MELBOURNE – In 1809, Jeremy Bentham, the founder of utilitarianism, set to work on The Book of Fallacies. His goal was to expose the fallacious arguments used to block reforms like the abolition of “rotten boroughs” – electorates with so few electors that a powerful lord or landowner could effectively select the member of parliament, while newer cities like Manchester remained unrepresented.
Bentham collected examples of fallacies, often from parliamentary debates. By 1811, he had sorted them into nearly 50 different types, with titles like “Attack us, you attack Government,” the “No precedent argument,” and the “Good in theory, bad in practice” fallacy. (One thing on which both Immanuel Kant and Bentham agree is that this last example is a fallacy: If something is bad in practice, there must be a flaw in the theory.)
Bentham was thus a pioneer of an area of science that has made considerable progress in recent years. He would have relished the work of psychologists showing that we have a confirmation bias (we favor and remember information that supports, rather than contradicts, our beliefs); that we systematically overestimate the accuracy of our beliefs (the overconfidence effect); and that we have a propensity to respond to the plight of a single identifiable individual rather than a large number of people about whom we have only statistical information.
Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform the first systematic methodological study of controversy detection using social-media network structure and content. Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain. Our approach to quantifying controversy is a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, which represents alignment of opinion among users; (ii) partitioning the conversation graph to identify potential sides of controversy; and (iii) measuring the amount of controversy from characteristics of the graph. We perform an extensive comparison of controversy measures, as well as graph building approaches and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.
Quantifying Controversy in Social Media Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis
Is it possible to predict how individuals will perform before the teamwork begins? Research by former cyclist Hugh Trenchard and others suggests that the mathematics of pelotons– the groups and bunches that cyclists form during a race – could be key to understanding how cyclists behave as a collective entity. While these collective dynamics may not tell us who will win the Tour de France, they do have broader applications to a variety of other biological systems. Here, Trenchard tells us more about his research, and how it might even provide some clues to the origin of life.
In Tuesday’s post, I argued that it was quite possible for political scientists to be both rigorous and relevant. But I closed by observing that economists generally don’t worry about the whole rigor vs. relevance debate. Their scholarly papers are impermeable black masses to lay readers, and yet policymakers and politicians defer to their expertise […]
Is porn bad for the brain? The Savvy Psychologist explains 3 studies that looked at how we process porn and other sexualized images, and reveals the potential effects on the brain—and on how we see our fellow men and women
We develop an analytical core for sociology. We follow standard dynamical systems theory by first specifying the conditions for social equilibrium, and then studying the dynamical principles that govern disequilibrium behavior. Our general social equilibrium model is an expansion of the general equilibrium model of economic theory, and our dynamical principles treat the society as a complex adaptive system that can be studied using evolutionary game theory and agent-based Markov models based on variants of the replicator dynamic.
Homo Socialis: An Analytical Core for Sociological Theory Gintis, Herbert and Helbing, Dirk
Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth’s surface; however, in modern contagions long-range edges—for example, due to airline transportation or communication media—allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct ‘contagion maps’ that use multiple contagions on a network to map the nodes as a point cloud. By analysing the topology, geometry and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modelling, forecast and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.
Axelrod's model for the dissemination of culture contains two key factors required to model the process of diffusion of innovations, namely, social influence (i.e., individuals become more similar when they interact) and homophily (i.e., individuals interact preferentially with similar others). The strength of these social influences are controlled by two parameters: F, the number of features that characterizes the cultures and q, the common number of states each feature can assume. Here we assume that the innovation is a new state of a cultural feature of a single individual -- the innovator -- and study how the innovation spreads through the networks among the individuals. For infinite regular lattices in one and two dimensions, we find that initially the innovation spreads linearly with the time t and diffusively in the long time limit, provided its introduction in the community is successful. For finite lattices, the growth curves for the number of adopters are typically concave functions of t. For random graphs with a finite number of nodes N, we argue that the classical S-shaped growth curves result from a trade-off between the average connectivity K of the graph and the per feature diversity q. A large q is needed to reduce the pace of the initial spreading of the innovation and thus delimit the early-adopters stage, whereas a large K is necessary to ensure the onset of the take-off stage at which the number of adopters grows superlinearly with t. In an infinite random graph we find that the number of adopters of a successful innovation scales with tγ with γ=1 for K>2 and 1/2<γ<1 for K=2. We suggest that the exponent γ may be a useful index to characterize the process of diffusion of successful innovations in diverse scenarios.
The incoming applicant does not necessarily need to know their bricks from their baseplates. Instead, they will head a new research center that focuses on children’s relationships with play in education, development and learning. They will also investigate how unrestrictive play can help improve a child’s experience of education.
This unusually titled position was created by the university after receiving $6.2 million (£4 million) in donations from the Lego Foundation, which aims "to make children's lives better – and communities stronger – by making sure the fundamental value of play is understood, embraced and acted upon." The Lego Foundation owns 25% of the Danish toy company Lego.
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