Abstract
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. We adopted the interaction kernel approach to model enhancement of people's information-gathering ability and introduced a generalized nonlocal gradient as individuals' perception kernel. We found that the characteristic distance between population peaks becomes greater as the wider range of opinions becomes available to individuals or the more attention is attracted to opinions distant from theirs. These findings may provide a possible explanation for why disagreement is growing in today's increasingly interconnected society, without attributing its cause only to specific individuals or events.
Highlights
Today’s society faces many urgent critical challenges
The present study explores this view through mathematical modeling and analysis of opinion dynamics
We begin the analysis by replacing the spatiotemporal function P(x, t ) with a constant homogeneous population level Ph plus a sinusoidal spatial perturbation with temporally changing small amplitude P(t ) [18], as follows: P(x, t ) → Ph + P(t ) sin(ωx + φ). This replacement allows for linearization of Eq (1) into the following nonspatial linear dynamical equation of P: d P= dt
Summary
Today’s society faces many urgent critical challenges One such challenge is addressing the widening disagreement and conflicts among different social constituent groups with incompatible views on politics, economies, international relationships, religions, cultures, lifestyle, and other aspects of our life. Studies on this challenge often focus on how escalated views and opinions emerge in society [1]. Typical approaches in this area include detection of extremism in online media [2,3,4] and modeling contagious processes of extremism through social networks [5,6].
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