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..
Analysis of Equilibria in Iterative Voting Schemes
Authors: Zinovi Rabinovich, Svetlana Obraztsova, Omer Lev, Evangelos Markakis, Jeffrey Rosenschein
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide characterisations and complexity results for three models of iterative voting under the plurality rule. Our focus is on providing a better understanding regarding the set of equilibria attainable by iterative voting processes. ... we show that deciding whether a given pro๏ฌle is an iteratively reachable equilibrium is NP-complete. We fully characterise the set of attainable truth-biased equilibria, and show that it is possible to determine all such equilibria in polynomial time. ... We establish convergence of the iterative process, albeit not necessarily to a Nash equilibrium. As in the case with truth bias, we also provide a polynomial time algorithm to ๏ฌnd all the attainable equilibria. |
| Researcher Affiliation | Academia | Zinovi Rabinovich Independent Researcher Jerusalem, Israel EMAIL Svetlana Obraztsova National Technical University of Athens, Greece Tel-Aviv University, Israel EMAIL Omer Levs Hebrew University of Jerusalem Israel EMAIL Evangelos Markakis Athens University of Economics & Business Greece EMAIL Jeffrey S. Rosenschein Hebrew University of Jerusalem Israel EMAIL.i |
| Pseudocode | Yes | Algorithm 1 Checking reachability of NE under lazy voting |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve training on datasets or provide information about dataset availability for such purposes. |
| Dataset Splits | No | The paper is theoretical and does not involve training or validation splits of datasets. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments that would require specifying hardware details. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |