12,858 research outputs found
The Leviathan model: Absolute dominance, generalised distrust, small worlds and other patterns emerging from combining vanity with opinion propagation
We propose an opinion dynamics model that combines processes of vanity and
opinion propagation. The interactions take place between randomly chosen pairs.
During an interaction, the agents propagate their opinions about themselves and
about other people they know. Moreover, each individual is subject to vanity:
if her interlocutor seems to value her highly, then she increases her opinion
about this interlocutor. On the contrary she tends to decrease her opinion
about those who seem to undervalue her. The combination of these dynamics with
the hypothesis that the opinion propagation is more efficient when coming from
highly valued individuals, leads to different patterns when varying the
parameters. For instance, for some parameters the positive opinion links
between individuals generate a small world network. In one of the patterns,
absolute dominance of one agent alternates with a state of generalised
distrust, where all agents have a very low opinion of all the others (including
themselves). We provide some explanations of the mechanisms behind these
emergent behaviors and finally propose a discussion about their interestComment: Improved version after referees comment
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
Multi-choice opinion dynamics model based on Latane theory
In this paper Nowak--Szamrej-Latan\'e model is reconsidered. This
computerised model of opinion formation bases on Latan\'e theory of social
impact. We modify this model to allow for multi (more than two) opinions. With
computer simulations we show that in the modified model the signatures of
order/disorder phase transition are still observed. The transition may be
observed in the average fraction of actors sharing the -th opinion, its
variation and also average number of clusters of actors with the same opinion
and the average size of the largest cluster of actors sharing the same opinion.
Also an influence of model control parameters on simulation results is shortly
reviewed. For a homogeneous society with identical actors' supportiveness and
persuasiveness the critical social temperature decreases with an increase
of available opinions from () via 4.7, 4.1 to for
, 4, 5, respectively.Comment: 12 page
Contributions to the Modelling of Auditory Hallucinations, Social robotics, and Multiagent Systems
165 p.The Thesis covers three diverse lines of work that have been tackled with the central endeavor of modeling and understanding the phenomena under consideration. Firstly, the Thesis works on the problem of finding brain connectivity biomarkers of auditory hallucinations, a rather frequent phenomena that can be related some pathologies, but which is also present in healthy population. We apply machine learning techniques to assess the significance of effective brain connections extracted by either dynamical causal modeling or Granger causality. Secondly, the Thesis deals with the usefulness of social robotics strorytelling as a therapeutic tools for children at risk of exclussion. The Thesis reports on the observations gathered in several therapeutic sessions carried out in Spain and Bulgaria, under the supervision of tutors and caregivers. Thirdly, the Thesis deals with the spatio-temporal dynamic modeling of social agents trying to explain the phenomena of opinion survival of the social minorities. The Thesis proposes a eco-social model endowed with spatial mobility of the agents. Such mobility and the spatial perception of the agents are found to be strong mechanisms explaining opinion propagation and survival
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
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