Simulation of Stance Perturbations

TL;DR

This paper uses agent-based modeling to analyze how social influence operations can succeed through stance perturbations, identifying key factors like influential agents and optimal strategies that lead to network consensus shifts.

Contribution

It introduces a co-evolutionary social influence model and demonstrates the effectiveness of cascade-based perturbation strategies in achieving consensus change.

Findings

Influential agents are most effective as Confederates.

Optimal perturbation involves cascading local ego networks.

Approximately 20-25% Confederates can shift network consensus.

Abstract

In this work, we analyze the circumstances under which social influence operations are likely to succeed. These circumstances include the selection of Confederate agents to execute intentional perturbations and the selection of Perturbation strategies. We use Agent-Based Modelling (ABM) as a simulation technique to observe the effect of intentional stance perturbations on scale-free networks. We develop a co-evolutionary social influence model to interrogate the tradeoff between perturbing stance and maintaining influence when these variables are linked through homophily. In our experiments, we observe that stances in a network will converge in sufficient simulation timesteps, influential agents are the best Confederates and the optimal Perturbation strategy involves the cascade of local ego networks. Finally, our experimental results support the theory of tipping points and are in line with empirical findings suggesting that 20-25% of agents need to be Confederates before a change in consensus can be achieved.