Title: Surges of collective human activity emerge from simple pairwise correlations,
Abstract: Human populations exhibit complex behaviors---characterized by long-range
correlations and surges in activity---across a range of social, political, and
technological contexts. Yet it remains unclear where these collective behaviors
come from, or if there even exists a set of unifying principles. Indeed,
existing explanations typically rely on context-specific mechanisms, such as
traffic jams driven by work schedules or spikes in online traffic induced by
significant events. However, analogies with statistical mechanics suggest a
more general mechanism: that collective patterns can emerge organically from
fine-scale interactions within a population. Here, across four different modes
of human activity, we show that the simplest correlations in a
population---those between pairs of individuals---can yield accurate
quantitative predictions for the large-scale behavior of the entire population.
To quantify the minimal consequences of pairwise correlations, we employ the
principle of maximum entropy, making our description equivalent to an Ising
model whose interactions and external fields are notably calculated from past
observations of population activity. In addition to providing accurate
quantitative predictions, we show that the topology of learned Ising
interactions resembles the network of inter-human communication within a
population. Together, these results demonstrate that fine-scale correlations
can be used to predict large-scale social behaviors, a perspective that has
critical implications for modeling and resource allocation in human
populations.