255,116 research outputs found
Cross-inhibition leads to group consensus despite the presence of strongly opinionated minorities and asocial behaviour
Strongly opinionated minorities can have a dramatic impact on the opinion
dynamics of a large population. Two factions of inflexible minorities,
polarised into two competing opinions, could lead the entire population to
persistent indecision. Equivalently, populations can remain undecided when
individuals sporadically change their opinion based on individual information
rather than social information. Our analysis compares the cross-inhibition
model with the voter model for decisions between equally good alternatives, and
with the weighted voter model for decisions among alternatives characterised by
different qualities. Here we show that cross-inhibition, differently from the
other two models, is a simple mechanism, ubiquitous in collective biological
systems, that allows the population to reach a stable majority for one
alternative even in the presence of asocial behaviour. The results predicted by
the mean-field models are confirmed by experiments with swarms of 100 locally
interacting robots. This work suggests an answer to the longstanding question
of why inhibitory signals are widespread in natural systems of collective
decision making, and, at the same time, it proposes an efficient mechanism for
designing resilient swarms of minimalistic robots
Polarization and opinion analysis in an online argumentation system for collaborative decision support
Argumentation is an important process in a collaborative decision making environment. Argumentation from a large number of stakeholders often produces a large argumentation tree. It is challenging to comprehend such an argumentation tree without intelligent analysis tools. Also, limited decision support is provided for its analysis by the existing argumentation systems. In an argumentation process, stakeholders tend to polarize on their opinions, and form polarization groups. Each group is usually led by a group leader. Polarization groups often overlap and a stakeholder is a member of multiple polarization groups. Identifying polarization groups and quantifying a stakeholder\u27s degree of membership in multiple polarization groups helps the decision maker understand both the social dynamics and the post-decision effects on each group.
Frameworks are developed in this dissertation to identify both polarization groups and quantify a stakeholder\u27s degree of membership in multiple polarization groups. These tasks are performed by quantifying opinions of stakeholders using argumentation reduction fuzzy inference system and further clustering opinions based on K-means and Fuzzy c-means algorithms.
Assessing the collective opinion of the group on individual arguments is also important. This helps stakeholders understand individual arguments from the collective perspective of the group. A framework is developed to derive the collective assessment score of individual arguments in a tree using the argumentation reduction inference system. Further, these arguments are clustered using argument strength and collective assessment score to identify clusters of arguments with collective support and collective attack.
Identifying outlier opinions in an argumentation tree helps in understanding opinions that are further away from the mean group opinion in the opinion space. Outlier opinions may exist from two perspectives in argumentation: individual viewpoint and collective viewpoint of the group. A framework is developed in this dissertation to address this challenge from both perspectives.
Evaluation of the methods is also presented and it shows that the proposed methods are effective in identifying polarization groups and outlier opinions. The information produced by these methods help decision makers and stakeholders in making more informed decisions --Abstract, pages iii-iv
Kinetic models of collective decision-making in the presence of equality bias
We introduce and discuss kinetic models describing the influence of the
competence in the evolution of decisions in a multi-agent system. The original
exchange mechanism, which is based on the human tendency to compromise and
change opinion through self-thinking, is here modified to include the role of
the agents' competence. In particular, we take into account the agents'
tendency to behave in the same way as if they were as good, or as bad, as their
partner: the so-called equality bias. This occurred in a situation where a wide
gap separated the competence of group members. We discuss the main properties
of the kinetic models and numerically investigate some examples of collective
decision under the influence of the equality bias. The results confirm that the
equality bias leads the group to suboptimal decisions
Multi-agent decision-making dynamics inspired by honeybees
When choosing between candidate nest sites, a honeybee swarm reliably chooses
the most valuable site and even when faced with the choice between near-equal
value sites, it makes highly efficient decisions. Value-sensitive
decision-making is enabled by a distributed social effort among the honeybees,
and it leads to decision-making dynamics of the swarm that are remarkably
robust to perturbation and adaptive to change. To explore and generalize these
features to other networks, we design distributed multi-agent network dynamics
that exhibit a pitchfork bifurcation, ubiquitous in biological models of
decision-making. Using tools of nonlinear dynamics we show how the designed
agent-based dynamics recover the high performing value-sensitive
decision-making of the honeybees and rigorously connect investigation of
mechanisms of animal group decision-making to systematic, bio-inspired control
of multi-agent network systems. We further present a distributed adaptive
bifurcation control law and prove how it enhances the network decision-making
performance beyond that observed in swarms
Hierarchical Consensus Formation Reduces the Influence of Opinion Bias
We study the role of hierarchical structures in a simple model of collective
consensus formation based on the bounded confidence model with continuous
individual opinions. For the particular variation of this model considered in
this paper, we assume that a bias towards an extreme opinion is introduced
whenever two individuals interact and form a common decision. As a simple proxy
for hierarchical social structures, we introduce a two-step decision making
process in which in the second step groups of like-minded individuals are
replaced by representatives once they have reached local consensus, and the
representatives in turn form a collective decision in a downstream process. We
find that the introduction of such a hierarchical decision making structure can
improve consensus formation, in the sense that the eventual collective opinion
is closer to the true average of individual opinions than without it. In
particular, we numerically study how the size of groups of like-minded
individuals being represented by delegate individuals affects the impact of the
bias on the final population-wide consensus. These results are of interest for
the design of organisational policies and the optimisation of hierarchical
structures in the context of group decision making.Comment: 12 pages, 5 figure
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