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].
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. |