Strategic Candidacy Games with Lazy Candidates
Authors: Svetlana Obraztsova, Edith Elkind, Maria Polukarov, Zinovi Rabinovich
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The goal of this paper is to initiate the study of this class of games. For simplicity, we focus on the case where the voters make their choice among the available candidates using the Plurality rule. We consider Nash equilibria of such games as well as analyze two natural best-response dynamics for this setting: one where initially the set of active candidates is empty and the candidates join one by one, and one where initially all candidates are present, but then withdraw one by one. We relate the properties of strategic candidacy games with lazy candidates to those of vanilla strategic candidacy games, explore the role of Pareto dominance and Condorcet winners in this setting, and prove a number of computational complexity results. In particular, we show that checking whether a given strategic candidacy game with lazy candidates admits a Nash equilibrium is NP-complete. |
| Researcher Affiliation | Collaboration | Svetlana Obraztsova Tel Aviv University Tel Aviv, Israel Edith Elkind University of Oxford Oxford, United Kingdom Maria Polukarov University of Southampton Southampton, United Kingdom Zinovi Rabinovich Mobileye Vision Technologies Ltd. Israel |
| Pseudocode | No | The paper contains formal definitions, propositions, theorems, and proofs, but no structured pseudocode or algorithm blocks. Tables 1 and 2 specify preference profiles for a proof construction, which are data representations, not algorithms. |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code or providing links to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use empirical datasets for training, validation, or testing. Tables 1 and 2 provide specific preference profiles for a theoretical construction within a proof, not a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experimental setup, hyperparameters, or training configurations. |