Convergence to Equilibria in Strategic Candidacy
Authors: Maria Polukarov, Svetlana Obraztsova, Zinovi Rabinovich, Alexander Kruglyi, Nicholas R. Jennings
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study equilibrium dynamics in candidacy games, in which candidates may strategically decide to enter the election or withdraw their candidacy, following their own preferences over possible outcomes. Focusing on games under Plurality, we extend the standard model to allow for situations where voters may refuse to return their votes to those candidates who had previously left the election, should they decide to run again. We show that if at the time when a candidate withdraws his candidacy, with some positive probability each voter takes this candidate out of his future consideration, the process converges with probability 1. This is in sharp contrast with the original model where the very existence of a Nash equilibrium is not guaranteed. We then consider the two extreme cases of this setting, where voters may block a withdrawn candidate with probabilities 0 or 1. In these scenarios, we study the complexity of reaching equilibria from a given initial point, converging to an equilibrium with a predermined winner or to an equilibrium with a given set of running candidates. Except for one easy case, we show that these problems are NP-complete, even when the initial point is fixed to a natural truthful state where all potential candidates stand for election. |
| Researcher Affiliation | Collaboration | Maria Polukarov University of Southampton United Kingdom Svetlana Obraztsova Tel Aviv University Israel Zinovi Rabinovich Mobileye Vision Technologies Ltd. Israel Alexander Kruglyi St.Petersburg State Polytechnical University Russia Nicholas R. Jennings University of Southampton United Kingdom |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training. Therefore, no access information for a publicly available or open dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. Therefore, no specific dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would require specific software dependencies with version numbers. Therefore, no such details are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments requiring specific setup details like hyperparameters or training configurations. |