58,090 research outputs found
Consensus as a Nash Equilibrium of a Dynamic Game
Consensus formation in a social network is modeled by a dynamic game of a
prescribed duration played by members of the network. Each member independently
minimizes a cost function that represents his/her motive. An integral cost
function penalizes a member's differences of opinion from the others as well as
from his/her own initial opinion, weighted by influence and stubbornness
parameters. Each member uses its rate of change of opinion as a control input.
This defines a dynamic non-cooperative game that turns out to have a unique
Nash equilibrium. Analytic explicit expressions are derived for the opinion
trajectory of each member for two representative cases obtained by suitable
assumptions on the graph topology of the network. These trajectories are then
examined under different assumptions on the relative sizes of the influence and
stubbornness parameters that appear in the cost functions.Comment: 7 pages, 9 figure, Pre-print from the Proceedings of the 12th
International Conference on Signal Image Technology and Internet-based
Systems (SITIS), 201
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
Generalized Opinion Dynamics from Local Optimization Rules
We study generalizations of the Hegselmann-Krause (HK) model for opinion
dynamics, incorporating features and parameters that are natural components of
observed social systems. The first generalization is one where the strength of
influence depends on the distance of the agents' opinions. Under this setup, we
identify conditions under which the opinions converge in finite time, and
provide a qualitative characterization of the equilibrium. We interpret the HK
model opinion update rule as a quadratic cost-minimization rule. This enables a
second generalization: a family of update rules which possess different
equilibrium properties. Subsequently, we investigate models in which a external
force can behave strategically to modulate/influence user updates. We consider
cases where this external force can introduce additional agents and cases where
they can modify the cost structures for other agents. We describe and analyze
some strategies through which such modulation may be possible in an
order-optimal manner. Our simulations demonstrate that generalized dynamics
differ qualitatively and quantitatively from traditional HK dynamics.Comment: 20 pages, under revie
Collective Decision Dynamics in the Presence of External Drivers
We develop a sequence of models describing information transmission and
decision dynamics for a network of individual agents subject to multiple
sources of influence. Our general framework is set in the context of an
impending natural disaster, where individuals, represented by nodes on the
network, must decide whether or not to evacuate. Sources of influence include a
one-to-many externally driven global broadcast as well as pairwise
interactions, across links in the network, in which agents transmit either
continuous opinions or binary actions. We consider both uniform and variable
threshold rules on the individual opinion as baseline models for
decision-making. Our results indicate that 1) social networks lead to
clustering and cohesive action among individuals, 2) binary information
introduces high temporal variability and stagnation, and 3) information
transmission over the network can either facilitate or hinder action adoption,
depending on the influence of the global broadcast relative to the social
network. Our framework highlights the essential role of local interactions
between agents in predicting collective behavior of the population as a whole.Comment: 14 pages, 7 figure
Opinion dynamics: models, extensions and external effects
Recently, social phenomena have received a lot of attention not only from
social scientists, but also from physicists, mathematicians and computer
scientists, in the emerging interdisciplinary field of complex system science.
Opinion dynamics is one of the processes studied, since opinions are the
drivers of human behaviour, and play a crucial role in many global challenges
that our complex world and societies are facing: global financial crises,
global pandemics, growth of cities, urbanisation and migration patterns, and
last but not least important, climate change and environmental sustainability
and protection. Opinion formation is a complex process affected by the
interplay of different elements, including the individual predisposition, the
influence of positive and negative peer interaction (social networks playing a
crucial role in this respect), the information each individual is exposed to,
and many others. Several models inspired from those in use in physics have been
developed to encompass many of these elements, and to allow for the
identification of the mechanisms involved in the opinion formation process and
the understanding of their role, with the practical aim of simulating opinion
formation and spreading under various conditions. These modelling schemes range
from binary simple models such as the voter model, to multi-dimensional
continuous approaches. Here, we provide a review of recent methods, focusing on
models employing both peer interaction and external information, and
emphasising the role that less studied mechanisms, such as disagreement, has in
driving the opinion dynamics. [...]Comment: 42 pages, 6 figure
The roundtable: an abstract model of conversation dynamics
Is it possible to abstract a formal mechanism originating schisms and
governing the size evolution of social conversations? In this work a
constructive solution to such problem is proposed: an abstract model of a
generic N-party turn-taking conversation. The model develops from simple yet
realistic assumptions derived from experimental evidence, abstracts from
conversation content and semantics while including topological information, and
is driven by stochastic dynamics. We find that a single mechanism - namely the
dynamics of conversational party's individual fitness, as related to
conversation size - controls the development of the self-organized schisming
phenomenon. Potential generalizations of the model - including individual
traits and preferences, memory effects and more elaborated conversational
topologies - may find important applications also in other fields of research,
where dynamically-interacting and networked agents play a fundamental role.Comment: 18 pages, 4 figures, to be published in Journal of Artificial
Societies and Social Simulatio
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