  People's opinions evolve over time as they interact with their friends,
family, colleagues, and others. In the study of opinion dynamics on networks,
one often encodes interactions between people in the form of dyadic
relationships, but many social interactions in real life are polyadic (i.e.,
they involve three or more people). In this paper, we extend an asynchronous
bounded-confidence model (BCM) on graphs, in which nodes are connected pairwise
by edges, to an asynchronous BCM on hypergraphs, in which arbitrarily many
nodes can be connected by a single hyperedge. We show that our hypergraph BCM
converges to consensus under a wide range of initial conditions for the
opinions of the nodes, including for non-uniform and asymmetric initial opinion
distributions. We also show that, under suitable conditions, echo chambers can
form on hypergraphs with community structure. We demonstrate that the opinions
of individuals can sometimes jump from one opinion cluster to another in a
single time step, a phenomenon (which we call ``opinion jumping'') that is not
possible in standard dyadic BCMs. Additionally, we observe that there is a
phase transition in the convergence time on {a complete hypergraph} when the
variance $\sigma^2$ of the initial opinion distribution equals the confidence
bound $c$. We prove that the convergence time grows at least exponentially fast
with the number of nodes when $\sigma^2 > c$ and the initial opinions are
normally distributed. Therefore, to determine the convergence properties of our
hypergraph BCM when the variance and the number of hyperedges are both large,
it is necessary to use analytical methods instead of relying only on Monte
Carlo simulations.
