74,731 research outputs found
An organization that transmits opinion to newcomers
We aim to identify the conditions under which social influence enables emergence of a shared opinion orientation among members of an organization over time, when membership is subject to continuous but partial turnover. We study an intra-organizational advice network that channels social influence over time, with a flow of joiners and leavers at regular intervals. We have been particularly inspired by a study of the Commercial Court of Paris, a judicial institution whose members are peer-elected businesspeople and are partly replaced every year. We develop an agent-based simulation of advice network evolution which incorporates a model of opinion dynamics based on a refinement of Deuant's relative agreement", combining opinion with a measure of "uncertainty" or openness to social influence. We focus on the effects on opinion of three factors, namely criteria for advisor selection, duration of membership in the organization, and new members' uncertainty. We show that criteria for interlocutor choice matter: a shared opinion is sustained over time if members select colleagues at least as experienced as themselves. Convergence of opinions appears in other congurations too, but the impact of initial opinion fades in time. Duration has an impact to the extent that the longer the time spent in the group, the stronger the possibility for convergence towards a common opinion. Finally, higher uncertainty reinforces convergence while lower uncertainty leads to coexistence of multiple opinions.social influence, advice networks, intra-organizational networks, opinion dynamics, agent-based simulation
The Naming Game in Social Networks: Community Formation and Consensus Engineering
We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat.
Mech.: Theory Exp. P06014] in empirical social networks. This stylized
agent-based model captures essential features of agreement dynamics in a
network of autonomous agents, corresponding to the development of shared
classification schemes in a network of artificial agents or opinion spreading
and social dynamics in social networks. Our study focuses on the impact that
communities in the underlying social graphs have on the outcome of the
agreement process. We find that networks with strong community structure hinder
the system from reaching global agreement; the evolution of the Naming Game in
these networks maintains clusters of coexisting opinions indefinitely. Further,
we investigate agent-based network strategies to facilitate convergence to
global consensus.Comment: The original publication is available at
http://www.springerlink.com/content/70370l311m1u0ng3
Vector opinion dynamics in a model for social influence
We present numerical simulations of a model of social influence, where the
opinion of each agent is represented by a binary vector. Agents adjust their
opinions as a result of random encounters, whenever the difference between
opinions is below a given threshold. Evolution leads to a steady state, which
highly depends on the threshold and a convergence parameter of the model. We
analyze the transition between clustered and homogeneous steady states. Results
of the cases of complete mixing and small-world networks are compared.Comment: Latex file, 14 pages and 11 figures, Accepted in Physica
Convergence Results for Two Models of Interaction
abstract: I investigate two models interacting agent systems: the first is motivated by the flocking and swarming behaviors in biological systems, while the second models opinion formation in social networks. In each setting, I define natural notions of convergence (to a ``flock" and to a ``consensus'', respectively), and study the convergence properties of each in the limit as . Specifically, I provide sufficient conditions for the convergence of both of the models, and conduct numerical experiments to study the resulting solutions.Dissertation/ThesisMasters Thesis Mathematics 201
Biased Opinion Dynamics: When the Devil Is in the Details
We investigate opinion dynamics in multi-agent networks when a bias toward
one of two possible opinions exists; for example, reflecting a status quo vs a
superior alternative. Starting with all agents sharing an initial opinion
representing the status quo, the system evolves in steps. In each step, one
agent selected uniformly at random adopts the superior opinion with some
probability , and with probability it follows an
underlying update rule to revise its opinion on the basis of those held by its
neighbors. We analyze convergence of the resulting process under two well-known
update rules, namely majority and voter. The framework we propose exhibits a
rich structure, with a non-obvious interplay between topology and underlying
update rule. For example, for the voter rule we show that the speed of
convergence bears no significant dependence on the underlying topology, whereas
the picture changes completely under the majority rule, where network density
negatively affects convergence. We believe that the model we propose is at the
same time simple, rich, and modular, affording mathematical characterization of
the interplay between bias, underlying opinion dynamics, and social structure
in a unified setting.Comment: The paper has appeared in the Proceedings of the Twenty-Ninth
International Joint Conference on Artificial Intelligence. The SOLE copyright
holder is IJCAI (International Joint Conferences on Artificial Intelligence),
all rights reserved. Link to the proceedings:
https://www.ijcai.org/Proceedings/2020/
A Multi-Agent Model for Opinion Evolution under Cognitive Biases
We generalize the DeGroot model for opinion dynamics to better capture
realistic social scenarios. We introduce a model where each agent has their own
individual cognitive biases. Society is represented as a directed graph whose
edges indicate how much agents influence one another. Biases are represented as
the functions in the square region and categorized into four
sub-regions based on the potential reactions they may elicit in an agent during
instances of opinion disagreement. Under the assumption that each bias of every
agent is a continuous function within the region of receptive but resistant
reactions (), we show that the society converges to a consensus if
the graph is strongly connected. Under the same assumption, we also establish
that the entire society converges to a unanimous opinion if and only if the
source components of the graph-namely, strongly connected components with no
external influence-converge to that opinion. We illustrate that convergence is
not guaranteed for strongly connected graphs when biases are either
discontinuous functions in or not included in . We
showcase our model through a series of examples and simulations, offering
insights into how opinions form in social networks under cognitive biases
Extremism propagation in social networks with hubs
One aspect of opinion change that has been of academic interest is the impact of people with extreme opinions (extremists) on opinion dynamics. An agent-based model has been used to study the role of small-world social network topologies on general opinion change in the presence of extremists. It has been found that opinion convergence to a single extreme occurs only when the average number of network connections for each individual is extremely high. Here, we extend the model to examine the effect of positively skewed degree distributions, in addition to small-world structures, on the types of opinion convergence that occur in the presence of extremists. We also examine what happens when extremist opinions are located on the well-connected nodes (hubs) created by the positively skewed distribution. We find that a positively skewed network topology encourages opinion convergence on a single extreme under a wider range of conditions than topologies whose degree distributions were not skewed. The importance of social position for social influence is highlighted by the result that, when positive extremists are placed on hubs, all population convergence is to the positive extreme even when there are twice as many negative extremists. Thus, our results have shown the importance of considering a positively skewed degree distribution, and in particular network hubs and social position, when examining extremist transmission
Estimating True Beliefs from Declared Opinions
Social networks often exert social pressure, causing individuals to adapt
their expressed opinions to conform to their peers. An agent in such systems
can be modeled as having a (true and unchanging) inherent belief but broadcasts
a declared opinion at each time step based on her inherent belief and the past
declared opinions of her neighbors. An important question in this setting is
parameter estimation: how to disentangle the effects of social pressure to
estimate inherent beliefs from declared opinions. To address this, Jadbabaie et
al. formulated the interacting P\'olya urn model of opinion dynamics under
social pressure and studied it on complete-graph social networks using an
aggregate estimator, and found that their estimator converges to the inherent
beliefs unless majority pressure pushes the network to consensus. In this work,
we study this model on arbitrary networks, providing an estimator which
converges to the inherent beliefs even in consensus situations. Finally, we
bound the convergence rate of our estimator in both consensus and non-consensus
scenarios; to get the bound for consensus scenarios (which converge slower than
non-consensus) we additionally found how quickly the system converges to
consensus
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