+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on Google+Follow on Google+
Follow on LinkedInFollow on LinkedIn

+ Translate

Competing opinions and stubborness: Connecting models to data

Physical Review. E 93(3): 032305-032305

Competing opinions and stubborness: Connecting models to data

We introduce a general contagionlike model for competing opinions that includes dynamic resistance to alternative opinions. We show that this model can describe candidate vote distributions, spatial vote correlations, and a slow approach to opinion consensus with sensible parameter values. These empirical properties of large group dynamics, previously understood using distinct models, may be different aspects of human behavior that can be captured by a more unified model, such as the one introduced in this paper.

Accession: 057474768

PMID: 27078364

DOI: 10.1103/PhysRevE.93.032305

Related references

Delva, W.; Leventhal, G.E.; Helleringer, Séphane., 2016: Connecting the dots: network data and models in HIV epidemiology. Effective HIV prevention requires knowledge of the structure and dynamics of the social networks across which infections are transmitted. These networks most commonly comprise chains of sexual relationships, but in some populations, sharing of con...

Mitchell, R.M.; Whitlock, R.H.; Gröhn, Yö.T.; Schukken, Y.H., 2015: Back to the real world: connecting models with data. Mathematical models for infectious disease are often used to improve our understanding of infection biology or to evaluate the potential efficacy of intervention programs. Here, we develop a mathematical model that aims to describe infection dynam...

Alencar, A.P.; Singer, J.M.; Rocha, F.Marcelo.M., 2012: Competing regression models for longitudinal data. The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a pr...

Bojak, I.; Oostendorp, T.F.; Reid, A.T.; Kötter, R., 2010: Connecting mean field models of neural activity to EEG and fMRI data. Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical...

Florian Hartig; James Dyke; Thomas Hickler; Steven I.H.ggins; Robert B.O.Hara; Simon Scheiter; Andreas Huth, 2012: Connecting dynamic vegetation models to data – an inverse perspective. Dynamic vegetation models provide process-based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside th...

A.W.Kimball, 1969: Models for the estimation of competing risks from grouped data. Biometrics 25(2): 329-337

Ilin, R., 2012: Unsupervised learning of categorical data with competing models. This paper considers the unsupervised learning of high-dimensional binary feature vectors representing categorical information. A cognitively inspired framework, referred to as modeling fields theory (MFT), is utilized as the basic methodology. A...

Lau, B.; Cole, S.R.; Gange, S.J., 2009: Competing risk regression models for epidemiologic data. Competing events can preclude the event of interest from occurring in epidemiologic data and can be analyzed by using extensions of survival analysis methods. In this paper, the authors outline 3 regression approaches for estimating 2 key quantiti...

Larson, M.G., 1984: Covariate analysis of competing-risks data with log-linear models. A general system of log-linear modeling is proposed for analysis of competing-risks data with discrete covariates. The instantaneous cause-specific failure rates, approximated by step-functions, are analyzed by techniques for multidimensional cont...

Xu, L.; Paterson, A.D.; Turpin, W.; Xu, W., 2016: Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data. Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing meth...