Overview

In the paper, we study the effect of homophily in collective opinion dynamics with spatial embedding and show that echo chambers emerge in low connectivity regimes. We also introduce Messenger as a solution to break echo chambers. This repository provides all necessary codes to replicate our study, including simulation, post-processing, and visualization scripts.

Paper Abstract

Here is the abstract of the paper: Collective estimation is a variant of collective decision-making, where agents need to achieve consensus on a continuous quantity in a self-organized fashion via social interactions. A particularly challenging scenario is a fully distributed collective estimation with strongly constrained, dynamical interaction networks, for example, encountered in real physical space. In such cases, agents first need to explore a spatially distributed signal through movement, then reach a consensus while being able to communicate only with nearby neighbors. Collectives face several challenges in achieving precise estimation consensus, particularly due to complex behaviors emerging from the simultaneous evolution of the agents' opinions and the interaction network. While homophilic networks may facilitate collective estimation in well-connected networks, we show that disproportionate interactions with like-minded neighbors lead to the emergence of echo chambers, preventing collective consensus. Our agent-based simulation results confirm that, besides a lack of exposure to attitude-challenging opinions, seeking reaffirming information entraps agents in echo chambers. In a potential solution, agents can break free from the pull of echo chambers. We suggest an additional state where stubborn mobile agents (called Messengers) carry data and connect the disconnected clusters by physically transporting their opinions to other clusters to inform and direct the other agents. However, an agent requires a switching mechanism to determine which state to adopt. We propose a generic, novel approach based on a Dichotomous Markov Process (DMP). We show that a wide range of collective behaviors arise from the DMP. We study a continuum between task specialization with no switching (full-time Messengers), generalization with slow switching (part-time Messengers), and rapid task switching (short-time Messengers). Our results show that stubborn agents can, in various ways, help the collective escape local minima, break the echo chambers, and promote consensus in collective opinion dynamics.

Contents

Python Codes

Codes for Paper

Data Availability

Getting Started

Matlab codes: you just need to run them on a proper version of Matlab. The current version of the codes run on Version 2023.a - 2024.a. It is not likely, but other versions might need slight adjustments.

Python codes: are provided with a test code, environment requirements.txt file, and a Docker image file. Please

License

GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007

Copyright (C) 2024 Mohsen Raoufi

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Contact

Citation

If you use this software and the dataset in your research, please cite our work as following:

[1] Raoufi, M., Hamann, H., & Romanczuk, P. (2024). Messengers: Breaking Echo Chambers in Collective Opinion Dynamics with Homophily. arXiv preprint arXiv:2406.06440.

[2] Raoufi, M., Hamann, H., & Romanczuk, P. (2024). Simulation Dataset: Breaking Echo Chambers in Collective Opinion Dynamics with Homophily.

bibTex format:

``` @article{raoufi2024messengers, title={Messengers: Breaking Echo Chambers in Collective Opinion Dynamics with Homophily}, author={Raoufi, Mohsen and Hamann, Heiko and Romanczuk, Pawel}, journal={arXiv preprint arXiv:2406.06440}, year={2024} }

@article{raoufi2024simulation, title={Simulation Dataset: Breaking Echo Chambers in Collective Opinion Dynamics with Homophily}, author={Raoufi, Mohsen and Hamann, Heiko and Romanczuk, Pawel}, year={2024} } ```