transportation preferences around the San Francisco Bay Area [16]. |
| Dataset Splits | Yes | The results are averaged over 1,000 different permutations of the data with a 75/25 train/test split employed for each permutation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions using 'SLSQP [25]' for optimization, but it does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | We applied small amounts of additive smoothing to each dataset. We used a discrete mixture of k MNL models (with O(kn) parameters), choosing k so that the MMNL model had strictly more parameters than the PCMC model on each data set. |