Equilibrium Refinement through Negotiation in Binary Voting
Authors: Umberto Grandi, Davide Grossi, Paolo Turrini
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We describe a model of equilibrium refinement for voting games which: (i) is applicable to one-shot voting in the general context of binary aggregation; (ii) does not rely on limit behavior in repeated interactions; and (iii) can capture the compromise-seeking phase that typically precedes decision-making by voting. More specifically we address the effect of pre-play negotiations on the outcomes of voting games on binary (yes-no) issues. We isolate precise conditions under which bad equilibria e.g., inefficient ones can be overcome, and good ones sustained. |
| Researcher Affiliation | Academia | Umberto Grandi University of Toulouse France umberto.grandi@irit.fr Davide Grossi University of Liverpool United Kingdom d.grossi@liverpool.ac.uk Paolo Turrini Imperial College London United Kingdom paolo.turrini@imperial.ac.uk |
| Pseudocode | No | The paper describes theoretical models and mathematical proofs, but it does not include any structured pseudocode or algorithm blocks. It mentions 'An algorithm to compute a prevote negotiation strategy that leads to a sustainable NE is provided in the proofs of Theorems 7 and 8', but this refers to the logical steps within the proofs rather than a distinct pseudocode block. |
| Open Source Code | No | The paper does not contain any statement about making source code publicly available or provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or reference any publicly available or open datasets for training or evaluation. The tables in the paper are illustrative examples, not actual dataset instances. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experiments that would require specific hardware specifications like GPU models, CPU types, or cloud resources. |
| Software Dependencies | No | The paper is purely theoretical and does not describe any software dependencies or specific version numbers needed to replicate the work. |
| Experiment Setup | No | The paper is purely theoretical and does not describe any experimental setup details, such as hyperparameter values, training configurations, or system-level settings. |