Explaining opinion polarisation with opinion copulas.
Ontology highlight
ABSTRACT:
SUBMITTER: Askitas N
PROVIDER: S-EPMC5568744 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
An empirically founded and widely established driving force in opinion dynamics is homophily i.e. the tendency of "birds of a feather" to "flock together". The closer our opinions are the more likely it is that we will interact and converge. Models using these assumptions are called bounded confidence models (BCM) as they assume a tolerance threshold after which interaction is unlikely. They are known to produce one or more clusters, depending on the size of the bound, with more than one cluster ...[more]
Similar Datasets
Project description:Canonical correlation analysis (CCA) is a common method used to estimate the associations between two different sets of variables by maximizing the Pearson correlation between linear combinations of the two sets of variables. We propose a version of CCA for transelliptical distributions with an elliptical copula using pairwise Kendall's tau to estimate a latent scatter matrix. Because Kendall's tau relies only on the ranks of the data this method does not make any assumptions about the marginal distributions of the variables, and is valid when moments do not exist. We establish consistency and asymptotic normality for canonical directions and correlations estimated using Kendall's tau. Simulations indicate that this estimator outperforms standard CCA for data generated from heavy tailed elliptical distributions. Our method also identifies more meaningful relationships when the marginal distributions are skewed. We also propose a method for testing for non-zero canonical correlations using bootstrap methods. This testing procedure does not require any assumptions on the joint distribution of the variables and works for all elliptical copulas. This is in contrast to permutation tests which are only valid when data are generated from a distribution with a Gaussian copula. This method's practical utility is shown in an analysis of the association between radial diffusivity in white matter tracts and cognitive tests scores for six-year-old children from the Early Brain Development Study at UNC-Chapel Hill. An R package implementing this method is available at github.com/blangworthy/transCCA.
| S-EPMC7839949 | biostudies-literature
Project description:Multi-scale methods that separate different time or spatial scales are among the most powerful techniques in physics, especially in applications that study nonlinear systems with noise. When the time scales (noise and perturbation) are of the same order, the scales separation becomes impossible. Thus, the multi-scale approach has to be modified to characterise a variety of noise-induced phenomena. Here, based on stochastic modelling and analytical study, we demonstrate in terms of the fluctuation-induced phenomena and Hurst R/S analysis metrics that the matching scales of random birefringence and pump-signal states of polarisation interaction in a fibre Raman amplifier results in a new random birefringence-mediated phenomenon, which is similar to stochastic anti-resonance. The observed phenomenon, apart from the fundamental interest, provides a base for advancing multi-scale methods with application to different coupled nonlinear systems ranging from lasers (multimode, mode-locked, random, etc.) to nanostructures (light-mediated conformation of molecules and chemical reactions, Brownian motors, etc.).
| S-EPMC6059843 | biostudies-literature
Project description:Many applications of risk analysis require us to jointly model multiple uncertain quantities. Bayesian networks and copulas are two common approaches to modeling joint uncertainties with probability distributions. This article focuses on new methodologies for copulas by developing work of Cooke, Bedford, Kurowica, and others on vines as a way of constructing higher dimensional distributions that do not suffer from some of the restrictions of alternatives such as the multivariate Gaussian copula. The article provides a fundamental approximation result, demonstrating that we can approximate any density as closely as we like using vines. It further operationalizes this result by showing how minimum information copulas can be used to provide parametric classes of copulas that have such good levels of approximation. We extend previous approaches using vines by considering nonconstant conditional dependencies, which are particularly relevant in financial risk modeling. We discuss how such models may be quantified, in terms of expert judgment or by fitting data, and illustrate the approach by modeling two financial data sets.
| S-EPMC4989465 | biostudies-literature
Project description:We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity-based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over-dispersion, and/or zero-inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modeled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint absences in the space of the original species variables. A permutation-based filter with exact family-wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail a Monte Carlo expectation maximization algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity-based settings. Our proposed analysis pathway provides new opportunities to combine model-based approaches with dissimilarity-based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve.
| S-EPMC6434552 | biostudies-literature
Project description:Environmental exposures typically involve mixtures of pollutants, which must be understood to evaluate cumulative risks, that is, the likelihood of adverse health effects arising from two or more chemicals. This study uses several powerful techniques to characterize dependency structures of mixture components in personal exposure measurements of volatile organic compounds (VOCs) with aims of advancing the understanding of environmental mixtures, improving the ability to model mixture components in a statistically valid manner, and demonstrating broadly applicable techniques. We first describe characteristics of mixtures and introduce several terms, including the mixture fraction which represents a mixture component's share of the total concentration of the mixture. Next, using VOC exposure data collected in the Relationship of Indoor Outdoor and Personal Air (RIOPA) study, mixtures are identified using positive matrix factorization (PMF) and by toxicological mode of action. Dependency structures of mixture components are examined using mixture fractions and modeled using copulas, which address dependencies of multiple variables across the entire distribution. Five candidate copulas (Gaussian, t, Gumbel, Clayton, and Frank) are evaluated, and the performance of fitted models was evaluated using simulation and mixture fractions. Cumulative cancer risks are calculated for mixtures, and results from copulas and multivariate lognormal models are compared to risks calculated using the observed data. Results obtained using the RIOPA dataset showed four VOC mixtures, representing gasoline vapor, vehicle exhaust, chlorinated solvents and disinfection by-products, and cleaning products and odorants. Often, a single compound dominated the mixture, however, mixture fractions were generally heterogeneous in that the VOC composition of the mixture changed with concentration. Three mixtures were identified by mode of action, representing VOCs associated with hematopoietic, liver and renal tumors. Estimated lifetime cumulative cancer risks exceeded 10(-3) for about 10% of RIOPA participants. Factors affecting the likelihood of high concentration mixtures included city, participant ethnicity, and house air exchange rates. The dependency structures of the VOC mixtures fitted Gumbel (two mixtures) and t (four mixtures) copulas, types that emphasize tail dependencies. Significantly, the copulas reproduced both risk predictions and exposure fractions with a high degree of accuracy, and performed better than multivariate lognormal distributions. Copulas may be the method of choice for VOC mixtures, particularly for the highest exposures or extreme events, cases that poorly fit lognormal distributions and that represent the greatest risks.
| S-EPMC4233140 | biostudies-literature
Project description:Dimension reduction techniques, such as partial least squares, are useful for computing summary measures and examining relationships in complex settings. Partial least squares requires an estimate of the covariance matrix as a first step in the analysis, making this estimate critical to the results. In addition, the covariance matrix also forms the basis for other techniques in multivariate analysis, such as principal component analysis and independent component analysis. This paper has been motivated by an example from an imaging study in Alzheimer's disease where there is complete separation between Alzheimer's and control subjects for one of the imaging modalities. This separation occurs in one block of variables and does not occur with the second block of variables resulting in inaccurate estimates of the covariance. We propose the use of a copula to obtain estimates of the covariance in this setting, where one set of variables comes from a mixture distribution. Simulation studies show that the proposed estimator is an improvement over the standard estimators of covariance. We illustrate the methods from the motivating example from a study in the area of Alzheimer's disease.
| S-EPMC3543761 | biostudies-literature
Project description:Air temperature data retrieved from global atmospheric models may show a systematic bias with respect to measurements from weather stations. This is a common concern in local climate studies. The current study presents two methods based upon copulas and Conditional Probability (CP) to predict bias-corrected mean air temperature in a data-scarce environment: CP-I offers a single conditional probability as a predictor, CP-II is a pixel-wise version of CP-I and offers spatially varying predictors. The methods were applied on daily air temperature in the Qazvin Plain, Iran that were collected at 24 weather stations and 150 ECMWF ERA-interim grid cells from a single month (June) over 11 years. We compared the methods with the commonly applied conditional expectation and conditional median methods. Leave-k-out cross-validation and correlation scores show that the new methods reduced the bias with 44-68% for the full data set and with 34-74% on a homogeneous subarea. We conclude that the two methods are able to locally improve ECMWF air temperatures in a data-scarce area.
| S-EPMC6505955 | biostudies-literature
Project description:Monocytes/macrophages respond to external stimuli with rapid changes in the expression of numerous inflammation-related genes to undergo polarisation towards the M1 (pro-inflammatory) or M2 (anti-inflammatory) phenotype. We have previously shown that, independently of Toll-like receptor activation, extracellular RNA (eRNA) could exert pro-thrombotic and pro-inflammatory properties in the cardiovascular system to provoke cytokine mobilisation. Here, mouse bone marrow-derived-macrophages (BMDM) differentiated with mouse macrophage-colony-stimulating factor (M-CSF) were found to be skewed towards the M1 phenotype when exposed to eRNA. This resulted in up-regulated expression of inflammatory markers such as Tnf-? and Il-6, together with Il-12 and iNOS, whereas anti-inflammatory genes such as chitinase-like proteins (Ym1/2) and macrophage mannose receptor-2 (Cd206) were significantly down-regulated. Human peripheral blood monocytes were treated with eRNA and analysed by micro-array analysis of the whole human genome, revealing an up-regulation of 79 genes by at least four-fold; 27 of which are related to signal transduction and 15 genes associated with inflammatory response. In accordance with the proposed actions of eRNA as a pro-inflammatory "alarm signal", these data shed light on the role of eRNA in the context of chronic inflammatory diseases such as atherosclerosis.
| S-EPMC4427572 | biostudies-literature
Project description:Alcohol solvents are significantly more polar than expected based on the measured H-bonding properties of monomeric alcohols in dilute solution. Self-association of alcohols leads to formation of cyclic aggregates and linear polymeric chains that have a different polarity from the alcohol monomer. Cyclic aggregates are less polar than the monomer, and the chain ends of linear polymers are more polar. The solvation properties of alcohols therefore depend on the interplay of these self-association equilibria and the equilibria involving interactions with solutes. Twenty-one different molecular recognition probes of varying polarity were used to probe the solvation properties of alkane-alcohol mixtures across a wide range of different solvent compositions. The results allow dissection of the complex equilibria present in these systems. Formation of a H-bond between two alcohol molecules leads to polarisation of the hydroxyl groups, resulting in an increase in binding affinity for subsequent interactions with the unbound donor and acceptor sites. The H-bond donor parameter (?) for these sites increases from 2.7 to 3.5, and the H-bond acceptor parameter (?) increases from 5.3 to 6.9. Polarisation is a short range effect limited to the first H-bond in a chain, and formation of subsequent H-bonds in longer chains does not further increase the polarity of chain ends. H-bond donor sites involved in a H-bond are unavailable for further interactions, because the formation of a bifurcated three-centre H-bond is three orders of magnitude less favourable than formation of a conventional two-centre H-bond. These findings are reproduced by quantum chemical calculations of the molecular electrostatic potential surfaces of alcohol aggregates. Thus, the overall solvation properties of alcohols depend on the speciation of different aggregates, the polarities of these species and the polarities of the solutes. At low alcohol concentrations, polar solutes are solvated by alcohol monomers, and at higher alcohol concentrations, solutes are solvated by the more polar chain ends of linear polymers. The less polar cyclic aggregates are less important for interactions with solutes. Similar behavior was found for ten different alcohol solvents. Tertiary alcohols are marginally less polar solvents than primary alcohols, due to steric interactions that destabilises the formation of polymeric aggregates leading to lower concentrations of polar chain ends. One alcohol with an electron-withdrawing substituent was studied, and this solvent showed slightly different behavior, because the H-bond donor and acceptor properties are different.
| S-EPMC5875020 | biostudies-literature
Project description:This paper examines the fundamental problem of testimony. Much of what we believe to know we know in good part, or even entirely, through the testimony of others. The problem with testimony is that we often have very little on which to base estimates of the accuracy of our sources. Simulations with otherwise optimal agents examine the impact of this for the accuracy of our beliefs about the world. It is demonstrated both where social networks of information dissemination help and where they hinder. Most importantly, it is shown that both social networks and a common strategy for gauging the accuracy of our sources give rise to polarisation even for entirely accuracy motivated agents. Crucially these two factors interact, amplifying one another's negative consequences, and this side effect of communication in a social network increases with network size. This suggests a new causal mechanism by which social media may have fostered the increase in polarisation currently observed in many parts of the world.
| S-EPMC10763946 | biostudies-literature