Opinion

Today's society faces widening disagreement and conflicts among constituents with incompatible views. Escalated views and opinions are seen not only in radical ideology or extremism but also in many other scenes of our everyday life. Here we show that widening disagreement among groups may be linked to the advancement of information communication technology by analyzing a mathematical model of population dynamics in a continuous opinion space.

In recent years, formal models are gaining momentum in the field of psychology, aimed to address the theory crisis and provide a quantitative foundation for theoretical inferences. These formal models are often used to study psychological states, for example, a healthy state and a psychopathological state. The relative stability of these psychological states is an important avenue for research to better understand individual differences and (clinical) change processes. The potential landscape is often used as a metaphor to conceptually illustrate stability.

Since scientific research is mostly driven by public funds and its translation impacts societies, how the lay public sees science and scientists have been subjects of pertinent interest. However an interesting paradox can be observed: the rise in science communication and outreach efforts seems to correlate with the rise of openly anti-science groups, leading to fears of a “post- truth” society.

Modeling efforts in opinion dynamics have to a large extent ignored that opinion exchange between individuals can also have an effect on how willing they are to express their opinion publicly. Making one’s opinion audible is important: Positions that are rarely voiced in public are improbable to be taken into consideration in, e.g., political decisions. We investigate the conditions that promote or impede opinion expression of different opinion groups in a game-theoretically grounded model with two opinion groups on a stochastic block network[1].

Despite the ever growing availability of traces produced by digital media, so far no large-scale empirical research has been devoted to online attention cycles, with few remarkable exceptions. To encourage such research, we propose a toy model inspired by one of the most influential accounts of attention dynamics: the “public arenas model” introduced in 1988 by Stephen Hilgartner and Charles Bosk (H&B) [4]. Despite its clarity and insightfulness, H&B’s framework has never been mathematically formalized because of its complexity and lack of formal description.

Automated recommendations are nowadays a central part of our online user experience. Today, many online services – including e-commerce sites, media streaming platforms, and social networks – use recommendations to achieve organizational goals, such as increasing sales or customer retention. However, the existing literature on the design of recommendation algorithms mostly does not consider this business-oriented perspective but focuses almost exclusively on consumers’ value.