When Networks Mislead Democracy: How Partisan Communication Undermines Collective Decision-Making

Democratic societies increasingly rely on communication networks to aggregate citizen preferences and information, yet these same networks can systematically mislead voters under certain conditions. While democratic theory envisions networks as conduits for collective wisdom, the conditions determining when networks enhance versus undermine collective decision-making remain poorly understood. 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. Our systematic computational analysis reveals 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. This demonstrates that diverse groups can outperform homogeneous high-ability groups, suggesting democratic systems benefit more from cognitive diversity than from concentrations of highly engaged partisans. 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 by revealing a fundamental tension between beneficial noise filtering and harmful bias amplification. We offer concrete design principles for preserving democratic competence in networked societies, particularly in competitive two-party systems where digital platforms increasingly mediate political communication.