Convergence in Multi-Issue Iterative Voting under Uncertainty
Authors: Joshua Kavner, Reshef Meir, Francesca Rossi, Lirong Xia
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical studies demonstrate that while cycles are common for agents without uncertainty, introducing uncertainty makes convergence almost guaranteed in practice. |
| Researcher Affiliation | Collaboration | 1Rensselaer Polytechnic Institute 2Technion Israel Institute of Technology 3IBM T.J. Watson Research Center kavnej@rpi.edu, reshefm@ie.technion.ac.il, Francesca.Rossi2@ibm.com, xialirong@gmail.com |
| Pseudocode | No | The paper describes dynamics and definitions (e.g., Definition 2 for Best response, Definition 4 for Local dominance improvement) but does not include any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only provides a link to the full version of the paper on arXiv: '1The full version of the paper may be found on the archive at: https://arxiv.org/abs/2301.08873' |
| Open Datasets | No | The paper mentions generating preference profiles: 'We generate 10, 000 preference profiles for each combination by sampling agents preferences uniformly and independently at random.' However, it does not provide any concrete access information (link, DOI, formal citation) for this or any other dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. It states: 'We generate 10, 000 preference profiles for each combination by sampling agents preferences uniformly and independently at random.' |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts). It only states 'Our computational experiments investigate...' |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. It only describes the conceptual models and dynamics. |
| Experiment Setup | No | The paper describes the input parameters for the experiments ('n {7, 11, 15, 19} agents, p {2, 3, 4, 5} binary issues, and r {0, 1, 2, 3} uncertainty') and the number of rounds for stopping ('50,000 rounds'). However, it does not include concrete hyperparameter values or detailed system-level training configurations typically found in an 'experimental setup' section. |