Abstract
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. Here, we introduce a model of public opinion expression. Two groups of agents with different opinion on an issue interact with each other, changing the willingness to express their opinion according to whether they perceive themselves as part of the majority or minority opinion. We formulate the model as a multigroup majority game and investigate the Nash equilibria. We also provide a dynamical systems perspective: Using the reinforcement learning algorithm of Q-learning, we reduce the N-agent system in a mean-field approach to two dimensions which represent the two opinion groups. This two-dimensional system is analyzed in a comprehensive bifurcation analysis of its parameters. The model identifies social-structural conditions for public opinion predominance of different groups. Among other findings, we show under which circumstances a minority can dominate public discourse.
Highlights
Fundamental to models of opinion dynamics is the assumption that people’s opinions are, in some way or another, influenced by the opinion of their peers
There is an extensive amount of models of opinion change in social systems
It might affect one’s willingness of opinion expression: The more positive the feedback, the more motivated one feels to publicly express one’s opinion. This approach to public discourse has remained, from a modeling perspective, rather unexplored. It is worth considering the following: In general, people are not always willing to reveal their opinion on certain issues to others [5]
Summary
Fundamental to models of opinion dynamics is the assumption that people’s opinions are, in some way or another, influenced by the opinion of their peers. It might affect one’s willingness of opinion expression: The more positive (negative) the feedback, the more (less) motivated one feels to publicly express one’s opinion In comparison, this approach to public discourse has remained, from a modeling perspective, rather unexplored. The behavioral adjustment of agents depends solely on the social feedback they receive when they express their opinion This affective experience-based interaction mechanism has already been shown to lead to opinion polarization in connected networks of sufficiently high modularity [28]. To address questions of bounded rationality and equilibrium selection, we develop a dynamical systems perspective, using reinforcement learning in the form of Q-learning [29] This allows us to perform a mean-field approximation for the expected reward of the two opinion groups, which reduces the system to two dimensions.
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