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 [1].
Convergence and Quality of Iterative Voting Under Non-Scoring Rules
Authors: Aaron Koolyk, Tyrone Strangway, Omer Lev, Jeffrey S. Rosenschein
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then conduct an empirical analysis of the iterative voting winners for these non-scoring rules, and compare the winner quality of various strategies. |
| Researcher Affiliation | Academia | Aaron Koolyk Hebrew University of Jerusalem EMAIL Tyrone Strangway and Omer Lev University of Toronto EMAIL Jeffrey S. Rosenschein Hebrew University of Jerusalem EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Software-wise, we extend the iterative voting simulation framework of [Meir et al., 2014],3 to new voting rules and dynamics, and will publish our code there. http://www.preflib.org/tools/ivs.php |
| Open Datasets | No | The paper states 'Proļ¬les are generated by either sampling from a uniform distribution or a single-peaked one.' but does not provide concrete access information (link, citation with authors/year, or specific repository) for a publicly available dataset. |
| Dataset Splits | No | The paper states 'Proļ¬les are generated by either sampling from a uniform distribution or a single-peaked one.' and 'For each voting rule, response dynamic, and distribution we sample 1000 different games, and because of the nondeterministic nature of iterative voting each of these games is repeated 100 times'. However, it does not specify explicit training, validation, or test dataset splits in terms of percentages, sample counts, or predefined standard splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions extending 'the iterative voting simulation framework' but does not provide specific software names with version numbers for reproducibility (e.g., Python, libraries, solvers). |
| Experiment Setup | Yes | In order to analyze the qualitative effects on outcome of iterative voting, we turn to empirical simulations. What makes one outcome better than another is a subtle question as there is no agreed-upon measure of quality. Furthermore, voting rules are deļ¬ned with different goals in mind. For example, Maximin ensures that the core number of supporters a candidate has, against any other, is maximal (an objective not shared by other rules). As we wish to see general properties of the interaction of voting rules and dynamics, we focused on a particular setting: 10 voters and 4 candidates. Proļ¬les are generated by either sampling from a uniform distribution or a single-peaked one. For each voting rule, response dynamic, and distribution we sample 1000 different games, and because of the nondeterministic nature of iterative voting each of these games is repeated 100 times, each time with a different order of voter responses. Iterative voting is executed until an equilibrium is reached, a cycle is detected, or some maximum number of iterations have elapsed. Though many sampled proļ¬les start in equilibrium, we are interested in the effects of the iterative process, and focus on proļ¬les where iterative voting occurred. For both our voting rules and response dynamics, ties are broken in a deterministic fashion. In the case of a tie in a voting rule, out of all the potential winning candidates the lexicographical ļ¬rst is selected. For response dynamics that encounter ties, the ļ¬rst proļ¬le that was discovered is chosen. |