Download - Information Cascades in Human Networks
Information Cascades• Generalization of Infection Modeling
• Infection based on % threshold of neighbors
• Agent-Based model to examine heterogeneous networks
• Discrete timesteps
Variable Activation Thresholds
• “A Simple Model of Global Cascades on Random Networks” ~ Duncan J. Watts, PNAS 2002
Variable Activity Times• “Diffusion in Networks and the Virtue of Burstiness”, M.
Akbarpour, M. O. Jackson, PNAS 2018
• Poisson, Reversing, and Sticky Agents
Starting Goal• Combine heterogeneous activation thresholds and activity times
• Apply to scale-free networks
• Examine resilience to targeted vs random attacks
• How do you best spread or halt a cascade in human communities?
Model• Random or Scale-Free
networks
• One initial agent infected
• Contagious for 10 turns
• Spreads to all possible neighbors each turn
• No “recovery”
• Simulation ends when no agents contagious
First Study Conclusions• Scale Free Networks generally safer
• Hubs act as gatekeepers, quarantine cascades
• If a hub is susceptible, can easily spread cascade
• Activity synchronization threatens communities
Second Study Conclusions
• Targeted attacks most effective in scale-free networks with mid-level susceptibility to cascades
• At high and low susceptibility, minimal difference from random attack unless very centralized