Opinion Diffusion Software with Strategic Opinion Revelation and Unfriending

TL;DR

This paper introduces a flexible software suite for modeling dynamic social networks and opinion diffusion, emphasizing agent diversity, strategic unfriending, and multi-agent reinforcement learning to better understand polarization and consensus.

Contribution

The software uniquely integrates dynamic topologies, multidimensional diffusion, diverse agent types, and strategic unfriending, advancing social network modeling capabilities.

Findings

Polarization and consensus depend on individual opinion goals.

Topological clustering influences opinion dynamics.

Diverse agent behaviors impact network evolution.

Abstract

We present a novel software suite for social network modeling and opinion diffusion processes. Much research on social network science has assumed networks with static topologies. More recently, attention has been turned to networks that evolve. Although software for modeling both the topological evolution of networks and diffusion processes are constantly improving, very little attention has been paid to agent modeling. Our software is designed to be robust, modular, and extensible, providing the ability to model dynamic social network topologies and multidimensional diffusion processes, different styles of agent including non-homophilic paradigms, as well as a testing environment for multi-agent reinforcement learning (MARL) experiments with diverse sets of agent types. We also illustrate the value of diverse agent modeling, and environments that allow for strategic unfriending. Our work shows that polarization and consensus dynamics, as well as topological clustering effects, may rely more than previously known on individuals' goals for the composition of their neighborhood's opinions.