The false consensus effect is the observation that people tend to overestimate the number of people who share their views. In modern environments we also see growing evidence of greater polarization. For example, according to the Pew Research Center over the past five decades, congressional US Democrat and Republican ideologies have increasingly diverged, with an ever shrinking middle ground. This is appears to also be reflected among US citizens, with a "disappearing center" hastened by growing “anarchist” and “anti-establishment” ideologies. Many have speculated that this polarization is a global phenomenon. The question we pose here is how beliefs and network structure might interact to facilitate both false consensus effects and rising polarization.

People have the ability to estimate frequencies of different behaviors, beliefs, and intentions of others, allowing them to fit into their immediate social worlds, learn from, and cooperate with others. However, psychology has produced a long list of apparent biases in social cognition. We show that this apparent contradiction can be resolved by understanding how cognitive processes underlying social judgments interact with the properties of social and task environments. We describe our social sampling model that incorporates this interaction and can explain biases in people’s estimates of broader populations. We also show that asking people about their social circles produces better predictions of elections than asking about their own voting intentions, provides good description of population attributes, and helps predict people’s future voting and vaccination behavior.