Mechanism Design for Team Formation
Authors: Mason Wright, Yevgeniy Vorobeychik
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now assess all of the proposed mechanisms empirically through simulations based both on randomly generated classes of preferences, as well as real-world data. |
| Researcher Affiliation | Academia | Mason Wright Computer Science & Engineering University of Michigan Ann Arbor, MI masondw@umich.edu Yevgeniy Vorobeychik Electrical Engineering and Computer Science Vanderbilt University Nashville, TN yevgeniy.vorobeychik@vanderbilt.edu |
| Pseudocode | Yes | Algorithm 1 A-CEEI-TF Algorithm Outline. |
| Open Source Code | No | The paper does not provide any statements about releasing its source code for the methodology, nor does it include links to a code repository. |
| Open Datasets | Yes | We use both randomly generated data and data from prior studies on preferences of human subjects over each other: Random-similar (R-sim) (Othman, Sandholm, and Budish 2010); Newfrat: NEWC15 (Newcomb 1958); Freeman: (Freeman and Freeman 1979). |
| Dataset Splits | No | The paper describes some experimental parameters and averaging over instances (e.g., 'averaged over 20 generated preference rankings' and '8 versions of each data set, with different random orders'), but does not specify explicit train/validation/test splits, percentages, or cross-validation methods for the datasets used. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For the randomly generated data sets, we set |N| = 20 and k = k = 5, and our results are averaged over 20 generated preference rankings for all players. We ran each mechanism on 8 versions of each data set, with different random orders over the players, which we held in common across data sets. We generate deviations from truthful reporting for agent j one at a time until 25 unique deviations have been produced. |