On the Role of Memory in Robust Opinion Dynamics
Authors: Luca Becchetti, Andrea Clementi, Amos Korman, Francesco Pasquale, Luca Trevisan, Robin Vacus
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evidence discussed in Section 5 suggests that its convergence time might be compatible with n log O(1) n. In terms of parallel time (i.e., the average number of activations per agent), this would imply an exponential gap between this case and the memoryless case. The Ft T dynamics is then compared experimentally to the voter model. Results are summed up in Figure 1, in terms of parallel rounds (one parallel round corresponds to n activations). They suggest that the expected convergence time of Ft T is about Θ(polylogn) parallel rounds. |
| Researcher Affiliation | Academia | 1Sapienza University of Rome, Rome, Italy 2Tor Vergata University of Rome, Rome, Italy 3 CNRS, located at FILOFOCS, Tel-Aviv, Israel 4Bocconi University, Milan, Italy 5CNRS, IRIF, Paris, France |
| Pseudocode | No | The paper describes algorithms (like the voter model and FtT dynamics) in prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | More details regarding the dynamics and its simulations can be found in [Becchetti et al., 2023]. |
| Open Datasets | No | The paper describes a theoretical model and simulations of agent opinion dynamics from various 'initial configurations' but does not refer to or use a pre-existing publicly available or open dataset in the conventional sense of machine learning experiments. |
| Dataset Splits | No | The paper describes simulations of a theoretical model of opinion dynamics from various initial configurations but does not mention specific training, validation, or test dataset splits in the conventional sense of machine learning experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions simulations but does not provide specific software dependencies or library versions (e.g., Python, PyTorch, or specific solvers with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The dynamics that we use is derived from the algorithm of [Korman and Vacus, 2022], and called Follow The Trend (Ft T). It uses a sample size of ℓ= 10 log n and works for an arbitrary number of opinions k. ... for z = 1 source agent. |