20,523 research outputs found
Collective dynamics of belief evolution under cognitive coherence and social conformity
Human history has been marked by social instability and conflict, often
driven by the irreconcilability of opposing sets of beliefs, ideologies, and
religious dogmas. The dynamics of belief systems has been studied mainly from
two distinct perspectives, namely how cognitive biases lead to individual
belief rigidity and how social influence leads to social conformity. Here we
propose a unifying framework that connects cognitive and social forces together
in order to study the dynamics of societal belief evolution. Each individual is
endowed with a network of interacting beliefs that evolves through interaction
with other individuals in a social network. The adoption of beliefs is affected
by both internal coherence and social conformity. Our framework explains how
social instabilities can arise in otherwise homogeneous populations, how small
numbers of zealots with highly coherent beliefs can overturn societal
consensus, and how belief rigidity protects fringe groups and cults against
invasion from mainstream beliefs, allowing them to persist and even thrive in
larger societies. Our results suggest that strong consensus may be insufficient
to guarantee social stability, that the cognitive coherence of belief-systems
is vital in determining their ability to spread, and that coherent
belief-systems may pose a serious problem for resolving social polarization,
due to their ability to prevent consensus even under high levels of social
exposure. We therefore argue that the inclusion of cognitive factors into a
social model is crucial in providing a more complete picture of collective
human dynamics
Governance in Social Media: A case study of the Wikipedia promotion process
Social media sites are often guided by a core group of committed users
engaged in various forms of governance. A crucial aspect of this type of
governance is deliberation, in which such a group reaches decisions on issues
of importance to the site. Despite its crucial --- though subtle --- role in
how a number of prominent social media sites function, there has been
relatively little investigation of the deliberative aspects of social media
governance. Here we explore this issue, investigating a particular deliberative
process that is extensive, public, and recorded: the promotion of Wikipedia
admins, which is determined by elections that engage committed members of the
Wikipedia community. We find that the group decision-making at the heart of
this process exhibits several fundamental forms of relative assessment. First
we observe that the chance that a voter will support a candidate is strongly
dependent on the relationship between characteristics of the voter and the
candidate. Second we investigate how both individual voter decisions and
overall election outcomes can be based on models that take into account the
sequential, public nature of the voting
Voter model on a directed network: Role of bidirectional opinion exchanges
The voter model with the node update rule is numerically investigated on a
directed network. We start from a directed hierarchical tree, and split and
rewire each incoming arc at the probability . In order to discriminate the
better and worse opinions, we break the symmetry () by
giving a little more preference to the opinion . It is found that
as becomes larger, introducing more complicated pattern of information flow
channels, and as the network size becomes larger, the system eventually
evolves to the state in which more voters agree on the better opinion, even
though the voter at the top of the hierarchy keeps the worse opinion. We also
find that the pure hierarchical tree makes opinion agreement very fast, while
the final absorbing state can easily be influenced by voters at the higher
ranks. On the other hand, although the ordering occurs much slower, the
existence of complicated pattern of bidirectional information flow allows the
system to agree on the better opinion.Comment: 5 pages, 3 figures, Phys. Rev. E (in press
Foundations for Civic Impact: Advocacy and Civic Engagement Toolkit for Private Foundations
Offers guidance for private foundations on supporting grantees' policy and civic engagement activities, including rationale, rules for private foundations as grantmakers and as advocates, sample grantmaking materials, success stories, and resources
Collective intelligence: aggregation of information from neighbors in a guessing game
Complex systems show the capacity to aggregate information and to display
coordinated activity. In the case of social systems the interaction of
different individuals leads to the emergence of norms, trends in political
positions, opinions, cultural traits, and even scientific progress. Examples of
collective behavior can be observed in activities like the Wikipedia and Linux,
where individuals aggregate their knowledge for the benefit of the community,
and citizen science, where the potential of collectives to solve complex
problems is exploited. Here, we conducted an online experiment to investigate
the performance of a collective when solving a guessing problem in which each
actor is endowed with partial information and placed as the nodes of an
interaction network. We measure the performance of the collective in terms of
the temporal evolution of the accuracy, finding no statistical difference in
the performance for two classes of networks, regular lattices and random
networks. We also determine that a Bayesian description captures the behavior
pattern the individuals follow in aggregating information from neighbors to
make decisions. In comparison with other simple decision models, the strategy
followed by the players reveals a suboptimal performance of the collective. Our
contribution provides the basis for the micro-macro connection between
individual based descriptions and collective phenomena.Comment: 9 pages, 9 figure
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