Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
Authors: Daphne Cornelisse, Thomas Rood, Yoram Bachrach, Mateusz Malinowski, Tal Kachman
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation shows that the predictions for the various solutions (the Shapley value, Banzhaf index and Least-Core) accurately reflect the true game theoretic solutions on previously unobserved games. Furthermore, the resulting model can generalize even to games that are very far from the training distribution or with more players than the games in the training set. |
| Researcher Affiliation | Collaboration | Daphne Cornelisse1 Thomas Rood1 Mateusz Malinowski3 Yoram Bachrach3 Tal Kachman1,2 1Department of Artificial Intelligence, Radboud University, Netherlands 2Donders Institute for Brain, Cognition and Behavior, Radboud University, Netherlands 3Deep Mind, UK |
| Pseudocode | No | The paper includes diagrams of data generation and model architecture but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We take the Melbourne Housing dataset [39] and obtain 9000 instances by 13 features after preprocessing steps (encoding the categorical features and standardization). ... Here, we consider two well-known datasets: the classical UCI Bank Marketing dataset [35] (17 features, 11,162 observations) and the Melbourne Housing dataset [39] (13 features, 34,857 observations). |
| Dataset Splits | No | The paper mentions partitioning the dataset into train and test sets but does not explicitly specify a validation set or precise percentages/counts for the splits to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the "SHAP package (Kernel Explainer)" but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | During training we minimize the Mean square error (MSE) between the true and predicted solutions. For each increment, we train a model for 100 epochs and test it on the remainder of the unseen instances. |