7,088 research outputs found
Partitioned Sampling of Public Opinions Based on Their Social Dynamics
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?
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
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
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
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
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
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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