7,088 research outputs found

    Partitioned Sampling of Public Opinions Based on Their Social Dynamics

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    Public opinion polling is usually done by random sampling from the entire population, treating individual opinions as independent. In the real world, individuals' opinions are often correlated, e.g., among friends in a social network. In this paper, we explore the idea of partitioned sampling, which partitions individuals with high opinion similarities into groups and then samples every group separately to obtain an accurate estimate of the population opinion. We rigorously formulate the above idea as an optimization problem. We then show that the simple partitions which contain only one sample in each group are always better, and reduce finding the optimal simple partition to a well-studied Min-r-Partition problem. We adapt an approximation algorithm and a heuristic algorithm to solve the optimization problem. Moreover, to obtain opinion similarity efficiently, we adapt a well-known opinion evolution model to characterize social interactions, and provide an exact computation of opinion similarities based on the model. We use both synthetic and real-world datasets to demonstrate that the partitioned sampling method results in significant improvement in sampling quality and it is robust when some opinion similarities are inaccurate or even missing

    Individual Attitudes toward the Impact of Multinational Corporations on Domestic Businesses: How Important are Individual Characteristics and Country-Level Traits?

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    We study the importance of individual characteristics and national factors influencing individual attitudes towards the impact of multinational corporations on local businesses. Our sample includes more than 40 000 respondents in 29 countries from the 2003 National Identity Survey conducted by the International Social Survey Programme. We find that individual demographic factors and socioeconomic status, such as gender, age, income and education, are strong predictors of their attitudes. For example, income and education are positively associated with favourable attitudes towards the impact of multinational corporations (MNCs) on local businesses while age is negatively associated with individual attitudes towards MNCs. In addition, hierarchical ordered logit model results show that approximately 8% of total variations in individual attitudes around our sample mean are not explained by differences in personal traits. Instead, they are due to country-level heterogeneity such as, but not limited to, different degrees of openness or different aggregate income

    Markovian Dynamics on Complex Reaction Networks

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    Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm

    Real Wages and the Business Cycle: Accounting for Worker and Firm Heterogeneity

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    Using a longitudinal matched employer-employee data set for Portugal over the 1986-2005 period, this study analyzes the heterogeneity in wages responses to aggregate labor market conditions for newly hired workers and existing workers. Accounting for both worker and firm heterogeneity, the data support the hypothesis that entry wages are much more procyclical than current wages. A one-point increase in the unemployment rate decreases wages of newly hired male workers by around 2.8% and by just 1.4% for workers in continuing jobs. Since we estimate the fixed effects, we were able to show that unobserved heterogeneity plays a non-trivial role in the cyclicality of wages. In particular, worker fixed effects of new hires and separating workers behave countercyclically, whereas firm fixed effects exhibit a procyclical pattern. Finally, the results reveal, for all workers, a wage-productivity elasticity of 1.2, slightly above the one-for-one response predicted by the Mortensen-Pissarides model.wage cyclicality, hires, firm-specific effects, compositional effects, labor productivity

    Multilingual Twitter Sentiment Classification: The Role of Human Annotators

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    What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered

    Disentangling Achievement Orientation and Goal Setting: Effects on Self-Regulatory Processes

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    Creativity has been underscored as a key factor to organizational adaptability and competitiveness in today\u27s rapidly changing business environment. Designing as well as managing work environments that facilitate creativity have therefore received growing attention, resulting in a multitude of research examining the social-psychological work environment. Few studies, however, have focused on the contribution of the physical work environment to supporting creativity in the workplace. This study focuses on the role of the physical environment in supporting creativity in organizations by identifying specific physical features and attributes of the work environment perceived to promote or inhibit creativity. The research design compares four organizations publicly acclaimed for their innovative social-psychological work environments, but which are distinctly different in terms of the physical work environment. Quantitative and qualitative data were collected by means of survey questionnaires [N = 1 30). Results indicate that the physical work environment exerts indirect influence on creativity by contributing to two significant social-psychological conditions that are conducive to creativity, namely dynamism and freedom. The study specifies attributes of the physical work environment perceived to be positively and negatively associated with both of these conditions

    Opinion influence and evolution in social networks: a Markovian agents model

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    In this paper, the effect on collective opinions of filtering algorithms managed by social network platforms is modeled and investigated. A stochastic multi-agent model for opinion dynamics is proposed, that accounts for a centralized tuning of the strength of interaction between individuals. The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected by the opinions of the neighbors through influence parameters. The properties of this model are studied in a general setting as well as in interesting special cases. A general result is that the overall model of the social network behaves like a high-dimensional Markov chain, which is viable to Monte Carlo simulation. Under the assumption of identical agents and unbiased influence, it is shown that the influence intensity affects the variance, but not the expectation, of the number of individuals sharing a certain opinion. Moreover, a detailed analysis is carried out for the so-called Peer Assembly, which describes the evolution of binary opinions in a completely connected graph of identical agents. It is shown that the Peer Assembly can be lumped into a birth-death chain that can be given a complete analytical characterization. Both analytical results and simulation experiments are used to highlight the emergence of particular collective behaviours, e.g. consensus and herding, depending on the centralized tuning of the influence parameters.Comment: Revised version (May 2018
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