Emergent Opinion Dynamics on Endogenous Networks

TL;DR

This paper investigates how social networks evolve endogenously based on agent behaviors and how these dynamics influence opinion formation, using computational simulations to explore feedback effects on network topology.

Contribution

It introduces a model where agent behaviors influence and are influenced by the evolving network structure, extending previous static network analyses in opinion dynamics.

Findings

Network topology significantly affects emergent opinion patterns.

Behavioral feedback leads to dynamic changes in network structure.

Simulation results reveal complex feedback loops between opinions and network evolution.

Abstract

In recent years networks have gained unprecedented attention in studying a broad range of topics, among them in complex systems research. In particular, multi-agent systems have seen an increased recognition of the importance of the interaction topology. It is now widely recognized that emergent phenomena can be highly sensitive to the structure of the interaction network connecting the system's components, and there is a growing body of abstract network classes, whose contributions to emergent dynamics are well-understood. However, much less understanding have yet been gained about the effects of network dynamics, especially in cases when the emergent phenomena feeds back to and changes the underlying network topology. Our work starts with the application of the network approach to discrete choice analysis, a standard method in econometric estimation, where the classic approach is grounded in individual choice and lacks social network influences. In this paper, we extend our earlier results by considering the endogenous dynamics of social networks. In particular, we study a model where the behavior adopted by the agents feeds back to the underlying network structure, and report results obtained by computational multi-agent based simulations