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trust=0.3
trust=0.5
trust=0.8
trust=0.3
trust=0.5
trust=0.8
0.32
0.24
0.16
0.08
0.32
0.24
0.16
0.08
% of innovators
% of innovators
(a) Diffusion without bridges
(b) Diffusion with bridges
Fig. 6.
The diffusion of a new argument among caves. On the x-axis, the initial per-
centage of innovators. On the y-axis, the percentage of agents that believes
{a,c, e}
after the simulation.
is allowed (8% of total population). It is worthwhile noticing that, if bridges
are not allowed, the proportion of agents who know
{
a, c, e
}
at the end of the
simulation is more or less equal to the beginning, and the level of trust does not
influence agents much. When bridges are permitted,
{
a, c, e
}
has a much higher
probability to spread and, interestingly, results are much sharper: if
}
reaches a tipping point, it wins the whole population, i.e., all agents change their
minds and believe
{
a, c, e
gets forgotten also by innovators.
We conclude that bridges not only permit the diffusion of new ideas, but are
the real key for innovations to happen, provided they succeed to overcome the
threshold.
{
a, c, e
}
,otherwise
{
a, c, e
}
6 Conclusions
Using a network representation at different levels (social embedding and infor-
mation), we have built a simple framework for social agents where reasoning
is explicitly represented. We used abstract argumentation and argumentative
theory of reasoning to build agents that exchange information through simu-
lated dialogues. We demonstrated that our approach is, in principle, sucient
to reproduce two macro-behavior embedded in Granovetter's theory, i.e., the
tendency to inclusion of weak ties and a competitive advantage for non-isolated
caves. We showed that some argumentative frameworks are stronger than others
and thus can, in principle, spread more eciently when large audiences come
into play. Finally, we also found that a small amount of “argumentative inno-
vators” can successfully spread their opinions among a population, even at very
low threshold.
As future work, we plan to further investigate patterns, strengths and weak-
nesses of
AF
s from a social science perspective (e.g., to understand which argu-
mentation semantics better model human behavior, and if/why some opinions
