Bounded-Confidence Models of Opinion Dynamics with Neighborhood Effects
CoRR(2024)
摘要
As people's opinions change, their social networks typically coevolve with
them. People are often more susceptible to influence by people with similar
opinions than by people with dissimilar opinions. In a bounded-confidence model
(BCM) of opinion dynamics, interacting individuals influence each other through
dyadic influence if and only if their opinions are sufficiently similar to each
other. We introduce `neighborhood BCMs' (NBCMs) that include both the usual
dyadic influence and a transitive influence, which models the effect of friends
of a friend when determining whether or not an interaction with a friend
influences an individual. In this transitive influence, an individual's opinion
is influenced by a neighbor when, on average, the opinions of the neighbor's
neighbors are sufficiently similar to their own opinion. We formulate
neighborhood Deffuant–Weisbuch (NDW) and neighborhood Hegselmann–Krause (NHK)
BCMs. We simulate our NDW model on time-independent networks and observe
interesting opinion states that cannot occur in an associated baseline DW
model. We also simulate our NDW model on adaptive networks that coevolve with
opinions by changing its structure through `transitive homophily'. An
individual that breaks a tie to one of its neighbors and then rewires that tie
to a new individual, with a preference for individuals with a mean neighbor
opinion that is closer to that individual's opinion. We explore how the
qualitative opinion dynamics and network properties of our time-independent and
adaptive NDWM models change as we adjust the relative proportions of dyadic and
transitive influence. Finally, we study a two-layer opinion–disease model in
which we couple our NDW model with disease spread through a shared adaptive
network that can change both on the opinion layer and on the disease layer and
we examine how the opinion dynamics affect disease spread.
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