29 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
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
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
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
The amplification of risk in experimental diffusion chains
Understanding how people form and revise their perception of risk is central
to designing efficient risk communication methods, eliciting risk awareness,
and avoiding unnecessary anxiety among the public. However, public responses to
hazardous events such as climate change, contagious outbreaks, and terrorist
threats are complex and difficult-to-anticipate phenomena. Although many
psychological factors influencing risk perception have been identified in the
past, it remains unclear how perceptions of risk change when propagated from
one person to another and what impact the repeated social transmission of
perceived risk has at the population scale. Here, we study the social dynamics
of risk perception by analyzing how messages detailing the benefits and harms
of a controversial antibacterial agent undergo change when passed from one
person to the next in 10-subject experimental diffusion chains. Our analyses
show that when messages are propagated through the diffusion chains, they tend
to become shorter, gradually inaccurate, and increasingly dissimilar between
chains. In contrast, the perception of risk is propagated with higher fidelity
due to participants manipulating messages to fit their preconceptions, thereby
influencing the judgments of subsequent participants. Computer simulations
implementing this simple influence mechanism show that small judgment biases
tend to become more extreme, even when the injected message contradicts
preconceived risk judgments. Our results provide quantitative insights into the
social amplification of risk perception, and can help policy makers better
anticipate and manage the public response to emerging threats.Comment: Published online in PNAS Early Edition (open-access):
http://www.pnas.org/content/early/2015/04/14/142188311
Modeling crowd dynamics through coarse-grained data analysis
Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of crowd traffic management systems, whereby observations of crowds can be coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and densities. We design and run a laboratory experiments involving a total of 119 participants walking in opposite directions in a circular corridor and show that the model is able to accurately capture the experimental data in a typical crowd forecasting situation. Finally, we propose a simple segregation strategy for enhancing the traffic efficiency, and use the BM model to determine the conditions under which this strategy would be beneficial. The BM model, therefore, could serve as a building block to develop on the fly prediction of crowd movements and help deploying real-time crowd optimization strategies
Saving Human Lives: What Complexity Science and Information Systems can Contribute
We discuss models and data of crowd disasters, crime, terrorism, war and
disease spreading to show that conventional recipes, such as deterrence
strategies, are often not effective and sufficient to contain them. Many common
approaches do not provide a good picture of the actual system behavior, because
they neglect feedback loops, instabilities and cascade effects. The complex and
often counter-intuitive behavior of social systems and their macro-level
collective dynamics can be better understood by means of complexity science. We
highlight that a suitable system design and management can help to stop
undesirable cascade effects and to enable favorable kinds of self-organization
in the system. In such a way, complexity science can help to save human lives.Comment: 67 pages, 25 figures; accepted for publication in Journal of
Statistical Physics [for related work see http://www.futurict.eu/