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Simulation of Stance Perturbations

Peter Carragher, Lynnette Hui Xian Ng, Kathleen M. Carley

TL;DR

This paper investigates when social influence operations can successfully perturb stance distributions in scale-free networks using a co-evolutionary ABM. It couples Friedkin-like stance updates with a dynamic influence network that evolves via homophily, enabling analysis of Confederate selection and perturbation strategies, including a cascade approach targeting ego-networks. Key findings show that influential Confederates are most effective, the cascade perturbation yields the strongest stance shifts, and tipping points occur around 20–25% Confederate participation, aligning with empirical tipping-point literature. The work advances understanding of influence dynamics and consensus formation, while noting ethical considerations and the need for real-world data to validate the model's applicability.

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.

Simulation of Stance Perturbations

TL;DR

This paper investigates when social influence operations can successfully perturb stance distributions in scale-free networks using a co-evolutionary ABM. It couples Friedkin-like stance updates with a dynamic influence network that evolves via homophily, enabling analysis of Confederate selection and perturbation strategies, including a cascade approach targeting ego-networks. Key findings show that influential Confederates are most effective, the cascade perturbation yields the strongest stance shifts, and tipping points occur around 20–25% Confederate participation, aligning with empirical tipping-point literature. The work advances understanding of influence dynamics and consensus formation, while noting ethical considerations and the need for real-world data to validate the model's applicability.

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.
Paper Structure (19 sections, 7 equations, 5 figures, 3 tables)

This paper contains 19 sections, 7 equations, 5 figures, 3 tables.

Table of Contents

  1. Background
  2. Simulation Objectives
  3. Research Questions
  4. Contributions
  5. Model Definition
  6. Influence-Stance Tradeoff
  7. Confederate Selection
  8. Perturbation Strategy
  9. Methodology
  10. Virtual Experiments Setup
  11. Results and Discussion
  12. Convergence to a state of stable polarization
  13. Cascade is optimal
  14. Influential Confederates are best
  15. Minority stance tipping points exist
  16. ...and 4 more sections

Figures (5)

  • Figure 1: In an 80-node network, a single Confederate struggles to perturb consensus while maintaining influence using the conversion perturbation strategy. The Confederate maintains a -1 stance until its influence begins to drop at timestep 90. It then raises its stance until its influence rebuilds. This repeats at timestep 130, the beginning of a distinctly cyclic pattern.
  • Figure 2: Simulation of the change in agent stances for an 80-node network. Each line represents an agent's stance over time. Stances eventually converge into one of two extremes, 1 and -1.
  • Figure 3: Comparison of the perturbation strategies, lower is better. The cascade strategy is optimal.
  • Figure 4: Comparison of the agent selection strategies, lower is better. Influential Confederates are optimal.
  • Figure 5: With $>$20-25% of network agent as Confederates, mean stance shifts rapidly. Results are averaged over five 80-node networks, using the maximum influence selection strategy.