Consensus in Opinion Formation Processes in Fully Evolving Environments
Authors: Vincenzo Auletta, Angelo Fanelli, Diodato Ferraioli6022-6029
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is the proof that in the general model, where the environment fully evolves with opinions, under reasonable conditions on the structure of the social graphs used in each epoch and on the recall of the agents in updating their beliefs, the opinion formation dynamics is ergodic and converges to a consensus. We also show that if we assume the social graph fixed, then, just as in the simpler DG model, it is sufficient that the graph is strongly connected and aperiodic to guarantee the convergence of the dynamics to a consensus. We run some preliminary experiments on very simple settings that give interesting results but a much more extended experimental activity is necessary to understand how the dynamics convergence time depends on the different parameters of the problem. |
| Researcher Affiliation | Academia | Vincenzo Auletta University of Salerno, Italy. auletta@unisa.it Angelo Fanelli CNRS, (UMR-6211), France. angelo.fanelli@unicaen.fr Diodato Ferraioli University of Salerno, Italy. dferraioli@unisa.it |
| 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, nor does it explicitly state that the code for the described methodology is open-source or available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments that involve training with a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not contain specific experimental setup details like hyperparameter values or training configurations. |