Surges of collective human activity emerge from simple pairwise correlations

TL;DR

This paper demonstrates that simple pairwise correlations among individuals can accurately predict large-scale human activity surges across various contexts, suggesting a unifying principle rooted in statistical mechanics.

Contribution

It introduces a maximum entropy-based Ising model to connect pairwise interactions with collective human behaviors, revealing underlying network structures.

Findings

Pairwise correlations predict large-scale activity surges.

The Ising model accurately captures population behavior.

Interaction topology mirrors communication networks.

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.