323 research outputs found
Opinion Formation and the Collective Dynamics of Risk Perception
The formation of collective opinion is a complex phenomenon that results from
the combined effects of mass media exposure and social influence between
individuals. The present work introduces a model of opinion formation
specifically designed to address risk judgments, such as attitudes towards
climate change, terrorist threats, or children vaccination. The model assumes
that people collect risk information from the media environment and exchange
them locally with other individuals. Even though individuals are initially
exposed to the same sample of information, the model predicts the emergence of
opinion polarization and clustering. In particular, numerical simulations
highlight two crucial factors that determine the collective outcome: the
propensity of individuals to search for independent information, and the
strength of social influence. This work provides a quantitative framework to
anticipate and manage how the public responds to a given risk, and could help
understanding the systemic amplification of fears and worries, or the
underestimation of real dangers
Reach and speed of judgment propagation in the laboratory
In recent years, a large body of research has demonstrated that judgments and
behaviors can propagate from person to person. Phenomena as diverse as
political mobilization, health practices, altruism, and emotional states
exhibit similar dynamics of social contagion. The precise mechanisms of
judgment propagation are not well understood, however, because it is difficult
to control for confounding factors such as homophily or dynamic network
structures. We introduce a novel experimental design that renders possible the
stringent study of judgment propagation. In this design, experimental chains of
individuals can revise their initial judgment in a visual perception task after
observing a predecessor's judgment. The positioning of a very good performer at
the top of a chain created a performance gap, which triggered waves of judgment
propagation down the chain. We evaluated the dynamics of judgment propagation
experimentally. Despite strong social influence within pairs of individuals,
the reach of judgment propagation across a chain rarely exceeded a social
distance of three to four degrees of separation. Furthermore, computer
simulations showed that the speed of judgment propagation decayed exponentially
with the social distance from the source. We show that information distortion
and the overweighting of other people's errors are two individual-level
mechanisms hindering judgment propagation at the scale of the chain. Our
results contribute to the understanding of social contagion processes, and our
experimental method offers numerous new opportunities to study judgment
propagation in the laboratory
Social Influence and the Collective Dynamics of Opinion Formation
Social influence is the process by which individuals adapt their opinion,
revise their beliefs, or change their behavior as a result of social
interactions with other people. In our strongly interconnected society, social
influence plays a prominent role in many self-organized phenomena such as
herding in cultural markets, the spread of ideas and innovations, and the
amplification of fears during epidemics. Yet, the mechanisms of opinion
formation remain poorly understood, and existing physics-based models lack
systematic empirical validation. Here, we report two controlled experiments
showing how participants answering factual questions revise their initial
judgments after being exposed to the opinion and confidence level of others.
Based on the observation of 59 experimental subjects exposed to peer-opinion
for 15 different items, we draw an influence map that describes the strength of
peer influence during interactions. A simple process model derived from our
observations demonstrates how opinions in a group of interacting people can
converge or split over repeated interactions. In particular, we identify two
major attractors of opinion: (i) the expert effect, induced by the presence of
a highly confident individual in the group, and (ii) the majority effect,
caused by the presence of a critical mass of laypeople sharing similar
opinions. Additional simulations reveal the existence of a tipping point at
which one attractor will dominate over the other, driving collective opinion in
a given direction. These findings have implications for understanding the
mechanisms of public opinion formation and managing conflicting situations in
which self-confident and better informed minorities challenge the views of a
large uninformed majority.Comment: Published Nov 05, 2013. Open access at:
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.007843
Development of a micromechanical model in interaction with parameters related to the microstructure of carbon/epoxy composites.
Gaseous Hydrogen storage under high pressure for autonomous energy application leads to non-metallic solutions for the material of vessels. The choice of wound carbon / epoxy composites was adopted for the design of storage tanks under high pressure. In this paper, the development of a micromechanical model in interaction with the microstructure parameters is presented. First a finite element analysis (FEA) allows us to perform numerical simulations on a representative volume cell based on observed microstructure to determine the local mechanical response. Then a parametric study is done. It reveals the effects of the voids on the mechanical properties. These effects identification and evaluation will be the basics knowledge bricks to build a guide design and process improvements for the vessel dome behaviours
Analytical calculation of critical perturbation amplitudes and critical densities by non-linear stability analysis of a simple traffic flow model
Driven many-particle systems with nonlinear interactions are known to often display multi-stability, i.e. depending on the respective initial condition, there may be different outcomes. Here, we study this phenomenon for traffic models, some of which show stable and linearly unstable density regimes, but areas of metastability in between. In these areas, perturbations larger than a certain critical amplitude will cause a lasting breakdown of traffic, while smaller ones will fade away. While there are common methods to study linear instability, non-linear instability had to be studied numerically in the past. Here, we present an analytical study for the optimal velocity model with a stepwise specification of the optimal velocity function and a simple kind of perturbation. Despite various approximations, the analytical results are shown to reproduce numerical results very wel
Analytical Calculation of Critical Perturbation Amplitudes and Critical Densities by Non-Linear Stability Analysis of a Simple Traffic Flow Model
Driven many-particle systems with nonlinear interactions are known to often
display multi-stability, i.e. depending on the respective initial condition,
there may be different outcomes. Here, we study this phenomenon for traffic
models, some of which show stable and linearly unstable density regimes, but
areas of metastability in between. In these areas, perturbations larger than a
certain critical amplitude will cause a lasting breakdown of traffic, while
smaller ones will fade away. While there are common methods to study linear
instability, non-linear instability had to be studied numerically in the past.
Here, we present an analytical study for the optimal velocity model with a
stepwise specification of the optimal velocity function and a simple kind of
perturbation. Despite various approximations, the analytical results are shown
to reproduce numerical results very well.Comment: For related work see http://www.soms.ethz.ch
Simple Heuristics and the Modelling of Crowd Behaviours
A crowd of pedestrians is a complex system that exhibits a rich variety of self-organized collective behaviors, such as lane formation, stop-and-go waves, or crowd turbulence. Understanding the mechanisms of crowd dynamics requires establishing a link between the local behavior of pedestrians during interactions, and the global dynamics of the crowd at high density. For this, the elaboration of a model is necessary. In this contribution, we will make a distinction between two kinds of modelling methods: outcome models that are often based on analogies with Newtonian mechanics, and process models based on concepts of cognitive science. While outcome models describe directly the movements of a pedestrian by means of repulsive forces or probabilities to move from one place to another, process models generate the movement from the bottom-up by describing the underlying cognitive process used by the pedestrian during navigation. Here, we will describe and compare two representatives of outcome and process models, namely the social force model on the one hand, and the heuristic model on the other hand. In particular, we will describe the strength and the limitations of each approach, and discuss possible future improvements for process models
Can simple transmission chains foster collective intelligence in binary-choice tasks?
In many social systems, groups of individuals can find remarkably efficient
solutions to complex cognitive problems, sometimes even outperforming a single
expert. The success of the group, however, crucially depends on how the
judgments of the group members are aggregated to produce the collective answer.
A large variety of such aggregation methods have been described in the
literature, such as averaging the independent judgments, relying on the
majority or setting up a group discussion. In the present work, we introduce a
novel approach for aggregating judgments - the transmission chain - which has
not yet been consistently evaluated in the context of collective intelligence.
In a transmission chain, all group members have access to a unique collective
solution and can improve it sequentially. Over repeated improvements, the
collective solution that emerges reflects the judgments of every group members.
We address the question of whether such a transmission chain can foster
collective intelligence for binary-choice problems. In a series of numerical
simulations, we explore the impact of various factors on the performance of the
transmission chain, such as the group size, the model parameters, and the
structure of the population. The performance of this method is compared to
those of the majority rule and the confidence-weighted majority. Finally, we
rely on two existing datasets of individuals performing a series of binary
decisions to evaluate the expected performances of the three methods
empirically. We find that the parameter space where the transmission chain has
the best performance rarely appears in real datasets. We conclude that the
transmission chain is best suited for other types of problems, such as those
that have cumulative properties
Recommended Corrective Security Measures to Address the Weaknesses Identified Within the Shapash Nuclear Research Institute
The Shapash Nuclear Research Institute (SNRI) data book was issued by the International Atomic Energy Agency (IAEA) in 2013. The hypothetical facility data book describes the hypothetical site, which is divided into two areas: the low-security area, known as the administrative area, and the very high-security area, known as the protected area. The book contains detailed descriptions of each area’s safety and security measures, along with figures of multiple buildings in both areas, and also includes information about the site’s computer networks.
This paper aims to identify security weaknesses related to the institute’s location, the Administrative Area (AA), the Protected Area (PA), and the Instrumentation and Control Technology system (ICT) within the SNRI and proposes corrective actions to improve the site’s security measures against malicious acts, based on the IAEA nuclear security series publications, and ultimately proposes a new layout for the whole site and the research reactor building presenting the changes made, using a software called Edraw Max
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