Information aggregation

Information aggregation is the process by which distributed, partial, or noisy signals held by multiple agents or sensors are combined to produce collective judgments that exceed what any individual source could generate alone. The concept appears in economics (market price formation, voting theory), systems theory (sensor fusion, consensus protocols), and epistemology (reliabilism at institutional scale). Its fundamental challenge is the aggregation problem: procedures that aggregate individual signals correctly under one model of signal generation fail when the underlying model is misspecified. Arrow's impossibility theorem demonstrates this for preference aggregation; the Condorcet jury theorem demonstrates it for belief aggregation under independence assumptions. Whether any aggregation mechanism is unconditionally reliable is an open question in both social choice theory and collective intelligence research.