When Networks Mislead: How Partisan Communication Undermines Democratic Decision-Making

Abstract: Democratic societies increasingly rely on communication networks to aggregate citizen preferences and information, yet these same networks can systematically mislead voters under certain conditions. We introduce an agent-based model that captures two rival forces in partisan networks: honest noise filtering that lifts accuracy and strategic bluffing that embeds bias. Extensive simulations show that communication architecture shapes voting accuracy more than any individual-level trait. When candidate quality gaps are moderate, partisan bluffing overpowers honest signals and steers supporters of weaker contenders into collective error. However, positioning independents in central network roles serves as an epistemic circuit breaker, preventing echo chambers from spiraling toward systematic error. Counterintuitively, we discover that competitive elections with meaningful quality differences prove most vulnerable to collective delusion. Small numbers of extreme partisans can contaminate entire communities through cascading bias effects that persist across hundreds of communication rounds. Our findings challenge conventional wisdom about information aggregation in democracy and provide actionable insights for institutional design in an era of algorithmic filtering and social media polarization.