2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | L.11.3 | ||
| Paper Title | Polarization in Attraction-Repulsion Models | ||
| Authors | Elisabetta Cornacchia, École Polytechnique Fédérale de Lausanne, Switzerland; Neta Singer, Columbia University, United States; Emmanuel Abbe, École Polytechnique Fédérale de Lausanne, Switzerland | ||
| Session | L.11: Learning Theory II | ||
| Presentation | Lecture | ||
| Track | Statistics and Learning Theory | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | This paper introduces a model for opinion dynamics, where at each time step, randomly selected agents see their opinions — modeled as scalars in [0, 1] — evolve depending on a local interaction function. In the classical Bounded Confidence Model, agents opinions get attracted when they are close enough. The proposed model extends this by adding a repulsion component, which models the effect of opinions getting further pushed away when dissimilar enough. With this repulsion component added, and under a repulsion-attraction cleavage assumption, it is shown that a new stable configuration emerges beyond the classical consensus configuration, namely the polarization configuration. More specifically, it is shown that total consensus and total polarization are the only two possible limiting configurations. The paper further provides an analysis of the infinite population regime in dimension 1 and higher, with a phase transition phenomenon conjectured and backed heuristically. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
