Single-Peaked Opinion Updates
Authors: Robert Bredereck, Anne-Marie George, Jonas Israel, Leon Kellerhals
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study opinion diffusion [Grandi, 2017] in a setting where agents opinions are modelled as single-peaked rankings over a set of candidates. In each update step one agent observes the preferences of all their neighbours in the network, aggregates these by a given voting rule and changes their opinion accordingly. For issues where preferences are naturally single-peaked, it seems reasonable to assume that also the updated preferences of an agent in a diffusion process remain single-peaked. We investigate which voting rules are applicable in this sense, which lead to converging diffusion dynamics, and whether it is tractable to find update sequences that maximally spread an extreme opinion. |
| Researcher Affiliation | Academia | Institut f ur Informatik, TU Clausthal, Germany; Algorithm Engineering, Humboldt-Universit at zu Berlin, Germany; Analytical Solutions and Reasoning, University of Oslo, Norway; Research Group Efficient Algorithms, Technische Universit at Berlin, Germany; Algorithmics and Computational Complexity, Technische Universit at Berlin, Germany |
| Pseudocode | Yes | GREEDY SEQUENCE σ FOR EXTREME OPINION r (1) Update every non-stable voter with opinion r = r to opinion r if possible. (2) Update every non-stable voter with opinion r . (3) Stabilize network: update non-stable voters with opinions r = r . |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link or explicit statement of code release) for open-source code. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets, thus no information on publicly available training data is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with data, thus no information on dataset splits for training, validation, or testing is provided. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for its theoretical analysis or computations. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail experimental setup parameters such as hyperparameters or system-level training settings. |