Abstract
The study of opinions $-$ e.g., their formation and change, and their effects on our society $-$ by means of theoretical and numerical models has been one of the main goals of sociophysics until now, but it is one of the defining topics addressed by social psychology and complexity science. Despite the flourishing of different models and theories, several key questions still remain unanswered. The aim of this paper is to provide a cognitively grounded computational model of opinions in which they are described as mental representations and defined in terms of distinctive mental features. We also define how these representations change dynamically through different processes, describing the interplay between mental and social dynamics of opinions. We present two versions of the model, one with discrete opinions (voter model-like), and one with continuous ones (Deffuant-like). By means of numerical simulations, we compare the behaviour of our cognitive model with the classical sociophysical models, and we identify interesting differences in the dynamics of consensus for each of the models considered.
Highlights
Opinions represent a large part of human mental representations, and a large part of our everyday social interactions consist in exchanging, evaluating, revising and comparing opinions with our family, friends, acquaintances, or even strangers
Other disciplines have been interested in the topic, like sociophysics and complexity science [3, 4]
We aim at developing a cognitively grounded model of opinion dynamics that will allow us to answer the following questions: can we identify the distinctive features of opinions, and model them as interacting representations that get influenced by others’ mental states? How can heterogeneous agents, endowed with different representations of the external world, come to share a given viewpoint and what consequences this sharing has on individuals’ beliefs and their related behaviors?
Summary
Opinions represent a large part of human mental representations, and a large part of our everyday social interactions consist in exchanging, evaluating, revising and comparing opinions with our family, friends, acquaintances, or even strangers. Understanding opinions, describing how they are generated and revised, and how far opinions travel across social space both as a consequence of social influence and as one of the main means through which social influence unfolds, is crucial for grasping a deeper understanding of human social cognition and behaviors. The study of social phenomena as opinion formation and dynamics has become of great interest in physics. Due to similarities between spreading and ordering phenomena, opinion dynamics has been studied from a mathematical and numerical point of view by means of the tools of statistical and computational physics [5, 6]. In the physics community, opinions have been so far considered dynamic elements that can be approximated as spin systems or by similar statistical-mechanical methods [7]. The possibilities offered by Big Data Science to collect and analyze huge amounts of digital traces humans leave on the web and other media, has made opinion change and consensus achievement exceeds disciplinary boundaries and become one of our century’s grand scientific challenges [8]
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