223,941 research outputs found
Validating argument-based opinion dynamics with survey experiments
The empirical validation of models remains one of the most important
challenges in opinion dynamics. In this contribution, we report on recent
developments on combining data from survey experiments with computational
models of opinion formation. We extend previous work on the empirical
assessment of an argument-based model for opinion dynamics in which biased
processing is the principle mechanism. While previous work (Banisch & Shamon,
in press) has focused on calibrating the micro mechanism with experimental data
on argument-induced opinion change, this paper concentrates on the macro level
using the empirical data gathered in the survey experiment. For this purpose,
the argument model is extended by an external source of balanced information
which allows to control for the impact of peer influence processes relative to
other noisy processes. We show that surveyed opinion distributions are matched
with a high level of accuracy in a specific region in the parameter space,
indicating an equal impact of social influence and external noise. More
importantly, the estimated strength of biased processing given the macro data
is compatible with those values that achieve high likelihood at the micro
level. The main contribution of the paper is hence to show that the extended
argument-based model provides a solid bridge from the micro processes of
argument-induced attitude change to macro level opinion distributions. Beyond
that, we review the development of argument-based models and present a new
method for the automated classification of model outcomes.Comment: Keywords: opinion dynamics, validation, empirical confirmation,
survey experiments, parameter estimation, argument communication theory,
computational social scienc
Approximation of Optimal Control Surfaces for the Bass Model with Stochastic Dynamics
The Bass diffusion equation is a well-known and established modeling approach
for describing new product adoption in a competitive market. This model also
describes diffusion phenomena in various contexts: infectious disease spread
modeling and estimation, rumor spread on social networks, prediction of
renewable energy technology markets, among others. Most of these models,
however, consider a deterministic trajectory of the associated state variable
(e.g., market-share). In reality, the diffusion process is subject to noise,
and a stochastic component must be added to the state dynamics. The stochastic
Bass model has also been studied in many areas, such as energy markets and
marketing. Exploring the stochastic version of the Bass diffusion model, we
propose in this work an approximation of (stochastic) optimal control surfaces
for a continuous-time problem arising from a skew symmetric
evolutionary game, providing the stochastic counter-part of the Fourier-based
optimal control approximation already existent in the literature
Resilience and Controllability of Dynamic Collective Behaviors
The network paradigm is used to gain insight into the structural root causes
of the resilience of consensus in dynamic collective behaviors, and to analyze
the controllability of the swarm dynamics. Here we devise the dynamic signaling
network which is the information transfer channel underpinning the swarm
dynamics of the directed interagent connectivity based on a topological
neighborhood of interactions. The study of the connectedness of the swarm
signaling network reveals the profound relationship between group size and
number of interacting neighbors, which is found to be in good agreement with
field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl.
Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic
collective behaviors enabling us to uncover that the swarm signaling network is
a homogeneous clustered small-world network, thus facilitating emergent
outcomes if connectedness is maintained. Resilience of the emergent consensus
is tested by introducing exogenous environmental noise, which ultimately
stresses how deeply intertwined are the swarm dynamics in the physical and
network spaces. The availability of the signaling network allows us to
analytically establish for the first time the number of driver agents necessary
to fully control the swarm dynamics
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions
In this article, we study the nonlinear Fokker-Planck (FP) equation that
arises as a mean-field (macroscopic) approximation of bounded confidence
opinion dynamics, where opinions are influenced by environmental noises and
opinions of radicals (stubborn individuals). The distribution of radical
opinions serves as an infinite-dimensional exogenous input to the FP equation,
visibly influencing the steady opinion profile. We establish mathematical
properties of the FP equation. In particular, we (i) show the well-posedness of
the dynamic equation, (ii) provide existence result accompanied by a
quantitative global estimate for the corresponding stationary solution, and
(iii) establish an explicit lower bound on the noise level that guarantees
exponential convergence of the dynamics to stationary state. Combining the
results in (ii) and (iii) readily yields the input-output stability of the
system for sufficiently large noises. Next, using Fourier analysis, the
structure of opinion clusters under the uniform initial distribution is
examined. Specifically, two numerical schemes for identification of
order-disorder transition and characterization of initial clustering behavior
are provided. The results of analysis are validated through several numerical
simulations of the continuum-agent model (partial differential equation) and
the corresponding discrete-agent model (interacting stochastic differential
equations) for a particular distribution of radicals
- …