An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks
Bibliographic Data
| ID | 19136393 |
|---|---|
| Authors | Unchitta Kan (0000-0002-6215-4406, George Mason University), Michelle Feng (0000-0001-5048-6430, California Institute of Technology), Mary A Porter (0000-0002-5166-0717, Santa Fe Institute), Mason A Porter |
| Year | 2021 |
| Publication date | 2021-12-10 |
| Open Access | No |
| Type | PREPRINT |
| Publisher | Center for Open Science (PUBLISHER) |
| DOI | 10.31235/osf.io/gcxnf |
| OpenAlex | W4200515694 |
| Language | EN |
| Citations received | 1 |
| References cited | 11 |
Individuals who interact with each other in social networks often exchange ideas and influence each other's opinions. A popular approach to studying the dynamics of opinion spread on networks is by examining bounded-confidence (BC) models, in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other opinions if they lie within some confidence bound of their own opinion. We extend the Deffuant--Weisbuch (DW) model, which is a well-known BC model, by studying opinion dynamics that coevolve with network structure. We propose an adaptive variant of the DW model in which the nodes of a network can (1) alter their opinion when they interact with a neighboring node and (2) break a connection with a neighbor based on an opinion tolerance threshold and then form a new connection to a node following the principle of homophily. This opinion tolerance threshold acts as a threshold to determine if the opinions of adjacent nodes are sufficiently different to be viewed as discordant. We find that our adaptive BC model requires a larger confidence bound than the standard DW model for the nodes of a network to achieve a consensus. Interestingly, our model includes regions with `pseudo-consensus' steady states, in which there exist two subclusters within an opinion-consensus group that deviate from each other by a small amount. We conduct extensive numerical simulations of our adaptive BC model and examine the importance of early-time dynamics and nodes with initial moderate opinions for achieving consensus. We also examine the effects of coevolution on the convergence time of the dynamics
Bounded function · Homophily · Physics · Upper and lower bounds · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Psychology · Social Capital and Networks · Social Psychology · Theoretical Computer Science
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |