Agent Scheduling in Opinion Dynamics: A Taxonomy and Comparison Using Generalized Models

Abstract

Opinion dynamics models are an important field of study within the agent-based modeling community. Agent scheduling elements within existing opinion dynamics models vary but are largely unjustified and only minimally explained. Furthermore, previous research on the impact of scheduling is scarce, partially due to a lack of a common taxonomy with which to discuss and compare schedules. The Synchrony, Actor type, Scale (SAS) taxonomy is presented, which aims to provide a common lexicon for agent scheduling in opinion dynamics models. This is demonstrated using a generalized repeated averaging model (GRAM) and a generalized bounded confidence model (GBCM). Significant di erences in model outcomes with varied schedules are given, along with the results of intentional model biasing using only schedule variation. We call on opinion dynamics modelers to make explicit their choice of schedule and to justify that choice based on realistic social phenomena.